From b4af923686fc185ca4023746c647d22ef86d016d Mon Sep 17 00:00:00 2001 From: Wei Xiao <11197323+wxiao0421@users.noreply.github.com> Date: Thu, 23 Jul 2026 14:29:33 -0700 Subject: [PATCH 1/4] Add numpy-only simulation/tuning harness for comparing aggregators Add sentinel.simulation with score_groups, evaluate_groups, compare_aggregators, and run_grid_search. evaluate_groups reports three evaluation-metric families (ranking, threshold/classification, separation/distribution) so users can tune the summarize metric and hyperparameters for their use case, not just ranking. - New module + public API exports; docs regenerated (docs-sync passes) - tests/test_simulation.py covers all three metric families and score_groups plumbing - Demonstrate in sentinel_against_hate.ipynb (compare aggregators + grid search) - README: 'Simulation-based tuning' section - Cleanups: fix Example_Threshold_Script.py (cache_model kwarg + index path), rename test_sriracha_local_index.py -> test_sentinel_local_index.py Co-authored-by: Cursor --- README.md | 34 + docs/source/modules.rst | 1 + docs/source/sentinel.rst | 8 + examples/Example_Threshold_Script.py | 9 +- examples/sentinel_against_hate.ipynb | 4859 +++++++++-------- src/sentinel/__init__.py | 16 + src/sentinel/simulation.py | 572 ++ ..._index.py => test_sentinel_local_index.py} | 0 tests/test_simulation.py | 261 + 9 files changed, 3400 insertions(+), 2360 deletions(-) create mode 100644 src/sentinel/simulation.py rename tests/{test_sriracha_local_index.py => test_sentinel_local_index.py} (100%) create mode 100644 tests/test_simulation.py diff --git a/README.md b/README.md index 36c81e3..8cfc030 100644 --- a/README.md +++ b/README.md @@ -254,6 +254,40 @@ Notes: - All aggregators operate over per‑observation scores where non‑confident observations are already clipped to 0. - The default `skewness` remains a good choice when user activity volume varies widely. +## Simulation-based tuning + +Which aggregator and hyperparameters work best depends on your data, so it helps to measure them on labeled examples. The `sentinel.simulation` module is a small, numpy‑only harness for exactly that — no Ray, S3, or experiment trackers required. + +Give it groups of observations with known labels (`1` = rare/positive source, `0` = common/negative source), score them once, then compare every aggregator (and hyperparameter) cheaply: + +```python +import pandas as pd +from sentinel.simulation import LabeledGroup, score_groups, compare_aggregators, run_grid_search + +groups = [ + LabeledGroup(name="source_a", label=1, observations=["...", "..."]), # known rare-class source + LabeledGroup(name="source_b", label=0, observations=["...", "..."]), # known common-class source + # ... +] + +# Expensive step (runs the model) — done once. +scored = score_groups(index, groups, top_k=5) + +# Cheap step — compare all six aggregators on the same scores. +pd.DataFrame(compare_aggregators(scored)) + +# Or sweep hyperparameters as well. +pd.DataFrame(run_grid_search(index, groups, top_k_values=[3, 5, 10], min_score_values=[0.0, 0.1, 0.25])) +``` + +Each result row reports three families of evaluation metrics, so you can tune for whatever matters to your use case: + +- Ranking: `roc_auc`, `recall_at_n`, `precision_at_n`, `rank_ratio` (do known positives rank at the top?) +- Threshold / classification: `precision`, `recall`, `f1`, `false_positive_rate` at a chosen (or automatically best‑F1) cutoff +- Separation / distribution: `mean_separation`, `cohens_d`, `ks_statistic` (threshold‑free) + +See [examples/sentinel_against_hate.ipynb](examples/sentinel_against_hate.ipynb) for a worked example comparing aggregators on hate‑speech data. + ## How It Works Sentinel uses a two-step process to detect rare classes of text, focusing on high recall for realtime applications: diff --git a/docs/source/modules.rst b/docs/source/modules.rst index 0859667..82a26d7 100644 --- a/docs/source/modules.rst +++ b/docs/source/modules.rst @@ -5,3 +5,4 @@ API Reference :maxdepth: 4 sentinel + diff --git a/docs/source/sentinel.rst b/docs/source/sentinel.rst index b0f78d7..398fbf5 100644 --- a/docs/source/sentinel.rst +++ b/docs/source/sentinel.rst @@ -34,6 +34,14 @@ sentinel.sentinel_local_index :undoc-members: :show-inheritance: +sentinel.simulation +------------------- + +.. automodule:: sentinel.simulation + :members: + :undoc-members: + :show-inheritance: + Subpackages ---------- diff --git a/examples/Example_Threshold_Script.py b/examples/Example_Threshold_Script.py index 3f9d498..c085eb3 100644 --- a/examples/Example_Threshold_Script.py +++ b/examples/Example_Threshold_Script.py @@ -33,6 +33,11 @@ import time from typing import Dict, List +# Path to a saved Sentinel index. By default this points at the hate-speech +# index built by examples/sentinel_against_hate.ipynb. Change it to your own +# index path (local directory or s3:// URI) if needed. +DEFAULT_INDEX_PATH = "./hate_speech_model" + def create_user_profiles() -> Dict[str, List[str]]: """Create 10 different user profiles with varying speech patterns.""" @@ -163,9 +168,9 @@ def test_thresholds_and_ratios(review_mode: bool = False, # Time data loading load_start = time.time() index = SentinelLocalIndex.load( - path="path/to/local/index", + path=DEFAULT_INDEX_PATH, negative_to_positive_ratio=ratio, - Cache_Model=True & ~no_cache + cache_model=not no_cache, ) load_time = time.time() - load_start total_load_time += load_time diff --git a/examples/sentinel_against_hate.ipynb b/examples/sentinel_against_hate.ipynb index bdf9338..ad0abfd 100644 --- a/examples/sentinel_against_hate.ipynb +++ b/examples/sentinel_against_hate.ipynb @@ -1,2427 +1,2570 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "6bbeca2f", - "metadata": {}, - "source": [ - "# Hate Speech Detection with Sentinel: Building and Evaluating a Robust Detection Model\n", - "\n", - "This example demonstrates how to use the Sentinel library to build a robust hate speech detection model with minimal examples and evaluate it across different content sources. Sentinel excels at detecting extremely rare classes of harmful content using contrastive learning principles.\n", - "\n", - "## Data Sources and Methodology\n", - "\n", - "### Training Data:\n", - "- **Positive Examples (~1,500)**: Extracted from the Southern Poverty Law Center's (SPLC) Extremist Files database, specifically from \"In Their Own Words\" sections that contain direct quotes from individuals or groups identified as extremists. These quotes represent authentic examples of hate speech from various extremist ideologies.\n", - "- **Negative Examples (~15,000)**: Obtained from the Lex Fridman podcast dataset, which contains neutral, intellectual discussions. The 10:1 ratio of negative-to-positive examples reflects the typical imbalance in real-world content.\n", - "\n", - "### Evaluation Data:\n", - "- **Test Positives**: Podcast transcripts, known for controversial and potentially harmful content.\n", - "- **Test Negatives**: Different episodes from Lex Fridman's podcast, providing a contrast to potentially harmful content.\n", - "\n", - "### Workflow:\n", - "1. **Data Preparation**: Extract extremist quotes from SPLC and neutral content from conversational datasets. Segment longer texts into manageable chunks of 512 tokens with 128-token stride.\n", - "2. **Model Building**: Create embeddings of both positive and negative examples using the MiniLM-L6-v2 model, then build a Sentinel index to measure semantic similarity to the hate speech class.\n", - "3. **Evaluation**: Score content at both segment and episode levels using contrastive learning and statistical measures like skewness to identify patterns across observations.\n", - "4. **Analysis**: Compare the distribution of hate speech affinity scores between a controversial podcast and Lex Fridman content to validate the model's effectiveness.\n", - "\n", - "This approach demonstrates how Sentinel can detect rare patterns of concerning content with limited training examples, focusing on the semantic similarity to known extremist language rather than requiring extensive labeled datasets." - ] - }, - { - "cell_type": "markdown", - "id": "caac3997", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Before running this notebook, ensure you have installed the example dependencies and registered the Poetry environment as a Jupyter kernel. See the project README for details.\n", - "\n", - "### Downloading the data\n", - "\n", - "There is a script for downloading the Extremist Files from the Southern Poverty Law Center (SPLC) website, the neutral dataset, and the controversial podcast examples" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "d3aa300e", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import glob\n", - "import pandas as pd\n", - "from sentinel import SentinelLocalIndex\n", - "import re\n", - "from pathlib import Path\n", - "from tqdm.notebook import tqdm\n", - "import random\n", - "import pandas as pd\n", - "from tqdm.notebook import tqdm\n", - "import os\n", - "import requests\n", - "from datasets.utils.file_utils import get_datasets_user_agent" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "64a69da0", - "metadata": {}, - "outputs": [], - "source": [ - "from transformers import AutoTokenizer\n", - "\n", - "# Use MiniLM tokenizer\n", - "tokenizer = AutoTokenizer.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n", - "\n", - "def segment_text(text, max_tokens=512, stride=128):\n", - " tokens = tokenizer.encode(text, add_special_tokens=False)\n", - " segments = []\n", - " start = 0\n", - " while start < len(tokens):\n", - " end = start + max_tokens\n", - " segment_tokens = tokens[start:end]\n", - " segment_text = tokenizer.decode(segment_tokens)\n", - " segments.append(segment_text)\n", - " if end >= len(tokens):\n", - " break\n", - " start += stride\n", - " return segments" - ] - }, - { - "cell_type": "markdown", - "id": "c2965db6-192f-45dd-8d9a-88e5b92fa88d", - "metadata": {}, - "source": [ - "## Download and parse the positive example\n", - "Let's build the hate speech dataset using the example from Southern Poverty Law Center's (SPLC)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f0401ec2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 249 markdown files.\n" - ] - }, + "cells": [ { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6ff47bdd94334db8970505b6b189dc05", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing markdown files: 0%| | 0/249 [00:00 1)):\n", - " # Process the current section\n", - " paragraphs = process_section(current_section_lines)\n", - " all_paragraphs.extend({'filename': filename, 'paragraph': p} for p in paragraphs)\n", - " current_section_lines = []\n", - " in_own_words_section = False\n", - " continue\n", - " \n", - " # If we're in a section, collect the line\n", - " if in_own_words_section:\n", - " current_section_lines.append(line)\n", - " \n", - " # Don't forget to process the last section if we're still in one at the end of the file\n", - " if current_section_lines:\n", - " if not in_own_words_section:\n", - " print(f\"Warning: Reached end of file in {md_file} without closing 'In Their Own Words' section.\")\n", - " print(f\"Collected lines: {current_section_lines}\")\n", - " paragraphs = process_section(current_section_lines)\n", - " all_paragraphs.extend({'filename': filename, 'paragraph': p} for p in paragraphs)\n", - " \n", - " # if sections_found > 0:\n", - " # print(f\"Processing {md_file}: found {sections_found} 'In Their Own Words' sections\")\n", - " \n", - " return all_paragraphs\n", - " except Exception as e:\n", - " print(f\"Error processing {md_file}: {e}\")\n", - " return []\n", - "\n", - "# Helper function to process collected section lines into paragraphs\n", - "def process_section(lines):\n", - " if not lines:\n", - " return []\n", - " \n", - " # Skip if the first line starts with \"Background\" (table of contents)\n", - " if lines and lines[0].strip().startswith(\"Background\"):\n", - " return []\n", - " \n", - " quotes = []\n", - " \n", - " for line in lines:\n", - " line = line.strip()\n", - " if not line:\n", - " continue\n", - " \n", - " # Citation patterns - typically start with a dash, quote mark, or include a year/reference\n", - " is_citation = False\n", - " \n", - " # Patterns that strongly indicate a citation rather than a quote\n", - " if (\n", - " re.match(r'^(—|â|\\xa0?—|\\xa0?–|\"|â\\x80\\x94)$', line) or\n", - " # Lines ending with a year\n", - " re.search(r' \\d{4}\\.?\\s*$', line)\n", - " ) and len(line) < 100:\n", - " # If it looks like a citation, skip it\n", - " is_citation = True\n", - " \n", - " # If not a citation, add as a quote\n", - " if not is_citation:\n", - " quotes.append(line)\n", - " \n", - " # Filter out very short quotes (likely fragments)\n", - " quotes = [q for q in quotes if len(q) > 10]\n", - " \n", - " return quotes\n", - "\n", - "# Find all markdown files in the SPLC Extremist Files scraped dataset directory\n", - "splc_data_dir = \"data/splc_extremist_files\"\n", - "md_files = []\n", - "if os.path.exists(splc_data_dir):\n", - " md_files = glob.glob(os.path.join(splc_data_dir, \"**\", \"*.md\"), recursive=True)\n", - " print(f\"Found {len(md_files)} markdown files.\")\n", - "else:\n", - " print(\"Dataset directory not found. Please run the download cell first.\")\n", - "\n", - "# If we find markdown files, extract \"In Their Own Words\" sections\n", - "if md_files:\n", - " # Process all markdown files and collect paragraphs\n", - " all_paragraphs = []\n", - " for md_file in tqdm(md_files, desc=\"Processing markdown files\"):\n", - " paragraphs = extract_in_their_own_words(md_file)\n", - " all_paragraphs.extend(paragraphs)\n", - " \n", - " # Create a DataFrame from the extracted paragraphs\n", - " own_words_df = pd.DataFrame(all_paragraphs)\n", - " \n", - " if not own_words_df.empty:\n", - " print(f\"Extracted {len(own_words_df)} paragraphs from 'In Their Own Words' sections.\")\n", - " \n", - " else:\n", - " print(\"No 'In Their Own Words' sections found in the markdown files.\")\n", - "else:\n", - " print(\"No markdown files found.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "4b2ac613", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removing 5 files with too many quotes: ['tucker-carlson', 'center-immigration-studies', 'matt-walsh', 'mike-cernovich', 'paul-nehlen']\n" - ] - } - ], - "source": [ - "# Remove quotes from files that produced too many quotes, since these are probably malformatted and the extraction failed\n", - "files_with_too_many_quotes = own_words_df['filename'].value_counts()[own_words_df['filename'].value_counts() > 100].index.tolist()\n", - "print(f\"Removing {len(files_with_too_many_quotes)} files with too many quotes: {files_with_too_many_quotes}\")\n", - "own_words_df = own_words_df[~own_words_df['filename'].isin(files_with_too_many_quotes)]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "f95affd4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "DataFrame shape: (2515, 2)\n", - "Random 30 rows:\n" - ] - }, - { - "data": { - "text/html": [ - "
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filenameparagraph
617center-immigration-studiesWashington Times
927louis-beam— “New World Order,” 1999 essay by Beam
942bill-whiteIn August 2004, White was convicted of assaulting a woman when she was handing out flyers that identified him as a neo-Nazi landlord. During the court proceedings, White cursed at the woman from the witness stand and was held in contempt of court by the judge. In December 2009, a federal jury found White guilty of threatening several people through intimidating phone calls and internet postings. He was sentenced to 2 ½ years in prison at an April 2010 hearing. In January 2011, White was found guilty of using his website to encourage violence against a jury foreman in the trial of another white supremacist,
973william-pierce— Pierce, quoted in a biography of him called Fame of a Dead Man’s Deeds, on the power of narrative
1967tucker-carlsona reporter later exposed
1116stefan-molyneuxCollective Guilt for Fun and Profit”,
1375malik-zulu-shabazzAudience: “Jews!”
170paul-nehlenNehlen’s 2016 campaign for Congress was ideologically similar to Trump’s and was buoyed by FOX News and other outlets with large, national audiences. Breitbart.com ran several stories on Nehlen, for example. Right-wing pundits such as
2025tucker-carlson“Tucker is ultimately on our side. He can get millions and millions of boomers to nod along with talking points that would have only been seen on VDare or American Renaissance a few years ago,” Greer said on his podcast in the Spring of 2021,
321ronald-doggett“Here in the state of Virginia our Confederate History Month proclamations became so watered down with nods towards the non-Whites they weren’t even worth supporting. We can’t ever have anything just for Whites…I wonder what a true Confederate (White Supremacist) brought to today’s time would think of the cowardly defenders of his cause.”
1000scott-lively“It is not mere coincidence that the emperors of Rome in its horrific final days were homosexual; that Adolf Hitler’s inner circle were mostly homosexual; and that nearly all of the most prolific serial killers in U.S. history were homosexual. It is not mere coincidence that America’s cultural decline parallels the rise of ‘gay rights.’”
2331proud-boys“I am not afraid to speak out about the atrocities that whites and people of European descent face not only here in this country but in Western nations across the world. The war against whites, and Europeans and Western society is very real and it’s time we all started talking about it and stopped worrying about political correctness and optics.” – Kyle Chapman, who formed the Fraternal Order of Alt-Knights, a paramilitary wing of the Proud Boys, Unite America First Peace Rally, Sacramento, California, July 8, 2017
408moms-liberty“Gender dysphoria is a mental health disorder that is being normalized by predators across the USA. California kids are at extreme risk from predatory adults. Now they want to ‘liberate’ children all over the country. Does a double mastectomy on a preteen sound like progress?’
651center-immigration-studiesAmerican Renaissance
1614tomislav-sunicPostmortem Report: Cultural Examinations from Postmodernity
1135stefan-molyneux“If we could just get people to be nice to their babies for five years straight, that would be it for war, drug abuse, addiction, promiscuity, sexually transmitted diseases. Almost all would be completely eliminated, because they all arise from dysfunctional early childhood experiences, which are all run by women.”
582center-immigration-studies(CCC), which Charleston shooter
1961tucker-carlsonserved as a platform
1640michael-flynn“We the people are proud to proclaim that the United States of America is ‘One Nation under God’ – in this public profession of faith in God, we recognize his Lordship over our country, and we proudly stand beneath the banner of Christ and our flag in which millions have sacrificed their very lives for. In scripture through the strength and commitment of Matthew, he said, ‘Whoever is not with Me is against Me.’” – Blog post titled
1073paul-elam“P—- is the only real empowerment women will ever know. Put all the hopelessly wishful thinking of feminist ideology aside and what remains is the fact that it is men, and pretty much men only, who draw power from accomplishment, who invent technology, build nations, cure disease, create empires and generally advance civilization. Women – whether acknowledging it makes us feel warm and fuzzy or not – depend on men for all of that and the only tool they have at their disposal to have any sort of influence on any of it is the power of p—- and p—- is powerful indeed…Sexual robotics may well prove to be the best thing that ever happened to women from the standpoint of their humanity…. what would that do to the vast majority of women who would suddenly have to prove their worth as human beings beyond simply being the owners of said p—-?” – Paul Elam, An Ear for Men, Sex Robots: Part 3 – Disempowering P—-, October 2017
1582mike-cernovichDespite making completely unsubstantiated accusations of pedophilia, Cernovich discussed the allegations that aspiring Alabama senator Roy Moore had sexually abused underage girls cautiously. After tweeting that “If it’s true, string the guy up, man. I got no problem with that,” he later retweeted individuals casting doubt on the validity of the accusers, writing: “When you’re lied about in the news daily, as I am, you pause when 40-year-old accusations surface one month away from an election where WaPo endorsed the other candidate.” Taking it further, however, Cernovich then recorded a podcast giving “scientific” advice to Roy Moore,
907nation-islam“Pedophilia and sexual perversion institutionalized in Hollywood and the entertainment industries can be traced to Talmudic principles and Jewish influence. Now Jewish influence – satanic influence under the name of Jew…The wicked practices that govern their industries are largely justified and influenced by such Talmudic principles. The pervasive rape culture, Hollywood’s casting couch, sex trafficking and prostitution, the age-old buck breaking process that emasculates Black men and corrupts Black women.”
613center-immigration-studiesNo such reprimand occurred when Steinlight,
2039tucker-carlsonthe digital publication.
962edgar-steele“Without a pressure release valve, as open racism once provided, an explosion of epic proportions at some time in the future is guaranteed. There will be a race war, the initial skirmishes of which already are being fought in America’s streets, that will bring an altogether new meaning to the concepts of race war and genocide, courtesy of those who claim to abhor racism.”
2060tucker-carlsonTucker Carlson hosted male supremacist Andrew Tate on August 5, 2022. (Twitter)
1627james-timothy-turner“I am not in jail for violating the law. I am in jail because I stood for righteousness and truth in government. Pray that God will crumble the foundations and break the power and strength of the corporation and restore his righteous government in America.”
2186matt-walshIn addition to spreading disinformation about transgender identity and discredited pseudoscience, Walsh’s film relies on propagandist tactics of narrative manipulation in suggesting legal protections for transgender people will lead to people being prosecuted for using the wrong pronouns. And in making numerous
866barry-black— A 1998 comment to a Virginia newspaper
471david-yerushalmi— “Offensive and Defensive Lawfare: Fighting Civilization Jihad in America’s Courts,” Center for Security Policy press release,
\n", - "
" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Hate Speech Detection with Sentinel: Building and Evaluating a Robust Detection Model\n", + "\n", + "This example demonstrates how to use the Sentinel library to build a robust hate speech detection model with minimal examples and evaluate it across different content sources. Sentinel excels at detecting extremely rare classes of harmful content using contrastive learning principles.\n", + "\n", + "## Data Sources and Methodology\n", + "\n", + "### Training Data:\n", + "- **Positive Examples (~1,500)**: Extracted from the Southern Poverty Law Center's (SPLC) Extremist Files database, specifically from \"In Their Own Words\" sections that contain direct quotes from individuals or groups identified as extremists. These quotes represent authentic examples of hate speech from various extremist ideologies.\n", + "- **Negative Examples (~15,000)**: Obtained from the Lex Fridman podcast dataset, which contains neutral, intellectual discussions. The 10:1 ratio of negative-to-positive examples reflects the typical imbalance in real-world content.\n", + "\n", + "### Evaluation Data:\n", + "- **Test Positives**: Podcast transcripts, known for controversial and potentially harmful content.\n", + "- **Test Negatives**: Different episodes from Lex Fridman's podcast, providing a contrast to potentially harmful content.\n", + "\n", + "### Workflow:\n", + "1. **Data Preparation**: Extract extremist quotes from SPLC and neutral content from conversational datasets. Segment longer texts into manageable chunks of 512 tokens with 128-token stride.\n", + "2. **Model Building**: Create embeddings of both positive and negative examples using the MiniLM-L6-v2 model, then build a Sentinel index to measure semantic similarity to the hate speech class.\n", + "3. **Evaluation**: Score content at both segment and episode levels using contrastive learning and statistical measures like skewness to identify patterns across observations.\n", + "4. **Analysis**: Compare the distribution of hate speech affinity scores between a controversial podcast and Lex Fridman content to validate the model's effectiveness.\n", + "\n", + "This approach demonstrates how Sentinel can detect rare patterns of concerning content with limited training examples, focusing on the semantic similarity to known extremist language rather than requiring extensive labeled datasets." ], - "text/plain": [ - " filename \\\n", - "617 center-immigration-studies \n", - "927 louis-beam \n", - "942 bill-white \n", - "973 william-pierce \n", - "1967 tucker-carlson \n", - "1116 stefan-molyneux \n", - "1375 malik-zulu-shabazz \n", - "170 paul-nehlen \n", - "2025 tucker-carlson \n", - "321 ronald-doggett \n", - "1000 scott-lively \n", - "2331 proud-boys \n", - "408 moms-liberty \n", - "651 center-immigration-studies \n", - "1614 tomislav-sunic \n", - "1135 stefan-molyneux \n", - "582 center-immigration-studies \n", - "1961 tucker-carlson \n", - "1640 michael-flynn \n", - "1073 paul-elam \n", - "1582 mike-cernovich \n", - "907 nation-islam \n", - "613 center-immigration-studies \n", - "2039 tucker-carlson \n", - "962 edgar-steele \n", - "2060 tucker-carlson \n", - "1627 james-timothy-turner \n", - "2186 matt-walsh \n", - "866 barry-black \n", - "471 david-yerushalmi \n", - "\n", - " paragraph \n", - "617 Washington Times \n", - "927 — “New World Order,” 1999 essay by Beam \n", - "942 In August 2004, White was convicted of assaulting a woman when she was handing out flyers that identified him as a neo-Nazi landlord. During the court proceedings, White cursed at the woman from the witness stand and was held in contempt of court by the judge. In December 2009, a federal jury found White guilty of threatening several people through intimidating phone calls and internet postings. He was sentenced to 2 ½ years in prison at an April 2010 hearing. In January 2011, White was found guilty of using his website to encourage violence against a jury foreman in the trial of another white supremacist, \n", - "973 — Pierce, quoted in a biography of him called Fame of a Dead Man’s Deeds, on the power of narrative \n", - "1967 a reporter later exposed \n", - "1116 Collective Guilt for Fun and Profit”, \n", - "1375 Audience: “Jews!” \n", - "170 Nehlen’s 2016 campaign for Congress was ideologically similar to Trump’s and was buoyed by FOX News and other outlets with large, national audiences. Breitbart.com ran several stories on Nehlen, for example. Right-wing pundits such as \n", - "2025 “Tucker is ultimately on our side. He can get millions and millions of boomers to nod along with talking points that would have only been seen on VDare or American Renaissance a few years ago,” Greer said on his podcast in the Spring of 2021, \n", - "321 “Here in the state of Virginia our Confederate History Month proclamations became so watered down with nods towards the non-Whites they weren’t even worth supporting. We can’t ever have anything just for Whites…I wonder what a true Confederate (White Supremacist) brought to today’s time would think of the cowardly defenders of his cause.” \n", - "1000 “It is not mere coincidence that the emperors of Rome in its horrific final days were homosexual; that Adolf Hitler’s inner circle were mostly homosexual; and that nearly all of the most prolific serial killers in U.S. history were homosexual. It is not mere coincidence that America’s cultural decline parallels the rise of ‘gay rights.’” \n", - "2331 “I am not afraid to speak out about the atrocities that whites and people of European descent face not only here in this country but in Western nations across the world. The war against whites, and Europeans and Western society is very real and it’s time we all started talking about it and stopped worrying about political correctness and optics.” – Kyle Chapman, who formed the Fraternal Order of Alt-Knights, a paramilitary wing of the Proud Boys, Unite America First Peace Rally, Sacramento, California, July 8, 2017 \n", - "408 “Gender dysphoria is a mental health disorder that is being normalized by predators across the USA. California kids are at extreme risk from predatory adults. Now they want to ‘liberate’ children all over the country. Does a double mastectomy on a preteen sound like progress?’ \n", - "651 American Renaissance \n", - "1614 Postmortem Report: Cultural Examinations from Postmodernity \n", - "1135 “If we could just get people to be nice to their babies for five years straight, that would be it for war, drug abuse, addiction, promiscuity, sexually transmitted diseases. Almost all would be completely eliminated, because they all arise from dysfunctional early childhood experiences, which are all run by women.” \n", - "582 (CCC), which Charleston shooter \n", - "1961 served as a platform \n", - "1640 “We the people are proud to proclaim that the United States of America is ‘One Nation under God’ – in this public profession of faith in God, we recognize his Lordship over our country, and we proudly stand beneath the banner of Christ and our flag in which millions have sacrificed their very lives for. In scripture through the strength and commitment of Matthew, he said, ‘Whoever is not with Me is against Me.’” – Blog post titled \n", - "1073 “P—- is the only real empowerment women will ever know. Put all the hopelessly wishful thinking of feminist ideology aside and what remains is the fact that it is men, and pretty much men only, who draw power from accomplishment, who invent technology, build nations, cure disease, create empires and generally advance civilization. Women – whether acknowledging it makes us feel warm and fuzzy or not – depend on men for all of that and the only tool they have at their disposal to have any sort of influence on any of it is the power of p—- and p—- is powerful indeed…Sexual robotics may well prove to be the best thing that ever happened to women from the standpoint of their humanity…. what would that do to the vast majority of women who would suddenly have to prove their worth as human beings beyond simply being the owners of said p—-?” – Paul Elam, An Ear for Men, Sex Robots: Part 3 – Disempowering P—-, October 2017 \n", - "1582 Despite making completely unsubstantiated accusations of pedophilia, Cernovich discussed the allegations that aspiring Alabama senator Roy Moore had sexually abused underage girls cautiously. After tweeting that “If it’s true, string the guy up, man. I got no problem with that,” he later retweeted individuals casting doubt on the validity of the accusers, writing: “When you’re lied about in the news daily, as I am, you pause when 40-year-old accusations surface one month away from an election where WaPo endorsed the other candidate.” Taking it further, however, Cernovich then recorded a podcast giving “scientific” advice to Roy Moore, \n", - "907 “Pedophilia and sexual perversion institutionalized in Hollywood and the entertainment industries can be traced to Talmudic principles and Jewish influence. Now Jewish influence – satanic influence under the name of Jew…The wicked practices that govern their industries are largely justified and influenced by such Talmudic principles. The pervasive rape culture, Hollywood’s casting couch, sex trafficking and prostitution, the age-old buck breaking process that emasculates Black men and corrupts Black women.” \n", - "613 No such reprimand occurred when Steinlight, \n", - "2039 the digital publication. \n", - "962 “Without a pressure release valve, as open racism once provided, an explosion of epic proportions at some time in the future is guaranteed. There will be a race war, the initial skirmishes of which already are being fought in America’s streets, that will bring an altogether new meaning to the concepts of race war and genocide, courtesy of those who claim to abhor racism.” \n", - "2060 Tucker Carlson hosted male supremacist Andrew Tate on August 5, 2022. (Twitter) \n", - "1627 “I am not in jail for violating the law. I am in jail because I stood for righteousness and truth in government. Pray that God will crumble the foundations and break the power and strength of the corporation and restore his righteous government in America.” \n", - "2186 In addition to spreading disinformation about transgender identity and discredited pseudoscience, Walsh’s film relies on propagandist tactics of narrative manipulation in suggesting legal protections for transgender people will lead to people being prosecuted for using the wrong pronouns. And in making numerous \n", - "866 — A 1998 comment to a Virginia newspaper \n", - "471 — “Offensive and Defensive Lawfare: Fighting Civilization Jihad in America’s Courts,” Center for Security Policy press release, " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Number of unique extremists: 159\n", - "Unique extremist quotes:\n", - "tucker-carlson 324\n", - "center-immigration-studies 286\n", - "matt-walsh 152\n", - "mike-cernovich 123\n", - "paul-nehlen 114\n", - "stefan-molyneux 28\n", - "greg-johnson 24\n", - "robert-spencer 24\n", - "gays-against-groomers 24\n", - "jack-posobiec 23\n", - "james-lindsay 23\n", - "veterans-patrol 22\n", - "roy-moore 22\n", - "alex-jones 21\n", - "faith-education-commerce 20\n", - "moms-liberty 19\n", - "act-america 19\n", - "august-kreis 19\n", - "family-research-council 19\n", - "pamela-geller 18\n", - "lieutenant-general-william-g-jerry-boykin-ret 17\n", - "proud-boys 17\n", - "david-yerushalmi 17\n", - "richard-bertrand-spencer 16\n", - "frazier-glenn-miller 15\n", - "malik-zulu-shabazz 15\n", - "pickup-artists-alpha-males-self-help 15\n", - "david-duke 15\n", - "roger-pearson 15\n", - "william-h-regnery-ii 15\n", - "center-security-policy 15\n", - "oath-keepers 14\n", - "joseph-francis-farah 14\n", - "michael-levin 14\n", - "cody-rutledge-wilson 14\n", - "elmer-stewart-rhodes 14\n", - "michael-flynn 14\n", - "david-lane 14\n", - "nation-islam 13\n", - "thomas-rousseau 13\n", - "matt-hale 12\n", - "louis-farrakhan 12\n", - "david-irving 12\n", - "scott-lively 12\n", - "peter-brimelow 12\n", - "base 12\n", - "john-tanton 12\n", - "andrew-anglin 12\n", - "bill-white 11\n", - "andrew-weev-auernheimer 11\n", - "charles-murray 11\n", - "nick-fuentes 11\n", - "male-supremacy 11\n", - "misogynist-incels 11\n", - "garrett-hardin 11\n", - "ernst-zundel 11\n", - "don-black 11\n", - "william-shockley 11\n", - "tom-metzger 10\n", - "matthew-heimbach 10\n", - "larry-klayman 10\n", - "vdare 10\n", - "ray-redfeairn 10\n", - "understanding-threat 10\n", - "james-mason 10\n", - "tomislav-sunic 10\n", - "linda-gottfredson 10\n", - "fred-phelps 10\n", - "michael-hill 10\n", - "raymond-cattell 10\n", - "paul-mullet 10\n", - "lydia-brimelow 10\n", - "asatru-folk-assembly 9\n", - "paul-elam 9\n", - "alt-right 9\n", - "michael-ralph-tubbs 9\n", - "jeff-berry 9\n", - "david-barton 9\n", - "craig-cobb 9\n", - "barnes-reviewfoundation-economic-liberty-inc 9\n", - "kyle-bristow 9\n", - "krisanne-hall 9\n", - "jean-philippe-rushton 9\n", - "louis-beam 9\n", - "edgar-steele 9\n", - "tim-wildmon 8\n", - "richard-butler 8\n", - "sam-francis 8\n", - "bo-gritz 8\n", - "tony-perkins 8\n", - "dan-stein 8\n", - "nathan-benjamin-damigo 8\n", - "daryush-roosh-valizadeh 8\n", - "bradley-dean-griffin 8\n", - "nationalist-social-club-nsc-131 8\n", - "remembrance-project 8\n", - "henry-harpending 8\n", - "kevin-strom 8\n", - "bryan-fischer 8\n", - "barry-black 8\n", - "paul-ray-ramsey 8\n", - "kevin-macdonald 8\n", - "frank-gaffney-jr 8\n", - "hal-turner 8\n", - "richard-lynn 7\n", - "jared-taylor 7\n", - "billy-roper 7\n", - "james-timothy-turner 7\n", - "johnny-monoxide-aka-john-ramondetta 7\n", - "atomwaffen-division 7\n", - "willis-carto 7\n", - "arthur-jensen 7\n", - "chuck-baldwin 7\n", - "aryan-brotherhood 7\n", - "wayne-lutton 6\n", - "american-freedom-party 6\n", - "alex-linder 6\n", - "chaya-raichik 6\n", - "thomas-robb 6\n", - "boyd-cathey 6\n", - "proenglish 6\n", - "gary-demar 6\n", - "gary-gerhard-lauck 6\n", - "ronald-doggett 6\n", - "radical-hebrew-israelites 6\n", - "michael-enoch-peinovich 6\n", - "stephen-miller 6\n", - "committee-open-debate-holocaust 5\n", - "michael-brian-vanderboegh-0 5\n", - "virginia-abernethy 5\n", - "april-gaede 5\n", - "larry-pratt 5\n", - "federation-american-immigration-reform 5\n", - "jeff-schoep 5\n", - "jamie-kelso 5\n", - "james-edwards 5\n", - "william-pierce 5\n", - "paul-cameron 5\n", - "kyle-rogers 5\n", - "james-wickstrom 5\n", - "glenn-spencer 4\n", - "barbara-coe 4\n", - "shaun-walker 4\n", - "william-daniel-johnson 4\n", - "ron-edwards 4\n", - "john-de-nugent 4\n", - "erich-gliebe 4\n", - "mark-weber 4\n", - "aryan-freedom-network 4\n", - "james-orien-allsup 4\n", - "kevin-lamb 4\n", - "paul-fromm 3\n", - "roan-garcia-quintana 3\n", - "cliff-kincaid 3\n", - "tom-deweese 3\n", - "jt-ready 3\n", - "vincent-bertollini 3\n", - "gordon-baum 3\n", - "harry-cooper 2\n", - "Name: filename, dtype: int64\n" - ] - } - ], - "source": [ - "pd.set_option(\"display.max_colwidth\", None)\n", - "pd.set_option(\"display.max_rows\", None)\n", - "\n", - "print(f\"DataFrame shape: {own_words_df.shape}\")\n", - "print(\"Random 30 rows:\")\n", - "display(own_words_df.sample(30, random_state=42))\n", - "\n", - "# Display unique filenames to see how many different extremists were extracted\n", - "print(f\"\\nNumber of unique extremists: {own_words_df['filename'].nunique()}\")\n", - "print(\"Unique extremist quotes:\")\n", - "\n", - "print(own_words_df['filename'].value_counts())\n", - "\n", - "if 8 != len(own_words_df[own_words_df[\"filename\"] == \"daryush-roosh-valizadeh\"]):\n", - " print(\"ERROR: Expected 8 quotes for Daryush Roosh Valizadeh, including one very long citation (117 characters).\")\n", - " display(own_words_df[own_words_df[\"filename\"] == \"daryush-roosh-valizadeh\"])\n", - "\n", - "\n", - "pd.set_option(\"display.max_colwidth\", 500)\n", - "pd.set_option(\"display.max_rows\", 10)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "81c31a4a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Max tokens in any paragraph: 322\n", - "Average tokens per paragraph: 42.7\n", - "Number of paragraphs exceeding 512 tokens: 0\n", - "\n", - "Longest paragraph (322 tokens):\n", - "“Only one conclusion is possible… . [T]he broad picture is clear and inescapable: at some point in the foreseeable future the white British people will become a minority in these islands, and whites w...\n" - ] - } - ], - "source": [ - "# Check token counts for some paragraphs\n", - "token_counts = []\n", - "for _, row in own_words_df.iterrows():\n", - " tokens = tokenizer.encode(row['paragraph'], add_special_tokens=False)\n", - " token_counts.append(len(tokens))\n", - "\n", - "print(f\"Max tokens in any paragraph: {max(token_counts)}\")\n", - "print(f\"Average tokens per paragraph: {sum(token_counts)/len(token_counts):.1f}\")\n", - "print(f\"Number of paragraphs exceeding 512 tokens: {sum(1 for count in token_counts if count > 512)}\")\n", - "\n", - "# Check the longest paragraphs\n", - "longest_idx = token_counts.index(max(token_counts))\n", - "print(f\"\\nLongest paragraph ({token_counts[longest_idx]} tokens):\")\n", - "print(own_words_df.iloc[longest_idx]['paragraph'][:200] + \"...\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "609a3723", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Segmented dataset shape: (2515, 4)\n" - ] + "id": "6bbeca2f" }, { - "data": { - "text/html": [ - "
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filenameparagraph_segmentsegment_idlabel
617center-immigration-studieswashington times01
927louis-beama “ new world order, ” 1999 essay by beam01
942bill-whitein august 2004, white was convicted of assaulting a woman when she was handing out flyers that identified him as a neo - nazi landlord. during the court proceedings, white cursed at the woman from the witness stand and was held in contempt of court by the judge. in december 2009, a federal jury found white guilty of threatening several people through intimidating phone calls and internet postings. he was sentenced to 2 a½ years in prison at an april 2010 hearing. in january 2011, white was f...01
973william-piercea pierce, quoted in a biography of him called fame of a dead man ’ s deeds, on the power of narrative01
1967tucker-carlsona reporter later exposed01
...............
866barry-blacka a 1998 comment to a virginia newspaper01
471david-yerushalmia aoffensive and defensive lawfare : fighting civilization jihad in americaas courts, a center for security policy press release,01
1174kevin-strom“ the aryan race, by dint of its intelligence and creativity and character has managed to drag itself up to a state of civilization and some degree of scientific understanding of the universe about us. but what dr. pierce could clearly see, and what the more jingoistic racialists cannot see, is that that state of civilization is but a few inches above the slime of universal savagery. a¦ the journey has just begun and the danger of falling back is very great. ”01
56david-barton“ and we don ’ t want racism in america. but then as you started watching, that ’ s not what this was about. this was about a hate america movement. ” in reference to cancel culture, the 1619 project, and covid - era anti - racism protests. – the elephant heard podcast, april 27, 202101
1457barnes-reviewfoundation-economic-liberty-incajewish involvement in the communist revolution by john wear, j. d. radical jewish activists, political instigators, and criminals were the main protagonists of the arussiana revolution, which overthrew the tzar, resulting in the brutal murder of the royal family and millions of gentiles. john wear highlights and documents the overwhelming evidence that jews played the leading role in this tragedy, using a variety of sources. a01
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33 rows × 4 columns

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" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Before running this notebook, ensure you have installed the example dependencies and registered the Poetry environment as a Jupyter kernel. See the project README for details.\n", + "\n", + "### Downloading the data\n", + "\n", + "There is a script for downloading the Extremist Files from the Southern Poverty Law Center (SPLC) website, the neutral dataset, and the controversial podcast examples" ], - "text/plain": [ - " filename \\\n", - "617 center-immigration-studies \n", - "927 louis-beam \n", - "942 bill-white \n", - "973 william-pierce \n", - "1967 tucker-carlson \n", - "... ... \n", - "866 barry-black \n", - "471 david-yerushalmi \n", - "1174 kevin-strom \n", - "56 david-barton \n", - "1457 barnes-reviewfoundation-economic-liberty-inc \n", - "\n", - " paragraph_segment \\\n", - "617 washington times \n", - "927 a “ new world order, ” 1999 essay by beam \n", - "942 in august 2004, white was convicted of assaulting a woman when she was handing out flyers that identified him as a neo - nazi landlord. during the court proceedings, white cursed at the woman from the witness stand and was held in contempt of court by the judge. in december 2009, a federal jury found white guilty of threatening several people through intimidating phone calls and internet postings. he was sentenced to 2 a½ years in prison at an april 2010 hearing. in january 2011, white was f... \n", - "973 a pierce, quoted in a biography of him called fame of a dead man ’ s deeds, on the power of narrative \n", - "1967 a reporter later exposed \n", - "... ... \n", - "866 a a 1998 comment to a virginia newspaper \n", - "471 a aoffensive and defensive lawfare : fighting civilization jihad in americaas courts, a center for security policy press release, \n", - "1174 “ the aryan race, by dint of its intelligence and creativity and character has managed to drag itself up to a state of civilization and some degree of scientific understanding of the universe about us. but what dr. pierce could clearly see, and what the more jingoistic racialists cannot see, is that that state of civilization is but a few inches above the slime of universal savagery. a¦ the journey has just begun and the danger of falling back is very great. ” \n", - "56 “ and we don ’ t want racism in america. but then as you started watching, that ’ s not what this was about. this was about a hate america movement. ” in reference to cancel culture, the 1619 project, and covid - era anti - racism protests. – the elephant heard podcast, april 27, 2021 \n", - "1457 ajewish involvement in the communist revolution by john wear, j. d. radical jewish activists, political instigators, and criminals were the main protagonists of the arussiana revolution, which overthrew the tzar, resulting in the brutal murder of the royal family and millions of gentiles. john wear highlights and documents the overwhelming evidence that jews played the leading role in this tragedy, using a variety of sources. a \n", - "\n", - " segment_id label \n", - "617 0 1 \n", - "927 0 1 \n", - "942 0 1 \n", - "973 0 1 \n", - "1967 0 1 \n", - "... ... ... \n", - "866 0 1 \n", - "471 0 1 \n", - "1174 0 1 \n", - "56 0 1 \n", - "1457 0 1 \n", - "\n", - "[33 rows x 4 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Apply segmentation to each quote in own_words_df\n", - "segmented_rows = []\n", - "for idx, row in own_words_df.iterrows():\n", - " segments = segment_text(row['paragraph'])\n", - " for i, segment in enumerate(segments):\n", - " segmented_rows.append({\n", - " 'filename': row['filename'],\n", - " 'paragraph_segment': segment,\n", - " 'segment_id': i,\n", - " 'label': 1 # positive example\n", - " })\n", - "\n", - "segmented_df = pd.DataFrame(segmented_rows)\n", - "print(f\"Segmented dataset shape: {segmented_df.shape}\")\n", - "display(segmented_df.sample(33, random_state=42))" - ] - }, - { - "cell_type": "markdown", - "id": "f60af7fa-c933-41f1-86f3-e52034201c06", - "metadata": {}, - "source": [ - "### Download the Neutral example \n", - "Download the neutral dataset from hugging face." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "d5092eff-7e7d-4129-8e01-bbf9b28ee016", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading neutral podcast dataset directly from Hugging Face...\n", - "Using cached file at /Users/evonck/.cache/huggingface/neutral/lex-fridman-podcastUsing cached file.parquet\n", - "Successfully loaded data with 346 rows\n", - "Processed 346 episodes with segments\n" - ] + "id": "caac3997" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Extracting segments: 100%|██████████| 316/316 [00:00<00:00, 3474.10it/s]\n" - ] + "cell_type": "code", + "metadata": {}, + "source": [ + "import os\n", + "import glob\n", + "import pandas as pd\n", + "from sentinel import SentinelLocalIndex\n", + "import re\n", + "from pathlib import Path\n", + "from tqdm.notebook import tqdm\n", + "import random\n", + "import pandas as pd\n", + "from tqdm.notebook import tqdm\n", + "import os\n", + "import requests\n", + "from datasets.utils.file_utils import get_datasets_user_agent" + ], + "execution_count": 1, + "outputs": [], + "id": "d3aa300e" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collected 750250 segments before sampling\n", - "Randomly sampled 15000 segments\n", - "Saved 15000 training segments to neutral-segments-training.csv\n", - "Saved 30 evaluation episodes to neutral-episodes-eval.parquet\n" - ] - } - ], - "source": [ - "%%bash\n", - "# Change directory into the 'scripts' folder\n", - "cd scripts\n", - "\n", - "# Run your Python script using poetry run\n", - "# This ensures all your Poetry-managed dependencies are available\n", - "poetry run python fetch-neutral-examples-data.py\n", - "\n", - "# The script will create two files:\n", - "# 1. `neutral-segments-training.csv`: Contains ~15,000 individual segments (10x our positive examples) for training\n", - "# 2. `neutral-episodes-eval.parquet`: Contains 30 full episodes for evaluation\n", - "# You can customize the number of segments and evaluation episodes:\n", - "# ```bash\n", - "# python fetch-neutral-examples-data.py -n 20000 -e 50 # 20k segments, 50 eval episodes\n", - "# ```" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "922bed03", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of positive examples: 2515\n", - "Number of negative examples to extract: 25150\n", - "Loading neutral podcast dataset directly from Hugging Face...\n", - "Using cached file at /Users/evonck/.cache/huggingface/lex-fridman-podcast/neutral-episodes-eval.parquet\n", - "Successfully loaded data with 346 rows\n", - "Processed 346 episodes with segments\n", - "Dataset loaded with 346 episodes\n", - "Extracting random segments...\n" - ] + "cell_type": "code", + "metadata": {}, + "source": [ + "from transformers import AutoTokenizer\n", + "\n", + "# Use MiniLM tokenizer\n", + "tokenizer = AutoTokenizer.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n", + "\n", + "def segment_text(text, max_tokens=512, stride=128):\n", + " tokens = tokenizer.encode(text, add_special_tokens=False)\n", + " segments = []\n", + " start = 0\n", + " while start < len(tokens):\n", + " end = start + max_tokens\n", + " segment_tokens = tokens[start:end]\n", + " segment_text = tokenizer.decode(segment_tokens)\n", + " segments.append(segment_text)\n", + " if end >= len(tokens):\n", + " break\n", + " start += stride\n", + " return segments" + ], + "execution_count": 2, + "outputs": [], + "id": "64a69da0" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4968b5e5b3804c72b40b1ab265589e5c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Extracting segments: 0%| | 0/346 [00:00 1)):\n", + " # Process the current section\n", + " paragraphs = process_section(current_section_lines)\n", + " all_paragraphs.extend({'filename': filename, 'paragraph': p} for p in paragraphs)\n", + " current_section_lines = []\n", + " in_own_words_section = False\n", + " continue\n", + " \n", + " # If we're in a section, collect the line\n", + " if in_own_words_section:\n", + " current_section_lines.append(line)\n", + " \n", + " # Don't forget to process the last section if we're still in one at the end of the file\n", + " if current_section_lines:\n", + " if not in_own_words_section:\n", + " print(f\"Warning: Reached end of file in {md_file} without closing 'In Their Own Words' section.\")\n", + " print(f\"Collected lines: {current_section_lines}\")\n", + " paragraphs = process_section(current_section_lines)\n", + " all_paragraphs.extend({'filename': filename, 'paragraph': p} for p in paragraphs)\n", + " \n", + " # if sections_found > 0:\n", + " # print(f\"Processing {md_file}: found {sections_found} 'In Their Own Words' sections\")\n", + " \n", + " return all_paragraphs\n", + " except Exception as e:\n", + " print(f\"Error processing {md_file}: {e}\")\n", + " return []\n", + "\n", + "# Helper function to process collected section lines into paragraphs\n", + "def process_section(lines):\n", + " if not lines:\n", + " return []\n", + " \n", + " # Skip if the first line starts with \"Background\" (table of contents)\n", + " if lines and lines[0].strip().startswith(\"Background\"):\n", + " return []\n", + " \n", + " quotes = []\n", + " \n", + " for line in lines:\n", + " line = line.strip()\n", + " if not line:\n", + " continue\n", + " \n", + " # Citation patterns - typically start with a dash, quote mark, or include a year/reference\n", + " is_citation = False\n", + " \n", + " # Patterns that strongly indicate a citation rather than a quote\n", + " if (\n", + " re.match(r'^(—|â|\\xa0?—|\\xa0?–|\"|â\\x80\\x94)$', line) or\n", + " # Lines ending with a year\n", + " re.search(r' \\d{4}\\.?\\s*$', line)\n", + " ) and len(line) < 100:\n", + " # If it looks like a citation, skip it\n", + " is_citation = True\n", + " \n", + " # If not a citation, add as a quote\n", + " if not is_citation:\n", + " quotes.append(line)\n", + " \n", + " # Filter out very short quotes (likely fragments)\n", + " quotes = [q for q in quotes if len(q) > 10]\n", + " \n", + " return quotes\n", + "\n", + "# Find all markdown files in the SPLC Extremist Files scraped dataset directory\n", + "splc_data_dir = \"data/splc_extremist_files\"\n", + "md_files = []\n", + "if os.path.exists(splc_data_dir):\n", + " md_files = glob.glob(os.path.join(splc_data_dir, \"**\", \"*.md\"), recursive=True)\n", + " print(f\"Found {len(md_files)} markdown files.\")\n", + "else:\n", + " print(\"Dataset directory not found. Please run the download cell first.\")\n", + "\n", + "# If we find markdown files, extract \"In Their Own Words\" sections\n", + "if md_files:\n", + " # Process all markdown files and collect paragraphs\n", + " all_paragraphs = []\n", + " for md_file in tqdm(md_files, desc=\"Processing markdown files\"):\n", + " paragraphs = extract_in_their_own_words(md_file)\n", + " all_paragraphs.extend(paragraphs)\n", + " \n", + " # Create a DataFrame from the extracted paragraphs\n", + " own_words_df = pd.DataFrame(all_paragraphs)\n", + " \n", + " if not own_words_df.empty:\n", + " print(f\"Extracted {len(own_words_df)} paragraphs from 'In Their Own Words' sections.\")\n", + " \n", + " else:\n", + " print(\"No 'In Their Own Words' sections found in the markdown files.\")\n", + "else:\n", + " print(\"No markdown files found.\")" + ], + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Found 249 markdown files.\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6ff47bdd94334db8970505b6b189dc05", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Processing markdown files: 0%| | 0/249 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
filenameparagraph_segmentsegment_idlabel
0neutral_podcastAnd I feel like the imagination is a really powerful tool00
1neutral_podcastOh, that was like the smaller one, like the firefly.10
2neutral_podcastComputers weren't really great at that.20
3neutral_podcastto sort of pretend that there's something that they're not in order to understand what's30
4neutral_podcastto math dot square root.40
\n", - "" + "cell_type": "code", + "metadata": {}, + "source": [ + "# Remove quotes from files that produced too many quotes, since these are probably malformatted and the extraction failed\n", + "files_with_too_many_quotes = own_words_df['filename'].value_counts()[own_words_df['filename'].value_counts() > 100].index.tolist()\n", + "print(f\"Removing {len(files_with_too_many_quotes)} files with too many quotes: {files_with_too_many_quotes}\")\n", + "own_words_df = own_words_df[~own_words_df['filename'].isin(files_with_too_many_quotes)]" ], - "text/plain": [ - " filename \\\n", - "0 neutral_podcast \n", - "1 neutral_podcast \n", - "2 neutral_podcast \n", - "3 neutral_podcast \n", - "4 neutral_podcast \n", - "\n", - " paragraph_segment \\\n", - "0 And I feel like the imagination is a really powerful tool \n", - "1 Oh, that was like the smaller one, like the firefly. \n", - "2 Computers weren't really great at that. \n", - "3 to sort of pretend that there's something that they're not in order to understand what's \n", - "4 to math dot square root. \n", - "\n", - " segment_id label \n", - "0 0 0 \n", - "1 1 0 \n", - "2 2 0 \n", - "3 3 0 \n", - "4 4 0 " - ] - }, - "metadata": {}, - "output_type": "display_data" + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Removing 5 files with too many quotes: ['tucker-carlson', 'center-immigration-studies', 'matt-walsh', 'mike-cernovich', 'paul-nehlen']\n" + ] + } + ], + "id": "4b2ac613" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Combined dataset shape: (27665, 4)\n", - "Sample of combined dataset:\n" - ] + "cell_type": "code", + "metadata": {}, + "source": [ + "pd.set_option(\"display.max_colwidth\", None)\n", + "pd.set_option(\"display.max_rows\", None)\n", + "\n", + "print(f\"DataFrame shape: {own_words_df.shape}\")\n", + "print(\"Random 30 rows:\")\n", + "display(own_words_df.sample(30, random_state=42))\n", + "\n", + "# Display unique filenames to see how many different extremists were extracted\n", + "print(f\"\\nNumber of unique extremists: {own_words_df['filename'].nunique()}\")\n", + "print(\"Unique extremist quotes:\")\n", + "\n", + "print(own_words_df['filename'].value_counts())\n", + "\n", + "if 8 != len(own_words_df[own_words_df[\"filename\"] == \"daryush-roosh-valizadeh\"]):\n", + " print(\"ERROR: Expected 8 quotes for Daryush Roosh Valizadeh, including one very long citation (117 characters).\")\n", + " display(own_words_df[own_words_df[\"filename\"] == \"daryush-roosh-valizadeh\"])\n", + "\n", + "\n", + "pd.set_option(\"display.max_colwidth\", 500)\n", + "pd.set_option(\"display.max_rows\", 10)\n" + ], + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "text": [ + "DataFrame shape: (2515, 2)\n", + "Random 30 rows:\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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filenameparagraph
617center-immigration-studiesWashington Times
927louis-beam— “New World Order,” 1999 essay by Beam
942bill-whiteIn August 2004, White was convicted of assaulting a woman when she was handing out flyers that identified him as a neo-Nazi landlord. During the court proceedings, White cursed at the woman from the witness stand and was held in contempt of court by the judge. In December 2009, a federal jury found White guilty of threatening several people through intimidating phone calls and internet postings. He was sentenced to 2 ½ years in prison at an April 2010 hearing. In January 2011, White was found guilty of using his website to encourage violence against a jury foreman in the trial of another white supremacist,
973william-pierce— Pierce, quoted in a biography of him called Fame of a Dead Man’s Deeds, on the power of narrative
1967tucker-carlsona reporter later exposed
1116stefan-molyneuxCollective Guilt for Fun and Profit”,
1375malik-zulu-shabazzAudience: “Jews!”
170paul-nehlenNehlen’s 2016 campaign for Congress was ideologically similar to Trump’s and was buoyed by FOX News and other outlets with large, national audiences. Breitbart.com ran several stories on Nehlen, for example. Right-wing pundits such as
2025tucker-carlson“Tucker is ultimately on our side. He can get millions and millions of boomers to nod along with talking points that would have only been seen on VDare or American Renaissance a few years ago,” Greer said on his podcast in the Spring of 2021,
321ronald-doggett“Here in the state of Virginia our Confederate History Month proclamations became so watered down with nods towards the non-Whites they weren’t even worth supporting. We can’t ever have anything just for Whites…I wonder what a true Confederate (White Supremacist) brought to today’s time would think of the cowardly defenders of his cause.”
1000scott-lively“It is not mere coincidence that the emperors of Rome in its horrific final days were homosexual; that Adolf Hitler’s inner circle were mostly homosexual; and that nearly all of the most prolific serial killers in U.S. history were homosexual. It is not mere coincidence that America’s cultural decline parallels the rise of ‘gay rights.’”
2331proud-boys“I am not afraid to speak out about the atrocities that whites and people of European descent face not only here in this country but in Western nations across the world. The war against whites, and Europeans and Western society is very real and it’s time we all started talking about it and stopped worrying about political correctness and optics.” – Kyle Chapman, who formed the Fraternal Order of Alt-Knights, a paramilitary wing of the Proud Boys, Unite America First Peace Rally, Sacramento, California, July 8, 2017
408moms-liberty“Gender dysphoria is a mental health disorder that is being normalized by predators across the USA. California kids are at extreme risk from predatory adults. Now they want to ‘liberate’ children all over the country. Does a double mastectomy on a preteen sound like progress?’
651center-immigration-studiesAmerican Renaissance
1614tomislav-sunicPostmortem Report: Cultural Examinations from Postmodernity
1135stefan-molyneux“If we could just get people to be nice to their babies for five years straight, that would be it for war, drug abuse, addiction, promiscuity, sexually transmitted diseases. Almost all would be completely eliminated, because they all arise from dysfunctional early childhood experiences, which are all run by women.”
582center-immigration-studies(CCC), which Charleston shooter
1961tucker-carlsonserved as a platform
1640michael-flynn“We the people are proud to proclaim that the United States of America is ‘One Nation under God’ – in this public profession of faith in God, we recognize his Lordship over our country, and we proudly stand beneath the banner of Christ and our flag in which millions have sacrificed their very lives for. In scripture through the strength and commitment of Matthew, he said, ‘Whoever is not with Me is against Me.’” – Blog post titled
1073paul-elam“P—- is the only real empowerment women will ever know. Put all the hopelessly wishful thinking of feminist ideology aside and what remains is the fact that it is men, and pretty much men only, who draw power from accomplishment, who invent technology, build nations, cure disease, create empires and generally advance civilization. Women – whether acknowledging it makes us feel warm and fuzzy or not – depend on men for all of that and the only tool they have at their disposal to have any sort of influence on any of it is the power of p—- and p—- is powerful indeed…Sexual robotics may well prove to be the best thing that ever happened to women from the standpoint of their humanity…. what would that do to the vast majority of women who would suddenly have to prove their worth as human beings beyond simply being the owners of said p—-?” – Paul Elam, An Ear for Men, Sex Robots: Part 3 – Disempowering P—-, October 2017
1582mike-cernovichDespite making completely unsubstantiated accusations of pedophilia, Cernovich discussed the allegations that aspiring Alabama senator Roy Moore had sexually abused underage girls cautiously. After tweeting that “If it’s true, string the guy up, man. I got no problem with that,” he later retweeted individuals casting doubt on the validity of the accusers, writing: “When you’re lied about in the news daily, as I am, you pause when 40-year-old accusations surface one month away from an election where WaPo endorsed the other candidate.” Taking it further, however, Cernovich then recorded a podcast giving “scientific” advice to Roy Moore,
907nation-islam“Pedophilia and sexual perversion institutionalized in Hollywood and the entertainment industries can be traced to Talmudic principles and Jewish influence. Now Jewish influence – satanic influence under the name of Jew…The wicked practices that govern their industries are largely justified and influenced by such Talmudic principles. The pervasive rape culture, Hollywood’s casting couch, sex trafficking and prostitution, the age-old buck breaking process that emasculates Black men and corrupts Black women.”
613center-immigration-studiesNo such reprimand occurred when Steinlight,
2039tucker-carlsonthe digital publication.
962edgar-steele“Without a pressure release valve, as open racism once provided, an explosion of epic proportions at some time in the future is guaranteed. There will be a race war, the initial skirmishes of which already are being fought in America’s streets, that will bring an altogether new meaning to the concepts of race war and genocide, courtesy of those who claim to abhor racism.”
2060tucker-carlsonTucker Carlson hosted male supremacist Andrew Tate on August 5, 2022. (Twitter)
1627james-timothy-turner“I am not in jail for violating the law. I am in jail because I stood for righteousness and truth in government. Pray that God will crumble the foundations and break the power and strength of the corporation and restore his righteous government in America.”
2186matt-walshIn addition to spreading disinformation about transgender identity and discredited pseudoscience, Walsh’s film relies on propagandist tactics of narrative manipulation in suggesting legal protections for transgender people will lead to people being prosecuted for using the wrong pronouns. And in making numerous
866barry-black— A 1998 comment to a Virginia newspaper
471david-yerushalmi— “Offensive and Defensive Lawfare: Fighting Civilization Jihad in America’s Courts,” Center for Security Policy press release,
\n", + "
" + ], + "text/plain": [ + " filename \\\n", + "617 center-immigration-studies \n", + "927 louis-beam \n", + "942 bill-white \n", + "973 william-pierce \n", + "1967 tucker-carlson \n", + "1116 stefan-molyneux \n", + "1375 malik-zulu-shabazz \n", + "170 paul-nehlen \n", + "2025 tucker-carlson \n", + "321 ronald-doggett \n", + "1000 scott-lively \n", + "2331 proud-boys \n", + "408 moms-liberty \n", + "651 center-immigration-studies \n", + "1614 tomislav-sunic \n", + "1135 stefan-molyneux \n", + "582 center-immigration-studies \n", + "1961 tucker-carlson \n", + "1640 michael-flynn \n", + "1073 paul-elam \n", + "1582 mike-cernovich \n", + "907 nation-islam \n", + "613 center-immigration-studies \n", + "2039 tucker-carlson \n", + "962 edgar-steele \n", + "2060 tucker-carlson \n", + "1627 james-timothy-turner \n", + "2186 matt-walsh \n", + "866 barry-black \n", + "471 david-yerushalmi \n", + "\n", + " paragraph \n", + "617 Washington Times \n", + "927 — “New World Order,” 1999 essay by Beam \n", + "942 In August 2004, White was convicted of assaulting a woman when she was handing out flyers that identified him as a neo-Nazi landlord. During the court proceedings, White cursed at the woman from the witness stand and was held in contempt of court by the judge. In December 2009, a federal jury found White guilty of threatening several people through intimidating phone calls and internet postings. He was sentenced to 2 ½ years in prison at an April 2010 hearing. In January 2011, White was found guilty of using his website to encourage violence against a jury foreman in the trial of another white supremacist, \n", + "973 — Pierce, quoted in a biography of him called Fame of a Dead Man’s Deeds, on the power of narrative \n", + "1967 a reporter later exposed \n", + "1116 Collective Guilt for Fun and Profit”, \n", + "1375 Audience: “Jews!” \n", + "170 Nehlen’s 2016 campaign for Congress was ideologically similar to Trump’s and was buoyed by FOX News and other outlets with large, national audiences. Breitbart.com ran several stories on Nehlen, for example. Right-wing pundits such as \n", + "2025 “Tucker is ultimately on our side. He can get millions and millions of boomers to nod along with talking points that would have only been seen on VDare or American Renaissance a few years ago,” Greer said on his podcast in the Spring of 2021, \n", + "321 “Here in the state of Virginia our Confederate History Month proclamations became so watered down with nods towards the non-Whites they weren’t even worth supporting. We can’t ever have anything just for Whites…I wonder what a true Confederate (White Supremacist) brought to today’s time would think of the cowardly defenders of his cause.” \n", + "1000 “It is not mere coincidence that the emperors of Rome in its horrific final days were homosexual; that Adolf Hitler’s inner circle were mostly homosexual; and that nearly all of the most prolific serial killers in U.S. history were homosexual. It is not mere coincidence that America’s cultural decline parallels the rise of ‘gay rights.’” \n", + "2331 “I am not afraid to speak out about the atrocities that whites and people of European descent face not only here in this country but in Western nations across the world. The war against whites, and Europeans and Western society is very real and it’s time we all started talking about it and stopped worrying about political correctness and optics.” – Kyle Chapman, who formed the Fraternal Order of Alt-Knights, a paramilitary wing of the Proud Boys, Unite America First Peace Rally, Sacramento, California, July 8, 2017 \n", + "408 “Gender dysphoria is a mental health disorder that is being normalized by predators across the USA. California kids are at extreme risk from predatory adults. Now they want to ‘liberate’ children all over the country. Does a double mastectomy on a preteen sound like progress?’ \n", + "651 American Renaissance \n", + "1614 Postmortem Report: Cultural Examinations from Postmodernity \n", + "1135 “If we could just get people to be nice to their babies for five years straight, that would be it for war, drug abuse, addiction, promiscuity, sexually transmitted diseases. Almost all would be completely eliminated, because they all arise from dysfunctional early childhood experiences, which are all run by women.” \n", + "582 (CCC), which Charleston shooter \n", + "1961 served as a platform \n", + "1640 “We the people are proud to proclaim that the United States of America is ‘One Nation under God’ – in this public profession of faith in God, we recognize his Lordship over our country, and we proudly stand beneath the banner of Christ and our flag in which millions have sacrificed their very lives for. In scripture through the strength and commitment of Matthew, he said, ‘Whoever is not with Me is against Me.’” – Blog post titled \n", + "1073 “P—- is the only real empowerment women will ever know. Put all the hopelessly wishful thinking of feminist ideology aside and what remains is the fact that it is men, and pretty much men only, who draw power from accomplishment, who invent technology, build nations, cure disease, create empires and generally advance civilization. Women – whether acknowledging it makes us feel warm and fuzzy or not – depend on men for all of that and the only tool they have at their disposal to have any sort of influence on any of it is the power of p—- and p—- is powerful indeed…Sexual robotics may well prove to be the best thing that ever happened to women from the standpoint of their humanity…. what would that do to the vast majority of women who would suddenly have to prove their worth as human beings beyond simply being the owners of said p—-?” – Paul Elam, An Ear for Men, Sex Robots: Part 3 – Disempowering P—-, October 2017 \n", + "1582 Despite making completely unsubstantiated accusations of pedophilia, Cernovich discussed the allegations that aspiring Alabama senator Roy Moore had sexually abused underage girls cautiously. After tweeting that “If it’s true, string the guy up, man. I got no problem with that,” he later retweeted individuals casting doubt on the validity of the accusers, writing: “When you’re lied about in the news daily, as I am, you pause when 40-year-old accusations surface one month away from an election where WaPo endorsed the other candidate.” Taking it further, however, Cernovich then recorded a podcast giving “scientific” advice to Roy Moore, \n", + "907 “Pedophilia and sexual perversion institutionalized in Hollywood and the entertainment industries can be traced to Talmudic principles and Jewish influence. Now Jewish influence – satanic influence under the name of Jew…The wicked practices that govern their industries are largely justified and influenced by such Talmudic principles. The pervasive rape culture, Hollywood’s casting couch, sex trafficking and prostitution, the age-old buck breaking process that emasculates Black men and corrupts Black women.” \n", + "613 No such reprimand occurred when Steinlight, \n", + "2039 the digital publication. \n", + "962 “Without a pressure release valve, as open racism once provided, an explosion of epic proportions at some time in the future is guaranteed. There will be a race war, the initial skirmishes of which already are being fought in America’s streets, that will bring an altogether new meaning to the concepts of race war and genocide, courtesy of those who claim to abhor racism.” \n", + "2060 Tucker Carlson hosted male supremacist Andrew Tate on August 5, 2022. (Twitter) \n", + "1627 “I am not in jail for violating the law. I am in jail because I stood for righteousness and truth in government. Pray that God will crumble the foundations and break the power and strength of the corporation and restore his righteous government in America.” \n", + "2186 In addition to spreading disinformation about transgender identity and discredited pseudoscience, Walsh’s film relies on propagandist tactics of narrative manipulation in suggesting legal protections for transgender people will lead to people being prosecuted for using the wrong pronouns. And in making numerous \n", + "866 — A 1998 comment to a Virginia newspaper \n", + "471 — “Offensive and Defensive Lawfare: Fighting Civilization Jihad in America’s Courts,” Center for Security Policy press release, " + ] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Number of unique extremists: 159\n", + "Unique extremist quotes:\n", + "tucker-carlson 324\n", + "center-immigration-studies 286\n", + "matt-walsh 152\n", + "mike-cernovich 123\n", + "paul-nehlen 114\n", + "stefan-molyneux 28\n", + "greg-johnson 24\n", + "robert-spencer 24\n", + "gays-against-groomers 24\n", + "jack-posobiec 23\n", + "james-lindsay 23\n", + "veterans-patrol 22\n", + "roy-moore 22\n", + "alex-jones 21\n", + "faith-education-commerce 20\n", + "moms-liberty 19\n", + "act-america 19\n", + "august-kreis 19\n", + "family-research-council 19\n", + "pamela-geller 18\n", + "lieutenant-general-william-g-jerry-boykin-ret 17\n", + "proud-boys 17\n", + "david-yerushalmi 17\n", + "richard-bertrand-spencer 16\n", + "frazier-glenn-miller 15\n", + "malik-zulu-shabazz 15\n", + "pickup-artists-alpha-males-self-help 15\n", + "david-duke 15\n", + "roger-pearson 15\n", + "william-h-regnery-ii 15\n", + "center-security-policy 15\n", + "oath-keepers 14\n", + "joseph-francis-farah 14\n", + "michael-levin 14\n", + "cody-rutledge-wilson 14\n", + "elmer-stewart-rhodes 14\n", + "michael-flynn 14\n", + "david-lane 14\n", + "nation-islam 13\n", + "thomas-rousseau 13\n", + "matt-hale 12\n", + "louis-farrakhan 12\n", + "david-irving 12\n", + "scott-lively 12\n", + "peter-brimelow 12\n", + "base 12\n", + "john-tanton 12\n", + "andrew-anglin 12\n", + "bill-white 11\n", + "andrew-weev-auernheimer 11\n", + "charles-murray 11\n", + "nick-fuentes 11\n", + "male-supremacy 11\n", + "misogynist-incels 11\n", + "garrett-hardin 11\n", + "ernst-zundel 11\n", + "don-black 11\n", + "william-shockley 11\n", + "tom-metzger 10\n", + "matthew-heimbach 10\n", + "larry-klayman 10\n", + "vdare 10\n", + "ray-redfeairn 10\n", + "understanding-threat 10\n", + "james-mason 10\n", + "tomislav-sunic 10\n", + "linda-gottfredson 10\n", + "fred-phelps 10\n", + "michael-hill 10\n", + "raymond-cattell 10\n", + "paul-mullet 10\n", + "lydia-brimelow 10\n", + "asatru-folk-assembly 9\n", + "paul-elam 9\n", + "alt-right 9\n", + "michael-ralph-tubbs 9\n", + "jeff-berry 9\n", + "david-barton 9\n", + "craig-cobb 9\n", + "barnes-reviewfoundation-economic-liberty-inc 9\n", + "kyle-bristow 9\n", + "krisanne-hall 9\n", + "jean-philippe-rushton 9\n", + "louis-beam 9\n", + "edgar-steele 9\n", + "tim-wildmon 8\n", + "richard-butler 8\n", + "sam-francis 8\n", + "bo-gritz 8\n", + "tony-perkins 8\n", + "dan-stein 8\n", + "nathan-benjamin-damigo 8\n", + "daryush-roosh-valizadeh 8\n", + "bradley-dean-griffin 8\n", + "nationalist-social-club-nsc-131 8\n", + "remembrance-project 8\n", + "henry-harpending 8\n", + "kevin-strom 8\n", + "bryan-fischer 8\n", + "barry-black 8\n", + "paul-ray-ramsey 8\n", + "kevin-macdonald 8\n", + "frank-gaffney-jr 8\n", + "hal-turner 8\n", + "richard-lynn 7\n", + "jared-taylor 7\n", + "billy-roper 7\n", + "james-timothy-turner 7\n", + "johnny-monoxide-aka-john-ramondetta 7\n", + "atomwaffen-division 7\n", + "willis-carto 7\n", + "arthur-jensen 7\n", + "chuck-baldwin 7\n", + "aryan-brotherhood 7\n", + "wayne-lutton 6\n", + "american-freedom-party 6\n", + "alex-linder 6\n", + "chaya-raichik 6\n", + "thomas-robb 6\n", + "boyd-cathey 6\n", + "proenglish 6\n", + "gary-demar 6\n", + "gary-gerhard-lauck 6\n", + "ronald-doggett 6\n", + "radical-hebrew-israelites 6\n", + "michael-enoch-peinovich 6\n", + "stephen-miller 6\n", + "committee-open-debate-holocaust 5\n", + "michael-brian-vanderboegh-0 5\n", + "virginia-abernethy 5\n", + "april-gaede 5\n", + "larry-pratt 5\n", + "federation-american-immigration-reform 5\n", + "jeff-schoep 5\n", + "jamie-kelso 5\n", + "james-edwards 5\n", + "william-pierce 5\n", + "paul-cameron 5\n", + "kyle-rogers 5\n", + "james-wickstrom 5\n", + "glenn-spencer 4\n", + "barbara-coe 4\n", + "shaun-walker 4\n", + "william-daniel-johnson 4\n", + "ron-edwards 4\n", + "john-de-nugent 4\n", + "erich-gliebe 4\n", + "mark-weber 4\n", + "aryan-freedom-network 4\n", + "james-orien-allsup 4\n", + "kevin-lamb 4\n", + "paul-fromm 3\n", + "roan-garcia-quintana 3\n", + "cliff-kincaid 3\n", + "tom-deweese 3\n", + "jt-ready 3\n", + "vincent-bertollini 3\n", + "gordon-baum 3\n", + "harry-cooper 2\n", + "Name: filename, dtype: int64\n" + ] + } + ], + "id": "f95affd4" }, { - "data": { - "text/html": [ - "
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filenameparagraph_segmentsegment_idlabel
22239neutral_podcastIt wasn't because we were evil rhino haters as a whole.197240
11674neutral_podcastYeah, it also probably says that it's quite useful91590
4097neutral_podcastThat's the problem.15820
5219neutral_podcastbecause anybody can always come back27040
3601neutral_podcastto try to unlock over a five to 10 year period10860
...............
15548neutral_podcastAnd they'll do just fine in those areas as long as pedestrians don't mess with them too130330
17636neutral_podcastSo, so the original one was CASP or critical assessment of of protein structure.151210
8246neutral_podcastAs William James said, death is the warm at the core of the human condition.57310
1972tucker-carlsonjonah bennett, who01
13943neutral_podcastyou can learn what it is to wrestle with difficult ideas114280
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" + "cell_type": "code", + "metadata": {}, + "source": [ + "# Check token counts for some paragraphs\n", + "token_counts = []\n", + "for _, row in own_words_df.iterrows():\n", + " tokens = tokenizer.encode(row['paragraph'], add_special_tokens=False)\n", + " token_counts.append(len(tokens))\n", + "\n", + "print(f\"Max tokens in any paragraph: {max(token_counts)}\")\n", + "print(f\"Average tokens per paragraph: {sum(token_counts)/len(token_counts):.1f}\")\n", + "print(f\"Number of paragraphs exceeding 512 tokens: {sum(1 for count in token_counts if count > 512)}\")\n", + "\n", + "# Check the longest paragraphs\n", + "longest_idx = token_counts.index(max(token_counts))\n", + "print(f\"\\nLongest paragraph ({token_counts[longest_idx]} tokens):\")\n", + "print(own_words_df.iloc[longest_idx]['paragraph'][:200] + \"...\")" ], - "text/plain": [ - " filename \\\n", - "22239 neutral_podcast \n", - "11674 neutral_podcast \n", - "4097 neutral_podcast \n", - "5219 neutral_podcast \n", - "3601 neutral_podcast \n", - "... ... \n", - "15548 neutral_podcast \n", - "17636 neutral_podcast \n", - "8246 neutral_podcast \n", - "1972 tucker-carlson \n", - "13943 neutral_podcast \n", - "\n", - " paragraph_segment \\\n", - "22239 It wasn't because we were evil rhino haters as a whole. \n", - "11674 Yeah, it also probably says that it's quite useful \n", - "4097 That's the problem. \n", - "5219 because anybody can always come back \n", - "3601 to try to unlock over a five to 10 year period \n", - "... ... \n", - "15548 And they'll do just fine in those areas as long as pedestrians don't mess with them too \n", - "17636 So, so the original one was CASP or critical assessment of of protein structure. \n", - "8246 As William James said, death is the warm at the core of the human condition. \n", - "1972 jonah bennett, who \n", - "13943 you can learn what it is to wrestle with difficult ideas \n", - "\n", - " segment_id label \n", - "22239 19724 0 \n", - "11674 9159 0 \n", - "4097 1582 0 \n", - "5219 2704 0 \n", - "3601 1086 0 \n", - "... ... ... \n", - "15548 13033 0 \n", - "17636 15121 0 \n", - "8246 5731 0 \n", - "1972 0 1 \n", - "13943 11428 0 \n", - "\n", - "[50 rows x 4 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Calculate how many negative examples we need (10x the number of positives)\n", - "\n", - "num_positives = len(segmented_df)\n", - "num_negatives = num_positives * 10\n", - "print(f\"Number of positive examples: {num_positives}\")\n", - "print(f\"Number of negative examples to extract: {num_negatives}\")\n", - "\n", - "# Function to download and read parquet file\n", - "def load_neutral_examplest_dataset():\n", - " try:\n", - " print(\"Loading neutral podcast dataset directly from Hugging Face...\")\n", - " # Direct download of the parquet file\n", - " url = \"https://huggingface.co/datasets/Whispering-GPT/lex-fridman-podcast/resolve/main/data/train-00000-of-00001-25f40520d4548308.parquet\"\n", - " \n", - " # Create cache directory\n", - " cache_dir = os.path.join(os.path.expanduser(\"~\"), \".cache\", \"huggingface\", \"lex-fridman-podcast\")\n", - " os.makedirs(cache_dir, exist_ok=True)\n", - " \n", - " # Download the file if it doesn't exist\n", - " local_path = os.path.join(cache_dir, \"neutral-episodes-eval.parquet\")\n", - " \n", - " if not os.path.exists(local_path):\n", - " print(f\"Downloading parquet file to {local_path}...\")\n", - " response = requests.get(url, stream=True)\n", - " response.raise_for_status()\n", - " \n", - " with open(local_path, 'wb') as f:\n", - " for chunk in response.iter_content(chunk_size=8192):\n", - " f.write(chunk)\n", - " print(\"Download complete!\")\n", - " else:\n", - " print(f\"Using cached file at {local_path}\")\n", - " \n", - " # Read the parquet file with pandas\n", - " df = pd.read_parquet(local_path)\n", - " print(f\"Successfully loaded data with {len(df)} rows\")\n", - " \n", - " # Convert to proper format for our code\n", - " podcast_data = []\n", - " for _, row in df.iterrows():\n", - " if 'segments' in row:\n", - " episode = {\"segments\": []}\n", - " # Process segments\n", - " for segment in row['segments']:\n", - " if isinstance(segment, dict) and 'text' in segment and segment['text']:\n", - " episode[\"segments\"].append({\"text\": segment['text']})\n", - " podcast_data.append(episode)\n", - " \n", - " print(f\"Processed {len(podcast_data)} episodes with segments\")\n", - " return podcast_data\n", - " \n", - " except Exception as e:\n", - " print(f\"Error loading dataset: {e}\")\n", - " return None\n", - "\n", - "# Load the Neutral podcast dataset\n", - "neutral_dataset = load_neutral_examplest_dataset()\n", - "\n", - "print(f\"Dataset loaded with {len(neutral_dataset)} episodes\")\n", - "\n", - "# Function to extract random segments from the dataset\n", - "def extract_random_segments(dataset, num_segments):\n", - " all_segments = []\n", - " \n", - " # Iterate through episodes\n", - " for episode in tqdm(dataset, desc=\"Extracting segments\"):\n", - " if \"segments\" in episode and episode[\"segments\"]:\n", - " # Extract text from random segments in this episode\n", - " episode_segments = episode[\"segments\"]\n", - " # Take some random segments from this episode\n", - " num_to_take = len(episode_segments)\n", - " if num_to_take > 0:\n", - " random_segments = random.sample(episode_segments, num_to_take)\n", - " for segment in random_segments:\n", - " if \"text\" in segment and segment[\"text\"]:\n", - " all_segments.append(segment[\"text\"])\n", - " \n", - " print(f\"Collected {len(all_segments)} segments before sampling\")\n", - " \n", - " # If we have more segments than needed, randomly sample\n", - " if len(all_segments) > num_segments:\n", - " all_segments = random.sample(all_segments, num_segments)\n", - " elif len(all_segments) < num_segments:\n", - " print(f\"Warning: Could only collect {len(all_segments)} segments, fewer than the requested {num_segments}\")\n", - " \n", - " return all_segments\n", - "\n", - "# Extract random segments\n", - "print(\"Extracting random segments...\")\n", - "negative_samples = extract_random_segments(neutral_dataset, num_negatives)\n", - "\n", - "# Create a DataFrame with the negative examples structured like segmented_df\n", - "# The segmented_df has 'filename', 'paragraph_segment', 'segment_id', 'label'\n", - "negative_df = pd.DataFrame({\n", - " 'filename': ['neutral_podcast'] * len(negative_samples),\n", - " 'paragraph_segment': negative_samples, # Using paragraph_segment to match positive df\n", - " 'segment_id': range(len(negative_samples)), # Adding segment_id\n", - " 'label': [0] * len(negative_samples) # 0 for negative examples\n", - "})\n", - "\n", - "# Display some statistics\n", - "print(f\"Extracted {len(negative_samples)} negative examples\")\n", - "print(\"\\nSample of negative examples:\")\n", - "display(negative_df.head(5))\n", - "\n", - "# Create a combined dataset\n", - "combined_df = pd.concat([segmented_df, negative_df], ignore_index=True)\n", - "print(f\"\\nCombined dataset shape: {combined_df.shape}\")\n", - "print(\"Sample of combined dataset:\")\n", - "display(combined_df.sample(50, random_state=42))" - ] - }, - { - "cell_type": "markdown", - "id": "fd8e697c-e7a0-453d-8696-2b6ec956feaf", - "metadata": {}, - "source": [ - "## Build The Indexes\n", - "Using the 2 datasets we have loaded, we will build the negative and positive index used by sentinel" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "e966e3b9", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting positive and negative examples...\n", - "Number of positive examples: 2515\n", - "Number of negative examples: 25150\n", - "\n", - "Encoding positive examples...\n", - "\n", - "Encoding negative examples...\n", - "\n", - "Creating Sentinel index...\n", - "\n", - "Saving index...\n", - "Saved index with encoder model: all-MiniLM-L6-v2\n" - ] - } - ], - "source": [ - "# Create a Sentinel hate speech detection index using our own data\n", - "import torch\n", - "from sentinel.sentinel_local_index import SentinelLocalIndex\n", - "from sentinel.embeddings.sbert import get_sentence_transformer_and_scaling_fn\n", - "\n", - "# Initialize sentence model and get scaling function\n", - "model_name = \"all-MiniLM-L6-v2\"\n", - "model, scale_fn = get_sentence_transformer_and_scaling_fn(model_name)\n", - "\n", - "# Prepare examples\n", - "print(\"Extracting positive and negative examples...\")\n", - "positive_examples = segmented_df['paragraph_segment'].tolist() # From extremists (hate speech)\n", - "negative_examples = negative_df['paragraph_segment'].tolist() # From neutral podcast (neutral speech)\n", - "\n", - "print(f\"Number of positive examples: {len(positive_examples)}\")\n", - "print(f\"Number of negative examples: {len(negative_examples)}\")\n", - "\n", - "# Encode examples\n", - "print(\"\\nEncoding positive examples...\")\n", - "positive_embeddings = model.encode(positive_examples, normalize_embeddings=True)\n", - "positive_embeddings = torch.tensor(positive_embeddings)\n", - "\n", - "print(\"\\nEncoding negative examples...\")\n", - "negative_embeddings = model.encode(negative_examples, normalize_embeddings=True)\n", - "negative_embeddings = torch.tensor(negative_embeddings)\n", - "\n", - "# Create the index\n", - "print(\"\\nCreating Sentinel index...\")\n", - "index = SentinelLocalIndex(\n", - " sentence_model=model,\n", - " positive_embeddings=positive_embeddings,\n", - " negative_embeddings=negative_embeddings,\n", - " scale_fn=scale_fn,\n", - " positive_corpus=positive_examples,\n", - " negative_corpus=negative_examples,\n", - ")\n", - "\n", - "# Save locally\n", - "print(\"\\nSaving index...\")\n", - "save_path = \"./hate_speech_model\"\n", - "saved_config = index.save(path=save_path, encoder_model_name_or_path=model_name)\n", - "print(f\"Saved index with encoder model: {saved_config.encoder_model_name_or_path}\")" - ] - }, - { - "cell_type": "markdown", - "id": "bf1a072e-ef45-4438-bd99-fcfa7d24a8e1", - "metadata": {}, - "source": [ - "# Testing the New Sentinel Index\n", - "Let's use our new sentinel index to detect violation in podcast" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "bd2f023f-b51f-4f66-b0ab-296439378c03", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The directory 'data/podcast_examples' already exists. Skipping git clone.\n" - ] + "execution_count": 5, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Max tokens in any paragraph: 322\n", + "Average tokens per paragraph: 42.7\n", + "Number of paragraphs exceeding 512 tokens: 0\n", + "\n", + "Longest paragraph (322 tokens):\n", + "“Only one conclusion is possible… . [T]he broad picture is clear and inescapable: at some point in the foreseeable future the white British people will become a minority in these islands, and whites w...\n" + ] + } + ], + "id": "81c31a4a" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] - } - ], - "source": [ - "%%bash\n", - "TARGET_DIR=\"data/podcast_examples\"\n", - "REPO_URL=\"https://github.com/Fudge/infowars.git\"\n", - "\n", - "if [ -d \"$TARGET_DIR\" ]; then\n", - " echo \"The directory '$TARGET_DIR' already exists. Skipping git clone.\"\n", - "else\n", - " echo \"Cloning '$REPO_URL' into '$TARGET_DIR'...\"\n", - " git clone \"$REPO_URL\" \"$TARGET_DIR\"\n", - " if [ $? -eq 0 ]; then\n", - " echo \"Cloning successful.\"\n", - " else\n", - " echo \"Error: Git clone failed.\"\n", - " fi\n", - "fi" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "a374928d-5c31-4f7e-951f-58f171ad90a7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found 9682 transcript files.\n" - ] - } - ], - "source": [ - "# List transcript files and load a sample for evaluation\n", - "data_dir = \"data/podcast_examples\" # Define data_dir for this cell\n", - "\n", - "transcript_files = glob.glob(os.path.join(data_dir, \"transcripts\", \"**\", \"*.txt\"), recursive=True)\n", - "print(f\"Found {len(transcript_files)} transcript files.\")\n", - "\n", - "# Check if transcript files were found before trying to open one\n", - "if transcript_files:\n", - " # Load the first transcript as an example\n", - " with open(transcript_files[0], \"r\", encoding=\"utf-8\", errors=\"ignore\") as f:\n", - " transcript_text = f.read()\n", - "else:\n", - " print(\"No transcript files found. Please ensure the podcast_examples directory exists and contains transcript files.\")\n", - " # Set a placeholder to avoid errors in subsequent cells\n", - " transcript_text = \"No transcript available.\"" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "455888f7", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Loading saved Sentinel index...\n", - "Index loaded successfully!\n" - ] + "cell_type": "code", + "metadata": {}, + "source": [ + "# Apply segmentation to each quote in own_words_df\n", + "segmented_rows = []\n", + "for idx, row in own_words_df.iterrows():\n", + " segments = segment_text(row['paragraph'])\n", + " for i, segment in enumerate(segments):\n", + " segmented_rows.append({\n", + " 'filename': row['filename'],\n", + " 'paragraph_segment': segment,\n", + " 'segment_id': i,\n", + " 'label': 1 # positive example\n", + " })\n", + "\n", + "segmented_df = pd.DataFrame(segmented_rows)\n", + "print(f\"Segmented dataset shape: {segmented_df.shape}\")\n", + "display(segmented_df.sample(33, random_state=42))" + ], + "execution_count": 6, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Segmented dataset shape: (2515, 4)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "
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filenameparagraph_segmentsegment_idlabel
617center-immigration-studieswashington times01
927louis-beama “ new world order, ” 1999 essay by beam01
942bill-whitein august 2004, white was convicted of assaulting a woman when she was handing out flyers that identified him as a neo - nazi landlord. during the court proceedings, white cursed at the woman from the witness stand and was held in contempt of court by the judge. in december 2009, a federal jury found white guilty of threatening several people through intimidating phone calls and internet postings. he was sentenced to 2 a½ years in prison at an april 2010 hearing. in january 2011, white was f...01
973william-piercea pierce, quoted in a biography of him called fame of a dead man ’ s deeds, on the power of narrative01
1967tucker-carlsona reporter later exposed01
...............
866barry-blacka a 1998 comment to a virginia newspaper01
471david-yerushalmia aoffensive and defensive lawfare : fighting civilization jihad in americaas courts, a center for security policy press release,01
1174kevin-strom“ the aryan race, by dint of its intelligence and creativity and character has managed to drag itself up to a state of civilization and some degree of scientific understanding of the universe about us. but what dr. pierce could clearly see, and what the more jingoistic racialists cannot see, is that that state of civilization is but a few inches above the slime of universal savagery. a¦ the journey has just begun and the danger of falling back is very great. ”01
56david-barton“ and we don ’ t want racism in america. but then as you started watching, that ’ s not what this was about. this was about a hate america movement. ” in reference to cancel culture, the 1619 project, and covid - era anti - racism protests. – the elephant heard podcast, april 27, 202101
1457barnes-reviewfoundation-economic-liberty-incajewish involvement in the communist revolution by john wear, j. d. radical jewish activists, political instigators, and criminals were the main protagonists of the arussiana revolution, which overthrew the tzar, resulting in the brutal murder of the royal family and millions of gentiles. john wear highlights and documents the overwhelming evidence that jews played the leading role in this tragedy, using a variety of sources. a01
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era anti - racism protests. – the elephant heard podcast, april 27, 2021 \n", + "1457 ajewish involvement in the communist revolution by john wear, j. d. radical jewish activists, political instigators, and criminals were the main protagonists of the arussiana revolution, which overthrew the tzar, resulting in the brutal murder of the royal family and millions of gentiles. john wear highlights and documents the overwhelming evidence that jews played the leading role in this tragedy, using a variety of sources. a \n", + "\n", + " segment_id label \n", + "617 0 1 \n", + "927 0 1 \n", + "942 0 1 \n", + "973 0 1 \n", + "1967 0 1 \n", + "... ... ... \n", + "866 0 1 \n", + "471 0 1 \n", + "1174 0 1 \n", + "56 0 1 \n", + "1457 0 1 \n", + "\n", + "[33 rows x 4 columns]" + ] + } + } + ], + "id": "609a3723" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0bc758d716b0447a9afba4dd7cd4610c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing transcripts: 0%| | 0/100 [00:00 0:\n", + " random_segments = random.sample(episode_segments, num_to_take)\n", + " for segment in random_segments:\n", + " if \"text\" in segment and segment[\"text\"]:\n", + " all_segments.append(segment[\"text\"])\n", + " \n", + " print(f\"Collected {len(all_segments)} segments before sampling\")\n", + " \n", + " # If we have more segments than needed, randomly sample\n", + " if len(all_segments) > num_segments:\n", + " all_segments = random.sample(all_segments, num_segments)\n", + " elif len(all_segments) < num_segments:\n", + " print(f\"Warning: Could only collect {len(all_segments)} segments, fewer than the requested {num_segments}\")\n", + " \n", + " return all_segments\n", + "\n", + "# Extract random segments\n", + "print(\"Extracting random segments...\")\n", + "negative_samples = extract_random_segments(neutral_dataset, num_negatives)\n", + "\n", + "# Create a DataFrame with the negative examples structured like segmented_df\n", + "# The segmented_df has 'filename', 'paragraph_segment', 'segment_id', 'label'\n", + "negative_df = pd.DataFrame({\n", + " 'filename': ['neutral_podcast'] * len(negative_samples),\n", + " 'paragraph_segment': negative_samples, # Using paragraph_segment to match positive df\n", + " 'segment_id': range(len(negative_samples)), # Adding segment_id\n", + " 'label': [0] * len(negative_samples) # 0 for negative examples\n", + "})\n", + "\n", + "# Display some statistics\n", + "print(f\"Extracted {len(negative_samples)} negative examples\")\n", + "print(\"\\nSample of negative examples:\")\n", + "display(negative_df.head(5))\n", + "\n", + "# Create a combined dataset\n", + "combined_df = pd.concat([segmented_df, negative_df], ignore_index=True)\n", + "print(f\"\\nCombined dataset shape: {combined_df.shape}\")\n", + "print(\"Sample of combined dataset:\")\n", + "display(combined_df.sample(50, random_state=42))" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Number of positive examples: 2515\n", + "Number of negative examples to extract: 25150\n", + "Loading neutral podcast dataset directly from Hugging Face...\n", + "Using cached file at /Users/evonck/.cache/huggingface/lex-fridman-podcast/neutral-episodes-eval.parquet\n", + "Successfully loaded data with 346 rows\n", + "Processed 346 episodes with segments\n", + "Dataset loaded with 346 episodes\n", + "Extracting random segments...\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4968b5e5b3804c72b40b1ab265589e5c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Extracting segments: 0%| | 0/346 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
filenameparagraph_segmentsegment_idlabel
0neutral_podcastAnd I feel like the imagination is a really powerful tool00
1neutral_podcastOh, that was like the smaller one, like the firefly.10
2neutral_podcastComputers weren't really great at that.20
3neutral_podcastto sort of pretend that there's something that they're not in order to understand what's30
4neutral_podcastto math dot square root.40
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filenameparagraph_segmentsegment_idlabel
22239neutral_podcastIt wasn't because we were evil rhino haters as a whole.197240
11674neutral_podcastYeah, it also probably says that it's quite useful91590
4097neutral_podcastThat's the problem.15820
5219neutral_podcastbecause anybody can always come back27040
3601neutral_podcastto try to unlock over a five to 10 year period10860
...............
15548neutral_podcastAnd they'll do just fine in those areas as long as pedestrians don't mess with them too130330
17636neutral_podcastSo, so the original one was CASP or critical assessment of of protein structure.151210
8246neutral_podcastAs William James said, death is the warm at the core of the human condition.57310
1972tucker-carlsonjonah bennett, who01
13943neutral_podcastyou can learn what it is to wrestle with difficult ideas114280
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" + ], + "text/plain": [ + " filename \\\n", + "22239 neutral_podcast \n", + "11674 neutral_podcast \n", + "4097 neutral_podcast \n", + "5219 neutral_podcast \n", + "3601 neutral_podcast \n", + "... ... \n", + "15548 neutral_podcast \n", + "17636 neutral_podcast \n", + "8246 neutral_podcast \n", + "1972 tucker-carlson \n", + "13943 neutral_podcast \n", + "\n", + " paragraph_segment \\\n", + "22239 It wasn't because we were evil rhino haters as a whole. \n", + "11674 Yeah, it also probably says that it's quite useful \n", + "4097 That's the problem. \n", + "5219 because anybody can always come back \n", + "3601 to try to unlock over a five to 10 year period \n", + "... ... \n", + "15548 And they'll do just fine in those areas as long as pedestrians don't mess with them too \n", + "17636 So, so the original one was CASP or critical assessment of of protein structure. \n", + "8246 As William James said, death is the warm at the core of the human condition. \n", + "1972 jonah bennett, who \n", + "13943 you can learn what it is to wrestle with difficult ideas \n", + "\n", + " segment_id label \n", + "22239 19724 0 \n", + "11674 9159 0 \n", + "4097 1582 0 \n", + "5219 2704 0 \n", + "3601 1086 0 \n", + "... ... ... \n", + "15548 13033 0 \n", + "17636 15121 0 \n", + "8246 5731 0 \n", + "1972 0 1 \n", + "13943 11428 0 \n", + "\n", + "[50 rows x 4 columns]" + ] + } + } + ], + "id": "922bed03" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "Token indices sequence length is longer than the specified maximum sequence length for this model (671 > 512). Running this sequence through the model will result in indexing errors\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Build The Indexes\n", + "Using the 2 datasets we have loaded, we will build the negative and positive index used by sentinel" + ], + "id": "fd8e697c-e7a0-453d-8696-2b6ec956feaf" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Created 2731 segments\n" - ] + "cell_type": "code", + "metadata": {}, + "source": [ + "# Create a Sentinel hate speech detection index using our own data\n", + "import torch\n", + "from sentinel.sentinel_local_index import SentinelLocalIndex\n", + "from sentinel.embeddings.sbert import get_sentence_transformer_and_scaling_fn\n", + "\n", + "# Initialize sentence model and get scaling function\n", + "model_name = \"all-MiniLM-L6-v2\"\n", + "model, scale_fn = get_sentence_transformer_and_scaling_fn(model_name)\n", + "\n", + "# Prepare examples\n", + "print(\"Extracting positive and negative examples...\")\n", + "positive_examples = segmented_df['paragraph_segment'].tolist() # From extremists (hate speech)\n", + "negative_examples = negative_df['paragraph_segment'].tolist() # From neutral podcast (neutral speech)\n", + "\n", + "print(f\"Number of positive examples: {len(positive_examples)}\")\n", + "print(f\"Number of negative examples: {len(negative_examples)}\")\n", + "\n", + "# Encode examples\n", + "print(\"\\nEncoding positive examples...\")\n", + "positive_embeddings = model.encode(positive_examples, normalize_embeddings=True)\n", + "positive_embeddings = torch.tensor(positive_embeddings)\n", + "\n", + "print(\"\\nEncoding negative examples...\")\n", + "negative_embeddings = model.encode(negative_examples, normalize_embeddings=True)\n", + "negative_embeddings = torch.tensor(negative_embeddings)\n", + "\n", + "# Create the index\n", + "print(\"\\nCreating Sentinel index...\")\n", + "index = SentinelLocalIndex(\n", + " sentence_model=model,\n", + " positive_embeddings=positive_embeddings,\n", + " negative_embeddings=negative_embeddings,\n", + " scale_fn=scale_fn,\n", + " positive_corpus=positive_examples,\n", + " negative_corpus=negative_examples,\n", + ")\n", + "\n", + "# Save locally\n", + "print(\"\\nSaving index...\")\n", + "save_path = \"./hate_speech_model\"\n", + "saved_config = index.save(path=save_path, encoder_model_name_or_path=model_name)\n", + "print(f\"Saved index with encoder model: {saved_config.encoder_model_name_or_path}\")" + ], + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Extracting positive and negative examples...\n", + "Number of positive examples: 2515\n", + "Number of negative examples: 25150\n", + "\n", + "Encoding positive examples...\n", + "\n", + "Encoding negative examples...\n", + "\n", + "Creating Sentinel index...\n", + "\n", + "Saving index...\n", + "Saved index with encoder model: all-MiniLM-L6-v2\n" + ] + } + ], + "id": "e966e3b9" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "82f14a11b74146348239229229bd63dd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Calculating rare class affinity: 0%| | 0/100 [00:00 HH:MM:SS] time markers\n", - " cleaned = re.sub(r'\\[\\d{2}:\\d{2}:\\d{2}\\s*-->\\s*\\d{2}:\\d{2}:\\d{2}\\]\\s*', '', text)\n", - " # Remove any remaining timestamps in similar formats\n", - " cleaned = re.sub(r'\\d{2}:\\d{2}:\\d{2}\\s*->\\s*\\d{2}:\\d{2}:\\d{2}\\s*', '', cleaned)\n", - " return cleaned.strip()\n", - "\n", - "# Load and clean transcripts\n", - "cleaned_transcripts = []\n", - "for file_path in tqdm(transcript_files[:100], desc=\"Processing transcripts\"): # Sample 100 episodes\n", - " try:\n", - " with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:\n", - " text = f.read()\n", - " cleaned_text = clean_transcript(text)\n", - " cleaned_transcripts.append({\n", - " 'file': file_path,\n", - " 'text': cleaned_text\n", - " })\n", - " except Exception as e:\n", - " print(f\"Error processing {file_path}: {e}\")\n", - "\n", - "print(f\"Processed {len(cleaned_transcripts)} transcripts\")\n", - "\n", - "# Segment the transcripts\n", - "all_segments = []\n", - "for transcript in tqdm(cleaned_transcripts, desc=\"Segmenting transcripts\"):\n", - " segments = segment_text(transcript['text'])\n", - " for segment in segments:\n", - " all_segments.append({\n", - " 'file': transcript['file'],\n", - " 'segment': segment\n", - " })\n", - "\n", - "print(f\"Created {len(all_segments)} segments\")\n", - "\n", - "# Create a DataFrame from the segments\n", - "results_df = pd.DataFrame(all_segments)\n", - "\n", - "# Group by file and calculate affinity scores for each episode\n", - "episode_results = {}\n", - "segment_scores_dict = {}\n", - "\n", - "# Process each file's segments\n", - "for file_path, group in tqdm(results_df.groupby('file'), desc=\"Calculating rare class affinity\"):\n", - " segments = group['segment'].tolist()\n", - " # Calculate hate speech affinity scores\n", - " result = index.calculate_rare_class_affinity(segments)\n", - " \n", - " # Store episode-level score\n", - " episode_results[file_path] = result.rare_class_affinity_score\n", - " \n", - " # Store individual segment scores\n", - " for segment, score in result.observation_scores.items():\n", - " # Create a tuple key to store both file and segment\n", - " segment_scores_dict[(file_path, segment)] = score\n", - "\n", - "# Create DataFrame for episode-level scores\n", - "episode_scores_df = pd.DataFrame({\n", - " 'file': list(episode_results.keys()),\n", - " 'hate_affinity_score': list(episode_results.values())\n", - "}).sort_values('hate_affinity_score', ascending=False)\n", - "\n", - "# Create DataFrame for segment-level scores\n", - "segments_with_scores = []\n", - "for (file_path, segment), score in segment_scores_dict.items():\n", - " segments_with_scores.append({\n", - " 'file': file_path,\n", - " 'segment': segment,\n", - " 'score': score\n", - " })\n", - "segment_scores_df = pd.DataFrame(segments_with_scores)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "8e1c158c", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using the same Sentinel index for Lex Fridman podcasts...\n", - "Selected 30 random Lex Fridman podcast episodes\n" - ] + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Testing the New Sentinel Index\n", + "Let's use our new sentinel index to detect violation in podcast" + ], + "id": "bf1a072e-ef45-4438-bd99-fcfa7d24a8e1" }, { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "827df5b146bf496bb89d8188ed756c04", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing Neutral episodes: 0%| | 0/30 [00:00 HH:MM:SS] time markers\n", + " cleaned = re.sub(r'\\[\\d{2}:\\d{2}:\\d{2}\\s*-->\\s*\\d{2}:\\d{2}:\\d{2}\\]\\s*', '', text)\n", + " # Remove any remaining timestamps in similar formats\n", + " cleaned = re.sub(r'\\d{2}:\\d{2}:\\d{2}\\s*->\\s*\\d{2}:\\d{2}:\\d{2}\\s*', '', cleaned)\n", + " return cleaned.strip()\n", + "\n", + "# Load and clean transcripts\n", + "cleaned_transcripts = []\n", + "for file_path in tqdm(transcript_files[:100], desc=\"Processing transcripts\"): # Sample 100 episodes\n", + " try:\n", + " with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:\n", + " text = f.read()\n", + " cleaned_text = clean_transcript(text)\n", + " cleaned_transcripts.append({\n", + " 'file': file_path,\n", + " 'text': cleaned_text\n", + " })\n", + " except Exception as e:\n", + " print(f\"Error processing {file_path}: {e}\")\n", + "\n", + "print(f\"Processed {len(cleaned_transcripts)} transcripts\")\n", + "\n", + "# Segment the transcripts\n", + "all_segments = []\n", + "for transcript in tqdm(cleaned_transcripts, desc=\"Segmenting transcripts\"):\n", + " segments = segment_text(transcript['text'])\n", + " for segment in segments:\n", + " all_segments.append({\n", + " 'file': transcript['file'],\n", + " 'segment': segment\n", + " })\n", + "\n", + "print(f\"Created {len(all_segments)} segments\")\n", + "\n", + "# Create a DataFrame from the segments\n", + "results_df = pd.DataFrame(all_segments)\n", + "\n", + "# Group by file and calculate affinity scores for each episode\n", + "episode_results = {}\n", + "segment_scores_dict = {}\n", + "\n", + "# Process each file's segments\n", + "for file_path, group in tqdm(results_df.groupby('file'), desc=\"Calculating rare class affinity\"):\n", + " segments = group['segment'].tolist()\n", + " # Calculate hate speech affinity scores\n", + " result = index.calculate_rare_class_affinity(segments)\n", + " \n", + " # Store episode-level score\n", + " episode_results[file_path] = result.rare_class_affinity_score\n", + " \n", + " # Store individual segment scores\n", + " for segment, score in result.observation_scores.items():\n", + " # Create a tuple key to store both file and segment\n", + " segment_scores_dict[(file_path, segment)] = score\n", + "\n", + "# Create DataFrame for episode-level scores\n", + "episode_scores_df = pd.DataFrame({\n", + " 'file': list(episode_results.keys()),\n", + " 'hate_affinity_score': list(episode_results.values())\n", + "}).sort_values('hate_affinity_score', ascending=False)\n", + "\n", + "# Create DataFrame for segment-level scores\n", + "segments_with_scores = []\n", + "for (file_path, segment), score in segment_scores_dict.items():\n", + " segments_with_scores.append({\n", + " 'file': file_path,\n", + " 'segment': segment,\n", + " 'score': score\n", + " })\n", + "segment_scores_df = pd.DataFrame(segments_with_scores)\n" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Loading saved Sentinel index...\n", + "Index loaded successfully!\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0bc758d716b0447a9afba4dd7cd4610c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Processing transcripts: 0%| | 0/100 [00:00 512). Running this sequence through the model will result in indexing errors\n" + ] + }, + { + "output_type": "stream", + "text": [ + "Created 2731 segments\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "82f14a11b74146348239229229bd63dd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Calculating rare class affinity: 0%| | 0/100 [00:00" + ] + } + }, + { + "output_type": "display_data", + "data": { + "image/png": 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Byi1UdUNCtBrWvV96tOZFt9KGonWtbnBZlLJFEPjb3/6WuyjXbSWPWZ3Lj1dC7CdaTeOLf91W8kq/zsLcXzxurxWTepXFxHQNj2tTx7DcXT+C7aIG5/kp36Iv7kleV5wDcS7ULc8rr7ySf5ZnFY8yRa+PBZUntou6jgA8vwtG5fcY3a/nd+FqccSEYzG5XszaHxPS1RXnSEy+GOH2pJNOSkWb3+cpWq4jhEaAjt4CMbHZz3/+82a/VnzG4/iX7+1e/lzP717vcSElWufHjBkzz2Px+YrPW8y+Pz+Lel7H9tFKHkv87YqJ7+Iz1dg95AHaEmPIAQoULThnnnlm7q45vxmQY2bphuILZyiP7Szft7exgNwcMdty3XHtERwjnMXs2nW/BMdtjOreozi6Oze8PdqilG2vvfbKLezRTbaumME6gkjd118c8Tox5jl6GpR98sknObzEOOSGoaylRCtpwxbRKEPDmbKbOoZxISe678YFmrqzkje8N3ZTHnrooUafV54roGG343HjxuUZrctiLHCcK3E+llsy43ZYjz32WG6tbCjKH8e5vF28z/gMNBTblN/r7rvvni/ORKtnXKxoiZ4Q5dbxaNWPoSB1lyhnnA9NteS3tjgX5jecIS4eRM+ZmH09bqO3MJ+ZmA09xmg3dnEthjGUz4MI2zHT/JVXXjnPLObluohzOuos5ryoO8RlwoQJ+aJGzJK+oKEUC3teR9f4hudE/F2K86X8txGgLdNCDtBKYjKyaB2KoBFfTCOMx8RF0RIbt1pqOFlVXXHbsOiyHrcAiu1jbG3c3idawOLLbflLaEwEdvnll+cvo/GlPca+NhybubCWW265vO+YCC7KG1/uo3UyJl8qizHtEdTjNlMRWmKsb7RI1Z1kbVHLFl1Po6t0tP7Hl/m4fVWEi/hyH11TG+67uWJ89C9/+ct8m6m4P3u07sZ7iVvJxXttOIa9pUR34uglEV2KY5KvCLLRahxBqq4IvBF0YrxshLEYOxtDFqIFMcYMRwjbfPPNc1f3CE0RlmJ4Q7Q+N7y4UVfsL95/zElQ7v0Qk8lFyI5zII55w+7mMdlX3K4txuxGMIvzI7rdl8XEYHFOx3uL4xuTtMW95V966aV8jKNeY5x9hNy47VkE7ZhYLkJcTKwXLZwx4VtMMBeBOIJZXJCJ8y3uPR4Tg0XLbYTICGR17x3fXBG24xg31XIbt+KL4QVxbOI4FymOZ1xIilvIxfGIC0jxuSmL4xMXFuLCybe//e18TBck/hadeuqp+X3GOOzYZ3TRj/qNYBv3WS+L29jF34Y4DuV5BqJO43yLegzRvT32GdvFLc2ii3l83mJfC3Nf+KjzhTmvo3dG9BqKvz/x+YnXifcd52Q8B6DNK3qad4COrnxbn/IStwKK2zDttttu+RZidW+v1dRtz0aPHl3ad999SyuuuGJ+fvw84IAD5rmt1O23315af/31822a6t5mLG55tMEGGzRavqZue3bDDTeURo4cWerbt2++LVbcaqnhbY7Ceeedl2+RFrdrittFPf300/Psc35la3jbs/Itj0aMGJHfZ+fOnUuDBg3KtzWqe1ulpm49Nb/bsTUUt3Q69NBDSyussEI+rhtttFGjt2Zb1NueNbVt+djWve1Z3LapXIa43dqQIUNK//73vxt9D7/+9a9La6yxRr51WMNbYcW/47lxS6hu3bqV1lxzzXzrqqiP+XnkkUfyMdxwww3zc+N4r7LKKvm5dW9ZVfe9xe214lZaUefrrrtuvfdTtw7j/FlrrbXysY33t80225R+9rOf5dvL1RW3Fttiiy3yeRa3xYp6OPHEE/Ntsuq644478j5iu549e5Y++9nP5vO0rKnzvLFzrK5nnnkmH8+TTz65yW3eeuutvE2cl5W67Vlj50lTn8e6df3RRx+VDjzwwHxbsPJtyhraa6+98mOPPvpoaWG88cYbpVNOOaW09dZb5898fE779OmTy3jfffc1eru8L37xi7kMcb7FLdMaHr9nn302n5NxXi+11FKlnXfeeZ7yLOi2kAs6r//73//meojzMG61F9vF7fHiNo0A7UFN/KfoiwIAQNsXvQhijHIMS6Bti8nYokdC3dniAWh7jCEHAOhAYq6H6NYd3b0BaNuMIQcA6ABipviYA+E3v/lNHjce4/MBaNu0kAMAdAAPPvhgbhWPYB4T3TV2/24A2hZjyAEAAKAAWsgBAACgAAI5AAAAFKDDT+o2d+7cNG7cuNSjR49UU1NTdHEAAADo4EqlUpo2bVpaccUV0xJLLFG9gTzC+MCBA4suBgAAAFXmnXfeSSuvvHL1BvJoGS8fiJ49e1a89X3SpEmpT58+873qQcehzquTeq8+6rz6qPPqpN6rjzqvPnMLqvOpU6fmhuFyHq3aQF7uph5hvCUC+cyZM/N+faCrgzqvTuq9+qjz6qPOq5N6rz7qvPrMLbjOFzRs2lkIAAAABRDIAQAAoAACOQAAABSgw48hBwAAWJjbVH3yySfp008/LbooVHgM+Zw5c/I48kqOIV9yySVTp06dFvvW2gI5AABQ1WbPnp3ee++9NGPGjKKLQgtcaIlQHvcEX9zw3NBSSy2VBgwYkLp06dLsfQjkAABA1Yqw9uabb+YWzxVXXDGHq0oHN4rv+dCpAq3ZdfcZF3Hidmpx7gwaNKjZre8COQAAULUiWEUoj3tGR4snHUupBQJ56N69e+rcuXN6++238znUrVu3Zu3HpG4AAEDVc19yijhnnHUAAABQAIEcAAAACmAMOQAAQCNG3vJSq77eqP02atXXqzaHHHJImjx5crrtttsWavu33norrb766um5555Lm266aYuUSQs5AABAOzZ+/Ph09NFHpzXWWCN17do1T1C3zz77pNGjR1fsNXbaaad03HHHpfbsoosuSldffXVqS7SQAwAAtFPRirvtttum3r17p5/+9Kdpo402SnPmzEn33HNPOuqoo9K///3vVp3R/NNPP80zmre22bNnL/B+4L169UptjRZyAACAduo73/lOvp3Xk08+mYYNG5bWXnvttMEGG6Tjjz8+Pf7443mbsWPHpn333Tcts8wyqWfPnukrX/lKmjBhQu0+TjvttNwl+7e//W1abbXVcnD96le/mqZNm1bb1fvBBx/MLczxWrHEhYAHHngg//uuu+5KW2yxRW6df/jhh9OsWbPSMccck/r27ZtvB7bddtulp556Ku8rbjG38sorp8suu6ze+4hu4TFredxGLETX8m9+85upT58+ucy77LJLeuGFF+Yp829+85vcrbx827E//OEP+aJE3JZs+eWXT7vttluaPn167fsYOnRo7T7uvvvuXLa4mBHbfv7zn0+vv/56ak0COQAAQDv0wQcf5FAZLeFLL730PI9H0IwAHGE8to1Qfe+996Y33ngj7b///vW2jSAaY6v/9Kc/5SW2Pfvss/NjEcQHDx6cDj/88PTee+/lJbrFl/3whz/M2/7rX/9KG2+8cTrxxBPTH//4x3TNNdekZ599Nq211lppyJAhuQwRug844IB0/fXX13v93/3ud7mlf9VVV82/f/nLX04TJ07MYf+ZZ55Jm2++efrc5z6X91H22muv5de55ZZb0vPPP5/LFfv+xje+kcsSFwy++MUv5pb7xkRQjwsXTz/9dO7eH2WL7eOYVUUgj6sv5SssdZc4ocLMmTPzv+NqRVzNiSs+da/kAAAAVKsIpBE211133Sa3iaD50ksv5QAcrdhbbbVVuvbaa3PgLrdahwihMb56ww03TNtvv336+te/XjsGPVrMozv4Ukstlfr375+XJZdcsva5Z5xxRm6JXnPNNXMrebR+R/f5PffcM62//vrp17/+dW6xvuKKK/L2Bx10UHrkkUdyy335tW+88ca8PkQre7T433zzzWnLLbdMgwYNSj/72c/yBYZoAa/bTT3ey2abbZYvBEQg/+STT9J+++2Xs2a0lEcPgsiSjYl8GdvGBYNobb/yyivzsfrnP/+ZqiKQxwlQvsISS1ytKV8NCSNGjEh33nlnrog4YcaNG5cPGAAAQLVrquW3rmgpjtbsui3aEZIj3MZjZRFge/ToUfv7gAEDcgv1wojQXLelPcawR2t3WefOndNnP/vZ2teL8LveeuvVtpJH1ovXKufA6Jr+0Ucf1TbMlpc333yzXpfyaE2PLu1lm2yySW5FjyAe+4oLAR9++GGT5X711Vdzi3pMhhfd4uMYhPKFgg4/qVvdgxeim0NcVdlxxx3TlClT8hWUqKQYLxCuuuqqXHExFmLrrbcuqNQAAADFi5bj6GFciYnbIjTXFftd2K7bjXWXX5BoDb/++utzd/f4uccee+QAHiKMxwWB6HLeUFxIaOp1o9U+GnkfffTR9Ne//jX9/Oc/Tz/60Y9yi3scq4ZiJvoI9RHcV1xxxfx+o4dAtLxX3Szr8aavu+663Ic/Kj/GCcSVlV133bV2m+iKscoqq6THHnusyUAeEwjEUjZ16tT8Mw5upccCxP7iqlRrjjGgWOq8Oqn36qPOq486r07qvfo0VufldeWlvgW3QLd2i3ddyy67bB6bfckll+TbnjUMqDExWmSod955J7f6llvJo0t2PBaNnXXfd93Xb7guuqxHd/Cmtin/O1qbY9sIwZHdQuS66B197LHH1m4XLdMnnXRSHr8d3dCjm3v5seiCHrdyi4BdbrVueJwaK3PZNttsk5eTTz45Pz/Gxn//+9+v9/z3338/jRkzJv3qV7/KXfRDlLnu/ht7f42Vo7GsubB/V9pMII+DFCdFzHwXogKiIuteAQn9+vXLjzVl1KhR6fTTT59n/aRJk/KY9EqKgxwt+VEJMQEAHZ86r07qvf245tG3KravZdKs9FH630yvLWH4NvN+waA4PufVSb1Xn8bqPMJirI+wGUv97Vs3kDd8/YVx4YUX5nuER5fwU089NXfXjv3E+O9f/vKX6cUXX8ytvtEifd555+XHIrzvsMMOuet4/F6+KFH39cuBsrwuwvUTTzyRx61H9/Hlllsu3+KsvE15uxhDfuSRR+aJ3WLseVwEiNedMWNGGj58eO12MdP64MGD02GHHZb3s9dee9U+Fu8nGmBjRvTId9G6HUOc//KXv+R1MRa+sTLHuPP77rsvj2eP3tjxe+TAmHm+XM/luo7u+dEiH8coto2LFtGaHqI8dd9TY+dGeX3sL8J9wx4G5Rnq200gj+7pMeg/ugosjpEjR+ZW9rot5HESlKfLr6Q4+NGaH/v2R7w6qPPqpN7bj8lp4ca6LVh8ASulyal7dNpLLSFuBUPb4XNendR79WmszqPRLsJT3Du74f2zzx62cWrrImxG7+If//jH6Qc/+EEOrvH+IrRGq3MExdtvvz3fhiyGAsf7ju7hF198ce37jXVxXOq+//LxKa+LFuZoPI1x2h9//HGeqb08sVvDY3fOOefksHzooYfmYxtjzGM2+IZDlg866KA8iffBBx9cb/x6iPAdATlmdo9QHRPJxUWEyIvxWo2VOXoMxGRx0VU9cmB0R4/J4CLsx3GI58RSfs4NN9yQW+2jRX6dddbJs8nvvPPO+X3VfU+NnRvl9bG/CPbl266VNfy9KTWlRe0X0QLiXnPRtSGmq48p+UNc2YgB+TEIv24reRzU4447Lk/4tjCiIuLKTFwJa4lAHpMPxJcqf8SrgzqvTuq9/Rh5y0sV2lMp9U4z0uS0VIsF8lH7bdQi+6V5fM6rk3qvPo3VeQTymCys7r2s6ThK/68VPcJzBPhKmt+5s7A5tE385YnJ2uJDsffee9euiys6cRWjPNV+iD7+MfYhujYAAABAe9apLVylikAe4wnqdgOIqwkxniC6n8f4hLiqEGMdIoybYR0AAID2rvBA/re//S23en/jG9+Y57ELLrggdyWJG7bHzOkxg+Cll15aSDkBAACgQwXy3Xffvcnp/aMffkzhHwsAAAB0JG1iDDkAAABUG4EcAAAACiCQAwAAQAEEcgAAACiAQA4AAADVOMs6AABAm3Tnsa37evtclDqaQw45JE2ePDnddtttTW6z0047pU033TRdeOGFqdpoIQcAAGinYXfo0KGt9noPPPBAqqmpmWc56aSTmnzORRddlK6++upWK2N7o4UcAACAhTZmzJjUs2fP2t+XWWaZebb59NNPc1jv1atXK5eufdFCDgAA0AG9/PLLac8998yBuV+/funrX/96+u9//1vb2t2lS5f00EMP1W5/7rnnpr59+6YJEybMd7+xTf/+/WuX2H+0gvfu3Tvdcccdaf31109du3ZNY8eOnacVf/r06enggw/OzxkwYEA677zz5tn/aqutls4666za7VZdddW830mTJqV99903r9t4443T008/Xfuc999/Px1wwAFppZVWSksttVTaaKON0g033DBP1/hjjjkmnXjiiWm55ZbLZT/ttNNSkQRyAACADibGbe+yyy5ps802y8H17rvvzkH7K1/5Sm04Pe6443JInzJlSnruuefSySefnH7zm9/k8N4cM2bMSOecc07exz/+8Y8c3Bv6/ve/nx588MF0++23p7/+9a/5wsCzzz47z3YXXHBB2nbbbXO59t5771zOCOhf+9rX8vZrrrlm/r1UKuXtZ86cmbbYYov05z//OV+IOOKII/JznnzyyXr7veaaa9LSSy+dnnjiiXwB4owzzkj33ntvKoou6wAAAB3ML37xixzGf/KTn9Suu/LKK9PAgQPTK6+8ktZee+3cCh1hNMJrhNjhw4enL3zhCwvc98orr1zv97fffjv/nDNnTrr00kvTJpts0ujzPvroo3TFFVek6667Ln3uc5+rDcgN9xf22muvdOSRR+Z/n3LKKemyyy5Ln/nMZ9KXv/zlvO4HP/hBGjx4cL7IEC3d0TL+ve99r/b5Rx99dLrnnnvSTTfdlDbffPPa9dGyfuqpp+Z/Dxo0KB+n0aNHp9122y0VQSAHAADoYF544YV0//33Nzq++/XXX8+BPLqs/+53v8shNbqFR6v0wohu7j169Kj9fdlll80/Y3+xr6bE686ePTtttdVWteuWW265tM4668yzbd39lFvsoxt6w3UTJ07MgTzGrMfFhwjg//nPf/LrzJo1K3dfb2q/IbrNxz6KIpADAAB0MNEavc8+++Qu5A1FCC179NFH888PPvggL9Gde0FWX331PF68oe7du+eJ3Cqhc+fOtf8u77OxdXPnzs0/f/rTn+YZ3ePWaRHc431El/wI5k3tt7yf8j6KYAw5AABABxPdtGMcd0yQttZaa9VbyqE7WqxHjBiRfv3rX+dW6+iy3pLhNMZ9RyCO8dtlH374Ye5Cv7geeeSRPOFbjDGPLvNrrLFGRfbb0gRyAACAdiomZHv++efrLe+880466qijcot3zDz+1FNP5fAdY6oPPfTQ3L07lgivQ4YMyeuuuuqq9OKLLzY663mlRPf5ww47LE/sdt999+Vx6zEL+xJLLH4sjfHgMR4+Wvz/9a9/5fHnC5otvi3QZR0AAKAx+1yU2rqYpTwmb6srQm/MdB6txjH52e67757HU8c48T322CMH4DPPPDNPxvanP/2pthv7r371qxzgY/umJmZbXNG1vNydvkePHumEE07IFxUW10knnZTeeOONfIEhxo3HRHVxu7VK7Lsl1ZTK88R3UFOnTs03o4+KqHvz+kqI7hwxAUBM51+Jqzq0feq8Oqn39mPkLS9VaE+l1DvNSJNTTARTmbFwDY3a7/+fmIbi+ZxXJ/VefRqr87hd1ptvvpnHRXfr1q3oIlJhEXc/+eST1KlTp4qNby+b37mzsDnUXx4AAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAKpeB5/rmjZ6zgjkAABA1ercuXP+OWPGjKKLQjtTPmfK51BzuA85AABQtZZccsnUu3fvfDu0EPewrvTtsehYtz0rlUo5jMc5E+dOnEPNJZADAABVrX///vlnOZTTcZRKpXz/+bjvfKUvtEQYL587zSWQAwAAVS2C2oABA1Lfvn3TnDlzii4OFRRh/P3330/LL798DuWVEt3UF6dlvEwgBwAA+H/d1ysRsmhbgbxz586pW7duFQ3kldL2SgQAAABVQCAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAIACCOQAAABQAIEcAAAACiCQAwAAQAEEcgAAACiAQA4AAAAFEMgBAACgAAI5AAAAFEAgBwAAgAII5AAAAFAAgRwAAAAKIJADAABAAQRyAAAAKIBADgAAAAUQyAEAAKAAAjkAAABUYyD/z3/+k772ta+l5ZdfPnXv3j1ttNFG6emnn659vFQqpVNOOSUNGDAgP77rrrumV199tdAyAwAAQLsO5B9++GHadtttU+fOndNdd92V/vnPf6bzzjsvLbvssrXbnHvuueniiy9Ol19+eXriiSfS0ksvnYYMGZJmzpxZZNEBAABgsXRKBTrnnHPSwIED01VXXVW7bvXVV6/XOn7hhRemk046Ke2777553bXXXpv69euXbrvttvTVr361kHIDAABAuw7kd9xxR27t/vKXv5wefPDBtNJKK6XvfOc76fDDD8+Pv/nmm2n8+PG5m3pZr1690lZbbZUee+yxRgP5rFmz8lI2derU/HPu3Ll5qaTYX1w0qPR+abvUeXVS7+1JqYL7KS8tw/nUtvicVyf1Xn3UefWZW1CdL+zrFRrI33jjjXTZZZel448/Pv3f//1feuqpp9IxxxyTunTpkoYPH57DeIgW8bri9/JjDY0aNSqdfvrp86yfNGlSxbu5x0GeMmVKruAllih8OD6tQJ1XJ/XefvROMyq2r2XS7JRSTWopEydObLF9s+h8zquTeq8+6rz6zC2ozqdNm9b2A3kcnC233DL95Cc/yb9vttlm6eWXX87jxSOQN8fIkSNzwK/bQh7d4vv06ZN69uyZKl3+mpqavG8f6OqgzquTem8/JqdKhdz/tY5PTt1bLJT37du3RfZL8/icVyf1Xn3UefWZW1Cdd+vWre0H8pg5ff3116+3br311kt//OMf87/79++ff06YMCFvWxa/b7rppo3us2vXrnlpKA5+S1RAVG5L7Zu2SZ1XJ/XeXtRUeF/lpfKcS22Pz3l1Uu/VR51Xn5oC6nxhX6vQszBmWB8zZky9da+88kpaddVVayd4i1A+evToei3eMdv64MGDW728AAAAUCmFtpCPGDEibbPNNrnL+le+8pX05JNPpl/96ld5KV/JOO6449JZZ52VBg0alAP6ySefnFZcccU0dOjQIosOAAAA7TeQf+Yzn0m33nprHvd9xhln5MAdtzk76KCDarc58cQT0/Tp09MRRxyRJk+enLbbbrt09913L3SffAAAAGiLCg3k4fOf/3xemhKt5BHWYwEAAICOwkwGAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAIACCOQAAABQAIEcAAAACiCQAwAAQAEEcgAAACiAQA4AAAAFEMgBAACgAAI5AAAAFEAgBwAAgAII5AAAAFAAgRwAAAAKIJADAABAAQRyAAAAKIBADgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAIACCOQAAABQAIEcAAAACiCQAwAAQAEEcgAAACiAQA4AAAAFEMgBAACgAAI5AAAAFEAgBwAAgAII5AAAAFAAgRwAAAAKIJADAABAAQRyAAAAKIBADgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABBHIAAACotkB+2mmnpZqamnrLuuuuW/v4zJkz01FHHZWWX375tMwyy6Rhw4alCRMmFFlkAAAA6Bgt5BtssEF67733apeHH3649rERI0akO++8M918883pwQcfTOPGjUv77bdfoeUFAACASuhUeAE6dUr9+/efZ/2UKVPSFVdcka6//vq0yy675HVXXXVVWm+99dLjjz+ett5660b3N2vWrLyUTZ06Nf+cO3duXiop9lcqlSq+X9oudV6d1Ht7UqrgfspLy3A+tS0+59VJvVcfdV595hZU5wv7eoUH8ldffTWtuOKKqVu3bmnw4MFp1KhRaZVVVknPPPNMmjNnTtp1111rt43u7PHYY4891mQgj+effvrp86yfNGlS7gJf6YMcFw6igpdYovDOBrQCdV6d1Hv70TvNqNi+lkmzU0o1qaVMnDixxfbNovM5r07qvfqo8+ozt6A6nzZtWtsP5FtttVW6+uqr0zrrrJO7q0eQ3n777dPLL7+cxo8fn7p06ZJ69+5d7zn9+vXLjzVl5MiR6fjjj6/XQj5w4MDUp0+f1LNnz4pXbox7j337QFcHdV6d1Hv7MTlVKuT+r3V8cureYqG8b9++LbJfmsfnvDqp9+qjzqvP3ILqPBqc23wg33PPPWv/vfHGG+eAvuqqq6abbropde8eX4IWXdeuXfPSUBz8lqiAqNyW2jdtkzqvTuq9vaip8L7KS+U5l9oen/PqpN6rjzqvPjUF1PnCvlabOgujNXzttddOr732Wh5XPnv27DR58uR628Qs642NOQcAAID2pE0F8o8++ii9/vrracCAAWmLLbZInTt3TqNHj659fMyYMWns2LF5rDkAAAC0Z4V2Wf/e976X9tlnn9xNPW5pduqpp6Yll1wyHXDAAalXr17psMMOy+PBl1tuuTz+++ijj85hvKkJ3QAAAKC9KDSQv/vuuzl8v//++3mQ/XbbbZdvaRb/DhdccEHuez9s2LB8K7MhQ4akSy+9tMgiAwAAQPsP5DfeeOMCZ6a75JJL8gIAAAAdSZsaQw4AAADVQiAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAIACCOQAAABQAIEcAAAACiCQAwAAQAEEcgAAACiAQA4AAAAFEMgBAACgAAI5AAAAFEAgBwAAgAII5AAAAFAAgRwAAAAKIJADAABAAQRyAAAAKIBADgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAIACCOQAAABQAIEcAAAACiCQAwAAQAEEcgAAACiAQA4AAAAFEMgBAACgAAI5AAAAFEAgBwAAgAII5AAAANBeAvkbb7xR+ZIAAABAFWlWIF9rrbXSzjvvnK677ro0c+bMypcKAAAAOrhmBfJnn302bbzxxun4449P/fv3T0ceeWR68sknK186AAAA6KCaFcg33XTTdNFFF6Vx48alK6+8Mr333ntpu+22SxtuuGE6//zz06RJkypfUgAAAOhAFmtSt06dOqX99tsv3Xzzzemcc85Jr732Wvre976XBg4cmA4++OAc1AEAAIAKB/Knn346fec730kDBgzILeMRxl9//fV077335tbzfffdd3F2DwAAAB1Wp+Y8KcL3VVddlcaMGZP22muvdO211+afSyzxv3y/+uqrp6uvvjqtttpqlS4vAAAAVG8gv+yyy9I3vvGNdMghh+TW8cb07ds3XXHFFYtbPgAAAOiQmtVl/dVXX00jR45sMoyHLl26pOHDhy/0Ps8+++xUU1OTjjvuuNp1cUu1o446Ki2//PJpmWWWScOGDUsTJkxoTpEBAACg/Qfy6K4eE7k1FOuuueaaRd7fU089lX75y1/mW6nVNWLEiHTnnXfm/T744IN5XHpMIgcAAABVGchHjRqVVlhhhUa7qf/kJz9ZpH199NFH6aCDDkq//vWv07LLLlu7fsqUKbnLe4xX32WXXdIWW2yRLwQ8+uij6fHHH29OsQEAAKB9jyEfO3ZsnritoVVXXTU/tiiiS/ree++ddt1113TWWWfVrn/mmWfSnDlz8vqyddddN62yyirpscceS1tvvXWj+5s1a1ZeyqZOnZp/zp07Ny+VFPsrlUoV3y9tlzqvTuq9PSlVcD/lpWU4n9oWn/PqpN6rjzqvPnMLqvOFfb1mBfJoCX/xxRfnmUX9hRdeyOO9F9aNN96Ynn322dxlvaHx48fncei9e/eut75fv375sfm13p9++unzrJ80aVIek17pgxwt+VHB5Rnm6djUeXVS7+1H7zSjYvtaJs1OKdWkljJx4sQW2zeLzue8Oqn36qPOq8/cgup82rRpLRfIDzjggHTMMcekHj16pB122CGvizHexx57bPrqV7+6UPt455138vZxz/Ju3bqlSonJ5o4//vh6LeQDBw5Mffr0ST179kyVrtyYiC727QNdHdR5dVLv7cfkVKmQ+7/W8cmpe4uF8ri4Tdvhc16d1Hv1UefVZ25Bdb6wGbdZgfzMM89Mb731Vvrc5z6XOnXqVPtGDz744IUeQx5d0qN1YPPNN69d9+mnn6a///3v6Re/+EW655570uzZs9PkyZPrtZLHLOv9+/dvcr9du3bNS0Nx8FuiAqJyW2rftE3qvDqp9/aipsL7Ki+V51xqe3zOq5N6rz7qvPrUFFDnC/tazQrk0ZX897//fQ7m0U29e/fuaaONNspjyBdWhPmXXnqp3rpDDz00jxP/wQ9+kFu1O3funEaPHp1vdxbGjBmTx6gPHjy4OcUGAACANqNZgbxs7bXXzktzRHf3DTfcsN66pZdeOo9BL68/7LDDcvfz5ZZbLnc3P/roo3MYb2pCNwAAAOjQgTy6ll999dW59Tq6nTecQe6+++6rSOEuuOCC3NQfLeQxc/qQIUPSpZdeWpF9AwAAQLsL5DEZWwTyuF1ZtGZHn/xKeOCBB+YZCH/JJZfkBQAAAFK1B/K4XdlNN92U9tprr8qXCAAAAKrAEs2d1G2ttdaqfGkAAACgSjQrkJ9wwgnpoosuyjdXBwAAAFqpy/rDDz+c7r///nTXXXelDTbYIN+erK5bbrmlObsFAACAqtGsQN67d+/0xS9+sfKlAQAAgCrRrEB+1VVXVb4kAAAAUEWaNYY8fPLJJ+lvf/tb+uUvf5mmTZuW140bNy599NFHlSwfAAAAdEjNaiF/++230x577JHGjh2bZs2alXbbbbfUo0ePdM455+TfL7/88sqXFAAAAKq9hfzYY49NW265Zfrwww9T9+7da9fHuPLRo0dXsnwAAADQITWrhfyhhx5Kjz76aL4feV2rrbZa+s9//lOpsgEAAECH1awW8rlz56ZPP/10nvXvvvtu7roOAAAAtEAg33333dOFF15Y+3tNTU2ezO3UU09Ne+21V3N2CQAAAFWlWV3WzzvvvDRkyJC0/vrrp5kzZ6YDDzwwvfrqq2mFFVZIN9xwQ+VLCQAAAB1MswL5yiuvnF544YV04403phdffDG3jh922GHpoIMOqjfJGwAAAFDBQJ6f2KlT+trXvtbcpwMAAEBVa1Ygv/baa+f7+MEHH9zc8gAAAEBV6NTc+5DXNWfOnDRjxox8G7SlllpKIAcAAICWmGX9ww8/rLfEGPIxY8ak7bbbzqRuAAAA0FKBvDGDBg1KZ5999jyt5wAAAEALBvLyRG/jxo2r5C4BAACgQ2rWGPI77rij3u+lUim999576Re/+EXadtttK1U2AAAA6LCaFciHDh1a7/eamprUp0+ftMsuu6TzzjuvUmUDAACADqtZgXzu3LmVLwkAAABUkYqOIQcAAABasIX8+OOPX+htzz///Oa8BAAAAHRozQrkzz33XF7mzJmT1llnnbzulVdeSUsuuWTafPPN640tBwAAACoUyPfZZ5/Uo0ePdM0116Rll102r/vwww/ToYcemrbffvt0wgknNGe3AAAAUDWaNYY8ZlIfNWpUbRgP8e+zzjrLLOsAAADQUoF86tSpadKkSfOsj3XTpk1rzi4BAACgqjQrkH/xi1/M3dNvueWW9O677+blj3/8YzrssMPSfvvtV/lSAgAAQAfTrDHkl19+efre976XDjzwwDyxW95Rp045kP/0pz+tdBkBAACgw2lWIF9qqaXSpZdemsP366+/ntetueaaaemll650+QAAAKBDalaX9bL33nsvL4MGDcphvFQqVa5kAAAA0IE1K5C///776XOf+1xae+2101577ZVDeYgu6255BgAAAC0UyEeMGJE6d+6cxo4dm7uvl+2///7p7rvvbs4uAQAAoKo0awz5X//613TPPfeklVdeud766Lr+9ttvV6psAAAA0GE1q4V8+vTp9VrGyz744IPUtWvXSpQLAAAAOrRmBfLtt98+XXvttbW/19TUpLlz56Zzzz037bzzzpUsHwAAAHRIzeqyHsE7JnV7+umn0+zZs9OJJ56Y/vGPf+QW8kceeaTypQQAAIAOplkt5BtuuGF65ZVX0nbbbZf23Xff3IV9v/32S88991y+HzkAAABQ4RbyOXPmpD322CNdfvnl6Uc/+tGiPh0AAABoTgt53O7sxRdfbJnSAAAAQJVoVpf1r33ta+mKK66ofGkAAACgSjRrUrdPPvkkXXnllelvf/tb2mKLLdLSSy9d7/Hzzz+/UuUDAACADmmRAvkbb7yRVltttfTyyy+nzTffPK+Lyd3qilugAQAAABUM5IMGDUrvvfdeuv/++/Pv+++/f7r44otTv379FmU3AAAAUPUWaQx5qVSq9/tdd92Vb3kGAAAAtMKkbk0FdAAAAKAFAnmMD284RtyYcQAAAGjhMeTRIn7IIYekrl275t9nzpyZvvWtb80zy/ott9zSjKIAAABA9VikQD58+PB57kcOAAAAtHAgv+qqq5rxEgAAAEBFJ3VbXJdddlnaeOONU8+ePfMyePDgPHN7WXSJP+qoo9Lyyy+flllmmTRs2LA0YcKEIosMAAAA7T+Qr7zyyunss89OzzzzTHr66afTLrvskvbdd9/0j3/8Iz8+YsSIdOedd6abb745Pfjgg2ncuHFpv/32K7LIAAAA0Ppd1ittn332qff7j3/849xq/vjjj+ewfsUVV6Trr78+B/Vyl/n11lsvP7711lsXVGoAAABo54G8rk8//TS3hE+fPj13XY9W8zlz5qRdd921dpt11103rbLKKumxxx5rMpDPmjUrL2VTp07NP+fOnZuXSor9xczzld4vbZc6r07qvT0pVXA/5aVlOJ/aFp/z6qTeq486rz5zC6rzhX29wgP5Sy+9lAN4jBePceK33nprWn/99dPzzz+funTpknr37l1v+379+qXx48c3ub9Ro0al008/fZ71kyZNyq9R6YM8ZcqUXMFLLFFo739aiTqvTuq9/eidZlRsX8uk2SmlmtRSJk6c2GL7ZtH5nFcn9V591Hn1mVtQnU+bNq19BPJ11lknh+84SH/4wx/yrdVivHhzjRw5Mh1//PH1WsgHDhyY+vTpkyeOq3Tl1tTU5H37QFcHdV6d1Hv7MTlVKuT+r3V8cureYqG8b9++LbJfmsfnvDqp9+qjzqvP3ILqvFu3bu0jkEcr+FprrZX/vcUWW6SnnnoqXXTRRWn//fdPs2fPTpMnT67XSh6zrPfv37/J/XXt2jUvDcXBb4kKiMptqX3TNqnz6qTe24uaCu+rvFSec6nt8TmvTuq9+qjz6lNTQJ0v7Gst0RavYMQY8AjnnTt3TqNHj659bMyYMWns2LG5izsAAAC0Z4W2kEf38j333DNP1BZ97GNG9QceeCDdc889qVevXumwww7L3c+XW2653N386KOPzmHcDOsAAAC0d4UG8pjQ5uCDD07vvfdeDuAbb7xxDuO77bZbfvyCCy7ITf3Dhg3LreZDhgxJl156aZFFBgAAgPYfyOM+4wsaCH/JJZfkBQAAADqSNjeGHAAAAKqBQA4AAAAFEMgBAACgAAI5AAAAFEAgBwAAgAII5AAAAFAAgRwAAAAKIJADAABAAQRyAAAAKIBADgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAIACCOQAAABQAIEcAAAACiCQAwAAQAEEcgAAACiAQA4AAAAFEMgBAACgAAI5AAAAFEAgBwAAgAII5AAAAFAAgRwAAAAKIJADAABAAQRyAAAAKIBADgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAIACCOQAAABQAIEcAAAACiCQAwAAQAEEcgAAACiAQA4AAAAFEMgBAACgAAI5AAAAVFsgHzVqVPrMZz6TevTokfr27ZuGDh2axowZU2+bmTNnpqOOOiotv/zyaZlllknDhg1LEyZMKKzMAAAA0O4D+YMPPpjD9uOPP57uvffeNGfOnLT77run6dOn124zYsSIdOedd6abb745bz9u3Li03377FVlsAAAAWGydUoHuvvvuer9fffXVuaX8mWeeSTvssEOaMmVKuuKKK9L111+fdtlll7zNVVddldZbb70c4rfeeuuCSg4AAADtOJA3FAE8LLfccvlnBPNoNd91111rt1l33XXTKquskh577LFGA/msWbPyUjZ16tT8c+7cuXmppNhfqVSq+H5pu9R5dVLv7UmpgvspLy3D+dS2+JxXJ/VefdR59ZlbUJ0v7Ou1mUAeBT7uuOPStttumzbccMO8bvz48alLly6pd+/e9bbt169ffqypcemnn376POsnTZqUx6NXusxxESEqeIklzI9XDdR5dVLv7UfvNKNi+1omzU4p1aSWMnHixBbbN4vO57w6qffqo86rz9yC6nzatGntK5DHWPKXX345Pfzww4u1n5EjR6bjjz++Xgv5wIEDU58+fVLPnj1TpSu3pqYm79sHujqo8+qk3tuPyalSIfd/reOTU/cWC+UxRIu2w+e8Oqn36qPOq8/cguq8W7du7SeQf/e7301/+tOf0t///ve08sor167v379/mj17dpo8eXK9VvKYZT0ea0zXrl3z0lAc/JaogKjclto3bZM6r07qvb2oqfC+ykvlOZfaHp/z6qTeq486rz41BdT5wr5WoWdhdBuIMH7rrbem++67L62++ur1Ht9iiy1S586d0+jRo2vXxW3Rxo4dmwYPHlxAiQEAAKAyOhXdTT1mUL/99tvzvcjL48J79eqVunfvnn8edthhuQt6TPQWXc6PPvroHMbNsA4AAEB7Vmggv+yyy/LPnXbaqd76uLXZIYcckv99wQUX5Ob+YcOG5dnThwwZki699NJCygsAAAAdIpBHl/WFGQx/ySWX5AUAAAA6CjMZAAAAQAEEcgAAACiAQA4AAAAFEMgBAACgAAI5AAAAFEAgBwAAgAII5AAAAFAAgRwAAAAKIJADAABAAQRyAAAAKIBADgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAIACCOQAAABQAIEcAAAACiCQAwAAQAEEcgAAACiAQA4AAAAFEMgBAACgAAI5AAAAFEAgBwAAgAII5AAAAFAAgRwAAAAKIJADAABAAQRyAAAAKIBADgAAAAUQyAEAAKAAAjkAAAAUoFMRLwoAbd3W79+Sun48PtWkUpPb3Lbyia1aJgCgY9FCDgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAIACCOQAAABQAIEcAAAAqi2Q//3vf0/77LNPWnHFFVNNTU267bbb6j1eKpXSKaeckgYMGJC6d++edt111/Tqq68WVl4AAADoEIF8+vTpaZNNNkmXXHJJo4+fe+656eKLL06XX355euKJJ9LSSy+dhgwZkmbOnNnqZQUAAIBK6pQKtOeee+alMdE6fuGFF6aTTjop7bvvvnndtddem/r165db0r/61a+2cmkBAACggwTy+XnzzTfT+PHjczf1sl69eqWtttoqPfbYY00G8lmzZuWlbOrUqfnn3Llz81JJsb+4cFDp/dJ2qfPqpN7bk1LF9hN7KqWaFns951Pb4nNendR79VHn1WduQXW+sK/XZgN5hPEQLeJ1xe/lxxozatSodPrpp8+zftKkSRXv6h4HecqUKbmCl1jC/HjVQJ1XJ/XefvROMyq2r0+6LJtqciAvtcjrTZw4sdnPpfJ8zquTeq8+6rz6zC2ozqdNm9a+A3lzjRw5Mh1//PH1WsgHDhyY+vTpk3r27Fnxyo3J6GLfPtDVQZ1XJ/XefkxOlQq5pdRp9oepy8cTUs18AvnktFSzX6Fv377Nfi6V53NendR79VHn1WduQXXerVu39h3I+/fvn39OmDAhz7JeFr9vuummTT6va9eueWkoDn5LVEBUbkvtm7ZJnVcn9d5e1FR0TxHG5xfIF+f1nEttj895dVLv1UedV5+aAup8YV+rzZ6Fq6++eg7lo0ePrtfaHbOtDx48uNCyAQAAwOIqtIX8o48+Sq+99lq9idyef/75tNxyy6VVVlklHXfccemss85KgwYNygH95JNPzvcsHzp0aJHFBgAAgPYdyJ9++um088471/5eHvs9fPjwdPXVV6cTTzwx36v8iCOOSJMnT07bbbdduvvuuxe6Pz4AAAC0VYUG8p122inPdje/vv5nnHFGXgAAAKAjabNjyAEAAKAjE8gBAACgAAI5AAAAFEAgBwAAgAII5AAAAFAAgRwAAAAKIJADAABAAQRyAAAAKIBADgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoACdinhRAIBKGnnLS4u5h1LqnWakyWliSqkmtZRR+22UqueYto72dEwBGtJCDgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkAAAAUACzrANAR3PnsQu33T4XtXRJAID50EIOAAAABRDIAQAAoAACOQAAABRAIAcAAIACCOQAAABQAIEcAAAACuC2ZwAAtFsjb3lpMZ5dSr3TjDQ5TUwp1aSWNGq/jVp0/0D7pIUcAAAACiCQAwAAQAEEcgAAACiAQA4AAAAFEMgBAACgAGZZB4C24M5jU8ebwRpoj58lM8JD69FCDgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkAAAAUACzrAMAtJL2NNM2AC1PCzkAAAAUQCAHAACAAgjkAAAAUACBHAAAAAogkAMAAEABzLLehrSXmVdH7bdRqt5jWkq904w0OU1MKdWkatOe6h5oXUPfPbdVX++2lU+saLlKqSbN6t4/df14fKpJpcV6zbZoYY9De36PVN930vbyXa69fH9qT/U+qp0c04WhhRwAAAAKIJADAABAAdpFIL/kkkvSaqutlrp165a22mqr9OSTTxZdJAAAAOjYgfz3v/99Ov7449Opp56ann322bTJJpukIUOGpIkTY9wHAAAAtE9tPpCff/756fDDD0+HHnpoWn/99dPll1+ellpqqXTllVcWXTQAAADomLOsz549Oz3zzDNp5MiRteuWWGKJtOuuu6bHHnus0efMmjUrL2VTpkzJPydPnpzmzp1b0fLF/qZOnZq6dOmSy7W4Zs2YltqDOJbtReWPaSnNTB+nWenTqpxlvT3VfVv+rNMePvOl9NHHs9KcmXOanHF7cV9vns/T9NnN3tdiFGKBmyzse/xo5pzUmipdrjzLepp/nbeX/08vznFoz++xear7/+vVqfXrvL18f2pPn//Ji3BMi/oeF68ZSqWmv0eEmtKCtijQuHHj0korrZQeffTRNHjw4Nr1J554YnrwwQfTE088Mc9zTjvttHT66ae3ckkBAACgvnfeeSetvPLKqV22kDdHtKbHmPO6V0Q++OCDtPzyy6eampqKX/UYOHBgPsg9e/as6L5pm9R5dVLv1UedVx91Xp3Ue/VR59VnakF1Hu3e06ZNSyuuuOJ8t2vTgXyFFVZISy65ZJowYUK99fF7//79G31O165d81JX7969W7ScUbE+0NVFnVcn9V591Hn1UefVSb1XH3VefXoWUOe9evVa4DZtejBk9PPfYost0ujRo+u1eMfvdbuwAwAAQHvTplvIQ3Q/Hz58eNpyyy3TZz/72XThhRem6dOn51nXAQAAoL1q84F8//33T5MmTUqnnHJKGj9+fNp0003T3Xffnfr161d00XLX+Lg/esMu8nRc6rw6qffqo86rjzqvTuq9+qjz6tO1jdd5m55lHQAAADqqNj2GHAAAADoqgRwAAAAKIJADAABAAQRyAAAAKIBAvog++OCDdNBBB+Wbyvfu3Tsddthh6aOPPlrg8x577LG0yy67pKWXXjo/d4cddkgff/xxq5SZYuo8xJyJe+65Z6qpqUm33XZbi5eVYuo8tj/66KPTOuusk7p3755WWWWVdMwxx6QpU6a0arlZNJdccklabbXVUrdu3dJWW22Vnnzyyfluf/PNN6d11103b7/RRhulv/zlL61WVlq/zn/961+n7bffPi277LJ52XXXXRd4jtAxPutlN954Y/7/99ChQ1u8jBRb55MnT05HHXVUGjBgQJ6Je+211/Y3voPX+YUXXlj7vW3gwIFpxIgRaebMmakQMcs6C2+PPfYobbLJJqXHH3+89NBDD5XWWmut0gEHHDDf5zz66KOlnj17lkaNGlV6+eWXS//+979Lv//970szZ85stXLTunVedv7555f23HPPuJNB6dZbb23xslJMnb/00kul/fbbr3THHXeUXnvttdLo0aNLgwYNKg0bNqxVy83Cu/HGG0tdunQpXXnllaV//OMfpcMPP7zUu3fv0oQJExrd/pFHHiktueSSpXPPPbf0z3/+s3TSSSeVOnfunOuejlnnBx54YOmSSy4pPffcc6V//etfpUMOOaTUq1ev0rvvvtvqZaf16r3szTffLK200kql7bffvrTvvvu2Wnlp/TqfNWtWacsttyzttddepYcffjjX/QMPPFB6/vnnW73stE6d/+53vyt17do1/4z6vueee0oDBgwojRgxolQEgXwRxJewCFZPPfVU7bq77rqrVFNTU/rPf/7T5PO22mqr/OWN6qnzEF/i4n/m7733nkBeJXVe10033ZT/5zBnzpwWKimL47Of/WzpqKOOqv39008/La244or5wmljvvKVr5T23nvvef62H3nkkS1eVoqp84Y++eSTUo8ePUrXXHNNC5aStlDvUdfbbLNN6Te/+U1p+PDhAnkHr/PLLrustMYaa5Rmz57diqWkyDo/6qijSrvssku9dccff3xp2223LRVBl/VFEN3Oo/vqlltuWbsuurAtscQS6Yknnmj0ORMnTsyP9e3bN22zzTapX79+accdd0wPP/xwK5ac1qzzMGPGjHTggQfm7jP9+/dvpdJSZJ03FN3Vo8t7p06dWqikNNfs2bPTM888k+u1LOo3fo/6b0ysr7t9GDJkSJPb0/7rvLG/63PmzEnLLbdcC5aUtlDvZ5xxRv7eFsOV6Ph1fscdd6TBgwfnLuvxPX3DDTdMP/nJT9Knn37aiiWnNes8Mlk8p9yt/Y033shDFPbaa69UBN8UF8H48ePzH+i64st2/M85HmtMVHA47bTT0s9+9rO06aabpmuvvTZ97nOfSy+//HIaNGhQq5Sd1qvzEONQ4sO+7777tkIpaQt1Xtd///vfdOaZZ6YjjjiihUrJ4oj6iS9a8cWrrvj93//+d6PPibpvbPuFPSdof3Xe0A9+8IO04oorznNhho5V79FgcsUVV6Tnn3++lUpJ0XUe39Xvu+++PHdMhLLXXnstfec738kX4E499dRWKjmtWefRaBbP22677fJ8T5988kn61re+lf7v//4vFUELeUrphz/8YZ60Y37Lwv4Pu6G5c+fmn0ceeWQ69NBD02abbZYuuOCCPInAlVdeWeF3Qluo87jSGn/YY7IIqqPO65o6dWrae++90/rrr58vxAHt39lnn50n+Lr11lvzhEF0TNOmTUtf//rX84R+K6ywQtHFoZXEd/W4EP+rX/0qbbHFFmn//fdPP/rRj9Lll19edNFoIQ888EDuBXHppZemZ599Nt1yyy3pz3/+c25MKYIW8pTSCSeckA455JD5brPGGmvkrsfRBb2uuKISMyw31S05ZmsM8eW8rvXWWy+NHTt2sctO26vzCOOvv/567vZc17Bhw/KMvfFHgI5V53W/zO2xxx6pR48e+Yt7586dK1J2Kiu+aC+55JJpwoQJ9dbH703VcaxflO1p/3VeFr3bIpD/7W9/SxtvvHELl5Qi6z3+3/3WW2+lffbZZ56GlegpNWbMmLTmmmu2Qslpzc96fFeP/1/H8+p+T48eUNEdukuXLi1eblq3zk8++eR88e2b3/xm/j3unDJ9+vTcszEuxkSX99YkkKeU+vTpk5cFifElcVuEGHMQV9DK4Sv+WMf0+o2J6feji1v8Ea/rlVdeybfDouPVebTElj/gZfFBj54Rdf8nT8ep83LLeIwpjtulRC8JrWhtV3y5irodPXp07e2Mon7j9+9+97tNnhfx+HHHHVe77t57783r6Zh1Hs4999z04x//ON1zzz315pWgY9Z73NbwpZdeqrfupJNOyhdbL7roonxrJDreZ33bbbdN119/fd6uHMTie3oEdWG8Y9b5jBkz5gnd5Qsy0YW91RUylVw7vx3SZpttVnriiSfyrRHi1kZ1b4cUt0NZZ5118uNlF1xwQb7t2c0331x69dVX84zr3bp1y7dHomPWeUNmWe/YdT5lypQ84/ZGG22UP9cxs355idl6aZu3SIlbnlx99dV5Zv0jjjgi3yJl/Pjx+fGvf/3rpR/+8If1bnvWqVOn0s9+9rN8C6xTTz3Vbc86eJ2fffbZ+U4Jf/jDH+p9pqdNm1bgu6Cl670hs6x3/DofO3ZsvoPCd7/73dKYMWNKf/rTn0p9+/YtnXXWWQW+C1qyzuP/4VHnN9xwQ+mNN94o/fWvfy2tueaa+Y4qRRDIF9H777+fv5gvs8wyOWQfeuih9f7nHPeyi/B1//3313teTLu/8sorl5ZaaqnS4MGD872N6dh1XpdA3rHrPH7G740tsS1t089//vPSKquskkNX3DIl7jtftuOOO+Yv4g1vZbf22mvn7TfYYIPSn//85wJKTWvV+aqrrtroZzq+yNGxP+t1CeTVUeePPvpovrAeoS5ugfbjH//YBfUOXOdz5swpnXbaaTmERyPpwIEDS9/5zndKH374YSFlrykV0i4PAAAA1c0s6wAAAFAAgRwAAAAKIJADAABAAQRyAAAAKIBADgAAAAUQyAEAAKAAAjkAAAAUQCAHAACAAgjkANBB3XbbbWmttdZKSy65ZDruuOMaXXf11Ven3r17L9J+d9ppp9r9AQDNJ5ADwHwccsghaejQofOsf+CBB1JNTU2aPHlyoUF23XXXTV27dk3jx4+f57EjjzwyfelLX0rvvPNOOvPMMxtdt//++6dXXnllkV7zlltuqd1fWG211dKFF1642O9l0qRJ6dvf/nZaZZVV8nvq379/GjJkSHrkkUcWe98A0BZ1KroAAEDzPPzww+njjz/OAfuaa65JP/jBD2of++ijj9LEiRNzoF1xxRWbXBe6d+++SK+73HLLpZYwbNiwNHv27Pxe1lhjjTRhwoQ0evTo9P7776eWEq/XpUuXFts/AMyPFnIAqIAIjQcccEBaaaWV0lJLLZU22mijdMMNN9RraX/wwQfTRRddlFvWY3nrrbfyYy+//HLac8890zLLLJP69euXvv71r6f//ve/C3zNK664Ih144IF5+yuvvLJe632PHj3yv3fZZZf8Wk2ta9hl/bTTTkubbrpp+u1vf5tbvnv16pW++tWvpmnTpjXa0h//fvvtt9OIESNq39f06dNTz5490x/+8Id65Y3u8ksvvXS9fZVFT4OHHnoonXPOOWnnnXdOq666avrsZz+bRo4cmb7whS/U2y5a+eM4devWLW244YbpT3/6U+3jf/zjH9MGG2yQW9ij/Oedd16914l10bp/8MEH5zIeccQRtRc3tt9++3xxYuDAgemYY47J7wMAWpJADgAVMHPmzLTFFlukP//5zzlgR9CLoPzkk0/mxyOIDx48OB1++OHpvffey0sEvwiYEZA322yz9PTTT6e77747twx/5Stfme/rRai9+eab09e+9rW02267pSlTpuRAG7bZZps0ZsyY2oAar9XUusa8/vrrOTxH0I0lLiScffbZTXZfX3nlldMZZ5xR+74idEeIv+qqq+ptG79Ha375wkBdcTEilnjdWbNmNfpac+fOzRcuogv7ddddl/75z3/mcsV4+PDMM8/k4xav/dJLL+WLCyeffHK+6FDXz372s7TJJpuk5557Lj8e73ePPfbILfQvvvhi+v3vf58D+ne/+9351gEALLYSANCk4cOHl5ZccsnS0ksvXW/p1q1bKf43+uGHHzb53L333rt0wgkn1P6+4447lo499th625x55pml3Xffvd66d955J+97zJgxTe77V7/6VWnTTTet/T32G2Uti3LFPu6///75rrvqqqtKvXr1qv391FNPLS211FKlqVOn1q77/ve/X9pqq62afB+rrrpq6YILLqhXvieeeCIft3HjxuXfJ0yYUOrUqVPpgQceaPI9/eEPfygtu+yy+dhus802pZEjR5ZeeOGF2sfvueee0hJLLNHkcTnwwANLu+22W711Ufb111+/XlmHDh1ab5vDDjusdMQRR9Rb99BDD+XX+vjjj5ssLwAsLi3kALAA0YX6+eefr7f85je/qbfNp59+mrtCR1f1GGMdrb333HNPGjt27Hz3/cILL6T777+/toU4lpioLUTLbVOii3q0jpfFv6PFvLHu4IsqunXXbcUeMGBAHnu+KKK7eXQdj/HgIVq0oxv6Djvs0ORzooV63Lhx6Y477sgt1tGlfvPNN69t4Y7jHq3xa6+9dqPP/9e//pW23Xbbeuvi91dffTXXT9mWW245Tx3Ea9StgxhnHy3yb7755iK9bwBYFCZ1A4AFiC7Ycauwut599916v//0pz/N3dJjtvEI5fGcGGcdk4bNT0y0ts8+++Sx0w1FEG5MdNV+/PHHc3f4uhO5Rei88cYbc7f4xdG5c+d6v8e48Aini+qb3/xmuuSSS9IPf/jD3F390EMPzfuanxgXHl3wY4nu5LGPU089NY/BX9TJ55oSddOwDmJceowbbyhmfAeAliKQA0AFxLjmfffdt7bVOgJs3E5s/fXXr90mZvOu21IbogU4xnRHq3SnTgv3v+WYzC1amiPs1hWhNx5b3EC+qBp7XyGOxYknnpguvvjifBFh+PDhi7zvOH4xrjxsvPHG+UJIHNfGWsnXW2+9eW6RFr/HtuVx5o2JOojyNbzoAgAtTZd1AKiAQYMGpXvvvTc9+uijuet0tLjG5Gx1Reh+4okn8uzqMYt6hPajjjoqffDBB3mG9qeeeip3U4+u7tGa3FjInTNnTp4BPbaPGcbrLtGaHPv/xz/+0Yrv/H/v6+9//3v6z3/+U292+GWXXTbtt99+6fvf/37afffdc3fz+c1SH5PbRdf2mFgtuopHF/xzzz03X+gIO+64Y74QEV3b41jHNnfddVeeCC+ccMIJ+TZpMXQgQnt0l//FL36Rvve97823/NHLIOotJnGLbvHRxf322283qRsALU4gB4AKOOmkk3JLa4w9jluB9e/fPw0dOrTeNhEMo6U2Wn379OmTx5fH/cCjFTfCd4TW6O4eXd3jVmRLLDHv/6ZjfHWE1y9+8YuNthDHEq3krSlmWI+LDGuuuWZ+X3Uddthhudv+N77xjfnuI8Ztb7XVVumCCy7IoTsuMESX9Wjtj1BdFr0JPvOZz+QLEnEcowW+fOEijv9NN92Uu+3H80855ZRctujuPj/R8h4zyUeIj1ufxYz38dy692oHgJZQEzO7tcieAYCqF635cY/ymKwturYDAP8/Y8gBgIqbMWNGvid53Cc8uu8L4wAwL13WAYCKi7Hfcfu26Lo/cuTIoosDAG2SLusAAABQAC3kAAAAUACBHAAAAAogkAMAAEABBHIAAAAogEAOAAAABRDIAQAAoAACOQAAABRAIAcAAIDU+v4/oHOwJXvruLsAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Statistics for hate affinity scores:\n", + "\n", + "Controversial:\n", + " Mean: 0.0796\n", + " Median: 0.0000\n", + " Std Dev: 0.1947\n", + " Min: -0.5694\n", + " Max: 0.7799\n", + "\n", + "Lex Fridman:\n", + " Mean: 0.0639\n", + " Median: 0.0594\n", + " Std Dev: 0.0754\n", + " Min: 0.0000\n", + " Max: 0.3032\n" + ] + } + ], + "id": "29443f0c" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/ph/f73k11ns3_v8n6g3lrdb792w0000gr/T/ipykernel_89229/3618054468.py:35: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", - " box = plt.boxplot(data, patch_artist=True, labels=platforms)\n" - ] + "cell_type": "code", + "metadata": {}, + "source": [ + "# Save segment-level scores to CSV files for further analysis\n", + "segment_scores_df.to_csv(\"data/controversial_segment_scores.csv\", index=False)\n", + "lex_segment_scores_df.to_csv(\"data/lex_fridman_segment_scores.csv\", index=False)\n", + "\n", + "print(\"Saved segment-level scores to CSV files:\")\n", + "print(\"- controversial_segment_scores.csv\")\n", + "print(\"- lex_fridman_segment_scores.csv\")\n", + "\n", + "# Count high risk segments in both datasets (score > 0.5)\n", + "controversial_high_risk = len(segment_scores_df[segment_scores_df['score'] > 0.5])\n", + "lex_high_risk = len(lex_segment_scores_df[lex_segment_scores_df['score'] > 0.5])\n", + "\n", + "print(f\"\\nHigh risk segments (score > 0.5):\")\n", + "print(f\"- Controversial: {controversial_high_risk} segments ({controversial_high_risk/len(segment_scores_df)*100:.2f}% of all segments)\")\n", + "print(f\"- Lex Fridman: {lex_high_risk} segments ({lex_high_risk/len(lex_segment_scores_df)*100:.2f}% of all segments)\")\n", + "\n", + "# Count medium risk segments (score between 0.1 and 0.5)\n", + "controversial_medium_risk = len(segment_scores_df[(segment_scores_df['score'] > 0.1) & (segment_scores_df['score'] <= 0.5)])\n", + "lex_medium_risk = len(lex_segment_scores_df[(lex_segment_scores_df['score'] > 0.1) & (lex_segment_scores_df['score'] <= 0.5)])\n", + "\n", + "print(f\"\\nMedium risk segments (0.1 < score <= 0.5):\")\n", + "print(f\"- Controversial: {controversial_medium_risk} segments ({controversial_medium_risk/len(segment_scores_df)*100:.2f}% of all segments)\")\n", + "print(f\"- Lex Fridman: {lex_medium_risk} segments ({lex_medium_risk/len(lex_segment_scores_df)*100:.2f}% of all segments)\")" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Saved segment-level scores to CSV files:\n", + "- controversial_segment_scores.csv\n", + "- lex_fridman_segment_scores.csv\n", + "\n", + "High risk segments (score > 0.5):\n", + "- Controversial: 0 segments (0.00% of all segments)\n", + "- Lex Fridman: 0 segments (0.00% of all segments)\n", + "\n", + "Medium risk segments (0.1 < score <= 0.5):\n", + "- Controversial: 236 segments (8.64% of all segments)\n", + "- Lex Fridman: 67 segments (0.94% of all segments)\n" + ] + } + ], + "id": "55dbe289" }, { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "cell_type": "code", + "metadata": {}, + "source": [ + "# Create segment-level visualizations and comparisons\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Save the completed segment dataframes to CSV files\n", + "segment_scores_df.to_csv(\"data/controversial_segment_scores.csv\", index=False)\n", + "lex_segment_scores_df.to_csv(\"data/lex_fridman_segment_scores.csv\", index=False)\n", + "\n", + "# Create dataframes with platform labels for combined analysis\n", + "controversial_segments = pd.DataFrame({\n", + " 'platform': ['Controversial'] * len(segment_scores_df),\n", + " 'score': segment_scores_df['score']\n", + "})\n", + "\n", + "lex_segments = pd.DataFrame({\n", + " 'platform': ['Lex Fridman'] * len(lex_segment_scores_df),\n", + " 'score': lex_segment_scores_df['score']\n", + "})\n", + "\n", + "# Combine segment scores\n", + "all_segment_scores = pd.concat([controversial_segments, lex_segments], ignore_index=True)\n", + "all_segment_scores.to_csv(\"data/all_segment_scores.csv\", index=False)\n", + "print(\"Saved all segment-level data to CSV files\")\n", + "\n", + "# Create a boxplot comparison for segment-level scores\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Extract data for each platform\n", + "platforms = all_segment_scores['platform'].unique()\n", + "segment_data = [all_segment_scores[all_segment_scores['platform'] == platform]['score'] for platform in platforms]\n", + "\n", + "# Create box plot\n", + "box = plt.boxplot(segment_data, patch_artist=True, labels=platforms)\n", + "colors = ['#ff9999', '#66b3ff']\n", + "for patch, color in zip(box['boxes'], colors):\n", + " patch.set_facecolor(color)\n", + "\n", + "plt.title('Segment-Level Hate Speech Score Comparison')\n", + "plt.ylabel('Hate Speech Score')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.show()\n", + "\n", + "# Create histograms to compare segment-level score distributions\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Extract data for each platform\n", + "for i, platform in enumerate(platforms):\n", + " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", + " # Plot histogram with log scale for y-axis to better see the distribution tail\n", + " plt.hist(platform_data, bins=50, alpha=0.6, label=platform)\n", + "\n", + "plt.title('Distribution of Segment-Level Hate Speech Scores')\n", + "plt.xlabel('Hate Speech Score')\n", + "plt.ylabel('Frequency')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Create a second histogram with log scale for better comparison of tails\n", + "plt.figure(figsize=(12, 6))\n", + "for i, platform in enumerate(platforms):\n", + " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", + " # Plot histogram with log scale for y-axis\n", + " plt.hist(platform_data, bins=50, alpha=0.6, label=platform)\n", + "\n", + "plt.title('Distribution of Segment-Level Hate Speech Scores (Log Scale)')\n", + "plt.xlabel('Hate Speech Score')\n", + "plt.ylabel('Frequency (log scale)')\n", + "plt.yscale('log')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Create cumulative distribution function (CDF) plot\n", + "plt.figure(figsize=(12, 6))\n", + "for i, platform in enumerate(platforms):\n", + " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", + " # Sort the data\n", + " sorted_data = np.sort(platform_data)\n", + " # Get the cumulative probabilities\n", + " p = 1. * np.arange(len(sorted_data)) / (len(sorted_data) - 1)\n", + " # Plot the CDF\n", + " plt.plot(sorted_data, p, label=platform, color=colors[i], linewidth=2)\n", + "\n", + "plt.title('Cumulative Distribution of Segment-Level Hate Speech Scores')\n", + "plt.xlabel('Hate Speech Score')\n", + "plt.ylabel('Cumulative Probability')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Print detailed statistics for segment-level scores\n", + "print(\"\\nSegment-level statistics for hate speech scores:\")\n", + "for platform in all_segment_scores['platform'].unique():\n", + " platform_scores = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", + " print(f\"\\n{platform}:\")\n", + " print(f\" Count: {len(platform_scores)}\")\n", + " print(f\" Mean: {platform_scores.mean():.4f}\")\n", + " print(f\" Median: {platform_scores.median():.4f}\")\n", + " print(f\" Std Dev: {platform_scores.std():.4f}\")\n", + " print(f\" Min: {platform_scores.min():.4f}\")\n", + " print(f\" Max: {platform_scores.max():.4f}\")\n", + " print(f\" 25th Percentile: {platform_scores.quantile(0.25):.4f}\")\n", + " print(f\" 75th Percentile: {platform_scores.quantile(0.75):.4f}\")\n", + " print(f\" 90th Percentile: {platform_scores.quantile(0.9):.4f}\")\n", + " print(f\" 95th Percentile: {platform_scores.quantile(0.95):.4f}\")\n", + " print(f\" 99th Percentile: {platform_scores.quantile(0.99):.4f}\")\n", + " \n", + " # Count high and medium risk segments\n", + " high_risk = len(platform_scores[platform_scores > 0.5])\n", + " high_risk_pct = high_risk / len(platform_scores) * 100\n", + " medium_risk = len(platform_scores[(platform_scores > 0.1) & (platform_scores <= 0.5)])\n", + " medium_risk_pct = medium_risk / len(platform_scores) * 100\n", + " \n", + " print(f\" High risk segments (> 0.5): {high_risk} ({high_risk_pct:.2f}%)\")\n", + " print(f\" Medium risk segments (0.1-0.5): {medium_risk} ({medium_risk_pct:.2f}%)\")\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Saved all segment-level data to CSV files\n" + ] + }, + { + "output_type": "stream", + "text": [ + "/var/folders/ph/f73k11ns3_v8n6g3lrdb792w0000gr/T/ipykernel_89229/3057986402.py:33: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", + " box = plt.boxplot(segment_data, patch_artist=True, labels=platforms)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "image/png": 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", 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Ngc6Igk6Prr/mSGtEiHpY9R6oB1nJGEOP17BhQ7cMZkbSNpTkBfXIK++AepSVp0IjNFQuzYPPymge7aPPo84zo/clO7kS1CiowF83XUuN4FDve1aWngwtT1brU15c/0h9L2hUjnrvlf9BvfPKfaFgP6+k/Z72rqPm7WcUtIfmBVEOBI2k0AgIjbBQ/oZhw4a5fAZNmjTJszID8CeCfQCIAP2IEw1tPRr1aKlHXzf9KH7iiSdc77EaANR7eLREUjnh9SiF/khW0qvQgE89ZRktJ6fesNBhq9kpm35cK0u8pjWE9u4r67X3eCToOOod1o/p0N79SL+O16uq66ZrqiG46sHUualRJKOe37Tl/P777931D72OoVnvIyE7ZcqOzN57DU8OdbSkaWlppINGw2SlnEpOp4YFNY4poZkCn7TZ0nU89WTrsxXpz1FWqLdZvcfq2dcQ9cw+g5JZ+XQOqiPqGQ5t7Mgtb7lQjVIJlVHZlExPGfK9Hvqs1qesXn/tp8+rPg9qEMlP+q7T6yuBoXjfb979jOg66HqsWbMm3WP6ntH3zrEaMrwpShr5lZX6rv3Vu6+b3iNdp5EjR4at9gIAWcGcfQDIJQUeQ4YMcT/QlcU8Mxrim5b3Y9ebQ+qtxxyptdyVjTo0j4CWb9MPfmX0D/1hqUzhoWtPa535tEt9Zadsl156qQsQNGQ2lIYTKxAIff3c0OtoKHnofO7Dhw+77OXqBVW29+zSj2tlvk5L56350AoYFACo11G9/ZrTnFGwoOG/HjUCKet86Lxpzel++eWXLZKyU6bs0Huf0TB0BS6ht7Q9/cfqBdf1VO9lRtda76NHAZWGoStDuhrW9FjoEH7veLrGGV1TrdrgZUXPK14vdNpeZ60qkFZmnyVN69Fx1GCQ9ji6H7qEX0bUaJhRr7eXoyPtUHRd/9BcAvrMK6eIMuKrHNmpT1m9/hqZofdTWfjTjnaI1EgeNTpktIKCGjDVyOBdB32O1ZCkLP9pP/NeWXQNdD10XUKnP2mVAk0tUeNT2qX80tLnX/uocVfTTzK7jpouoO+FUPp+VoNLfi0fCcBf6NkHgGzQ8Fr15ijY0I89Bfrq3VTPrQI5JV3LjH7cahi/1lbW/prDqqWuNCdYPxi9H3ZKxDZu3Dj3A09BgRI4pZ0DmlVao1vH1hBelVeBh6YahCaGUw4BNQJoqLF+sGtOrXqQQhPmZbdsGobasmVL1/uqH8gabq2pCvrBrGGqaY+dU1oqS8O7NXR82bJlbmkqnYuWM9S5ps0ZkNVAQYm31CCh5GC6hgpiJk+ebBs3bnTH9QI7LVunAEvXQddU85DVqKMASiMbvAYeLS2nhg8l6NLSewqKNZTYqy+R7InOapmyQ0nc1KDSp08fNyxcDSl6j49FQaI3yiKUhoRrqLs+M5qT7C2npoBQy7XpPVS98ZalEwX3asTRMHQNF/fyMoQuf6nh/VqWUOevZRPV4KTX13Y1Kng93HlBwZwCR80RV0Cn/Bqq8+vXr0+3r5cUT58PTVHQqBFdT30uNLxcyw/q/BUYqw7rGJp7r/quJRIzo+RzChg1XF3THNSAp2kFeu/02Uib3FNz1RWIhi69J2psyG59yur11/ePzlsNpPp8qYFDr71kyRKX50ND1nNL38mqJ0pUqHnvqq+an6+gXkGzlv3zaJlPfUc2bdrUXV99n+naa6qVchyI3hMdU/tpWT0Nu9f3jo6l9zsrdUPL7Oka6XX0nquhQQ0Meh1dK30/aFSFRkboe1jXWa+j913f3aEJOwEgyyKS0x8AfM5bUsm7aRmmypUrBy6++GK3VFnoEm+ZLb03Z86cQMeOHQNVq1Z1z9dfLeuWdumxDz/8MFC/fn231Fbosmpakq1BgwYZli+zpfe07Fe/fv0CSUlJgWLFirkl1DJaQk5Lw2mZPi1rde6557rluNIe82hlS7v0nrfcVO/evd15FipUKFC7dm23pFToklai4/Ts2TNdmbK6PJqW07r55psDFSpUcNe1YcOGGS4PmNWl93S8J5980p27lhzTuZYrVy7QqlWrwDvvvJPh/ip/9erV3XmqXrRu3Trw0ksvhe23bt069/p6HypWrOiWD3v33Xfd+Ycup5fZ+5xZ+TO6flkpU2bL42k5uLTL+e3evTtw3XXXueXDvOXMjsY7dmY3b3k21RHVz1q1arn3Tu/hP/7xj8DTTz+dbrk41RudT0ZLOnr0nKeeespdP9VlvW/NmjULDBo0KLBz584cLb2XUd3MbDnOP/74wy0pp+ukJdWuuuoqt0Sb9tP3QdqlB/WZ0zJ0aZfhU73Q8m1amlA3LdGocqxZs+ao5f30008Dt9xyi9tfS0/qmura3nvvvemWnfPOTctW6rOp66WlQ0OXgcxuHc/q9ZcJEya41/P2U72fPXv2Met7Rt9LaemzNmDAgMA555zjvvv0GdZnTsf7/PPP0+2/atWq4PumpQe1bN+jjz4ats8333wTaNeunbuuxYsXD7Rs2TKwYMGCbC3RqmurY6hu6HVOPfVUtyygt/yhljfUddb7p/dd+zVv3twtmQoAOZGg/2S9aQAAAESKRgn07t3bJXhUTzCQXzSaREtKpp1qAwDwD+bsAwCQDzRvOZTm5moosJY/I9AHAACRxpx9AADygeYma91wJWVUsjvlRdB8Zs3dBwAAiDSCfQAA8oESoY0fP94F90pcpgRcb775Zrqs8gAAAJHAnH0AAAAAAHyGOfsAAAAAAPgMwT4AAAAAAD7DnP0sSE1NtY0bN1qpUqXcUjUAAAAAAOQlzbjftWuXVa1a1RITs99PT7CfBQr0q1evHu1iAAAAAACOM7///rtVq1Yt288j2M8C9eh7F7l06dIW66MQtm7dahUrVsxR6w8QLdRdxDPqL+IVdRfxjPoLv9fdlJQU1+nsxaPZRbCfBd7QfQX68RDs79+/35WTLz3EE+ou4hn1F/GKuot4Rv3F8VJ3E3I4lZxPBQAAAAAAPkOwDwAAAACAzxDsAwAAAADgM8zZBwAAAIAILpd2+PBhO3LkSLSLghies3/o0CE3b79IkSJWoECBPHkdgn0AAAAAiICDBw/apk2bbO/evdEuCmK8QUgB/65du1yCPi2rV7JkyYi/DsE+AAAAAOSSgrf169e7XtqqVata4cKFc5xFHcfH6I8CBQrYX3/9ZX/88YfVrl074j38BPsAAAAAEIFefQX8Whe9ePHi0S4O4iDYL1iwoFWsWNF+/fVXN6w/0sE+CfoAAAAAIEKysm464MnL0R/URAAAAAAAfIZgHwAAAAAAn2HOPgAAAADkoX7vrcy31xrWuWG+vdbx6KabbrIdO3bYBx98kKX9NR+/Zs2a9u2331rjxo0tP9GzDwAAAADHuc2bN9u9995rp5xyilv7XYkGL7/8cpszZ07EXuOiiy6yXr16WTwbPXq0TZo0yeIBPfsAAAAAcBxT7/O5555rZcuWtREjRljDhg1ddviZM2daz5497ccff8zXTPVHjhxxmeqjsaJC4cKFj7pPmTJlLF7Qsw8AAAAAx7G7777bZYVfvHixdenSxerUqWMNGjSwPn362KJFi9w+GzZssI4dO1rJkiWtdOnSdvXVV9uWLVuCxxg4cKAbpv7f//7XTj75ZBcUd+3a1Xbt2hUc/j5//nzXM67X0k2NDPPmzXP//vTTT61Zs2ZuVMGXX35pBw4csPvuu8+SkpKsaNGidt5559mSJUvcsbTEYbVq1Wzs2LFh56Gh8loN4bfffnP3Ndz+1ltvdcvbqcytWrWyFStWpCvz+PHj3VB7vY688847rsGjWLFidsIJJ1ibNm1sz549wfPo1KlT8BgzZsxwZVNDifa97LLL7JdffrFYQLAPAAAAAMepbdu2uYBVPfglSpRI97iCWAXXCvS1rwL22bNn27p16+yaa64J21dBruayT5s2zd2075NPPukeU5DfokULu+2222zTpk3upqkCnkceecTt+8MPP1ijRo2sb9++9u6779rkyZPtm2++sVq1alm7du1cGRTQX3vttTZlypSw13/99dfdCIUaNWq4+1dddZUlJye7hoRly5ZZ06ZNrXXr1u4Ynp9//tm9znvvvWfLly935dKxb7nlFlcWNUZ07tzZjTjIiBoB1CiydOlSN+VBZbvyyivdNYs2hvEDAAAAwHFKwa4C2dNOOy3TfRTErly50tavXx8M0F999VXX+6/e9rPOOsttU4Cr+eylSpVy92+88Ub33Mcff9z19GuIfPHixa1y5crpXmPw4MF28cUXBwNo9drrWJdcconb9vLLL7tGhldeecUeeughu/76623kyJFuxMFJJ53kXvvNN9+0/v37u/2//PJLN1JBwb5GC8jTTz/tGiPUc3/77bcHh+7rXNT7L2pYOHz4sAvwvUYD9fJnRiMhQk2YMMEd6/vvv7fTTz/doomefQAAAAA4TmXWYx1KPdwK8kN74uvXr+96/fWYR8P3vUBfqlSp4oLtrDjzzDPDRggoZ4B66T2FChWys88+O/h6Gn5fr169YO++RhHotdSbLytWrLDdu3e7ofWaeuDd1GAROsxeAb0X6MsZZ5zhev8V4OtYamTYvn17puVeu3atGwmgxIaaKqBrIGqEiDZ69gEAAADgOFW7dm03Zz4SSfgUkIfScbM6nD2jKQTHot59BfuaAqC/7du3d8G97N692zU2aBh+WmqkyOx1CxQo4EYQLFiwwGbNmmXPPfec/fvf/7avv/7azetPSysWqMFAjQJVq1Z156sefY0YiDZ69gEAAADgOFW+fHk3F/6FF14IJqELpSR36kH//fff3c2jYep6TD38WaVh/Mq0fyynnnqq2/err74KblNPv6YMhL7eddddZ6tWrXLz8TU0X8G/p2nTpm45QWX113z/0FuFChWO+vpqpNCogkGDBrmkfyrL+++/n26/v//+29asWeOmDmg0gK7T0UYB5Dd69v1o8UtKtWGWcOwhOZm6fHQkSwQAAAAgRinQV3CrYfKaO68EeZq3rh5uzZ1XYK9h7QqmR40a5R5TBv8LL7wwbPj9sWiIu3rIlYVfQ+rV0JAR9bbfddddbm6+9tGc/OHDh9vevXutR48eYcf7xz/+4bapEeGKK64IPtamTRuXEFCZ8/VcrTCwceNGmz59ukugl1m5VT7lGWjbtq1bCUD3t27d6gL5tMqVK+dGErz00ktuFIGG7muUQayIarCvN8dbFiGUKo4q3P79++2BBx5wiRa09IJanF588UWrVKlScF9dUFWEuXPnugrTvXt3GzZsWNi6jBq6oQyJq1evdvNM1PKiJRMAAAAAIK8N65x5grdYoPnmSkynRHqKv5SRXvPYtRSegn31dH/44Yd277332gUXXOAyzmvIvIa4Z8eDDz7o4jX1zu/bt8/Nn8+MMvNrSLyS/Gn5PgXnM2fOdAF2KDVAKH7s1q2bWyrPk5CQYJ988okbgn/zzTe7gF2JAVX+0HgyLc27/+KLL1yjRkpKihuir0SAXqLAULoOilW1RKCG7tetW9fGjBljF110kcWChEBWMjLkEV3w0GEcGoKhDIwK3HWBFMSr5UVZGJW98Z577nEX1BvOoecqMYPetBEjRrhKqTdZyzk88cQTbh9VIF34O++8062xqFaaXr16ueOq8SAr9Cbr9Xfu3One/FimD0TytKGWZNsskZ59xBFXd5OTXQuqPudAPKH+Il5RdxHPYq3+qqNSsUfoeu1ARhSCa3SEOqjVqZ1ZvcltHBrVnv3QrIde643mZ2g4iE5Iyyoo0UKrVq3c4xMnTnTDJxYtWmTnnHOOS5igISWfffaZa51R4D9kyBB7+OGHbeDAgW5uxbhx49yFU2uM6PlahuHZZ5/NcrAPAAAAAEA8iZk5+8pW+Nprr7nh9hpyoSQLSsKguRYerf2o+RoLFy50wb7+au5I6DAMBfAaEaAh+02aNHH7hB7D20e9+5lR64puoS0qXuthVrNJRovKp7EaqZaQ2wNFqkhANupuIOY/Y0BGqL+IV9RdxLNYq79eebwbcDReHfHqS0axZm7rdswE+x988IHL5ujNpVfmRPXMhy6LIArs9Zi3T9r5Ft79Y+2jAF7zRELndXg051+ZFzOadqDhObFMFWKnlbaAJVii5eJLJovrYQIRrbs7d7ovu1gYigdkB/UX8Yq6i3gWa/VXHZUqk4Zn6wZkRnXWm86uuqJ6o8z+aZcuVK4CXwT7GrKvpAdamzDa+vXr50YYeNQwoMR+mnYQD3P2EyzFKtr23AX7SUmRLBaQtbqbkOA+Z7HwP2wgO6i/iFfUXcSzWKu/6hRUcKZ52KHJwoHMKLhX0K/6q6z+aefs5zb3Q0zUQmXk17z79957L7hNSfc0tF+9/aG9+1u2bHGPefssXrw47Fh63HvM++ttC91HQXtGvfpSpEgRd0tLb0IsfJEcS0KCuUA/Vwn64uA84T/6H3a8fM6AtKi/iFfUXcSzWKq/KoPK492Ao/Xse3XEqy8Z1ePc1uvofyr+X+I9ZdHs0KFDcJuWeVBLh7Lne9asWeOW2tN6iaK/K1eudFk4PVoLUoG8lnPw9gk9hrePdwwAAAAAAPwmMRaG3yjY13qLocNdtMRAjx493HB6LcWnhH1aH1FBupLzSdu2bV1Qr7UXV6xY4dZd7N+/v/Xs2TPYM68l99atW2d9+/a1H3/80V588UWbOnWq9e7dO2rnDAAAAABAXor6MH4N31dv/S233JLuMS2Pp6ELXbp0cdnxlUVfwbqnQIECNm3aNJd9X40AJUqUcI0GgwcPDu6jZfemT5/ugvvRo0dbtWrVbPz48Sy7BwAAAADwragH++qdz2xpCiUkeOGFF9wtMzVq1LBPPvnkqK9x0UUX2bfffpvrsgIAAAAAEA+iHuwDAAAAgK99fH/+vdblo81vtDy7ErdrufajdfA2btzYRo0ala9li2VRn7MPAAAAAIheIN2pU6d8e7158+aFrVrg3ZR7LTOajj1p0qR8K6Nf0LMPAAAAAMhXWmlNq6h5SpYsmW4frUGvhgAlb0f20bMPAAAAAMjQqlWr7JJLLnHBeKVKldxKaH/99Vewl75w4cL2v//9L7j/8OHD3bLqW7ZsOepxtU/lypWDNx1fvfdly5a1jz76yK26phXWlMw97eiDPXv2WLdu3dxzqlSpYiNHjkx3/JNPPtmGDh0a3E+53nTcrVu3WseOHd22Ro0a2dKlS4PP+fvvv+3aa6+1E0880YoXL24NGza0N954I910gfvuu8+t9la+fHlX9oEDB1osItgHAAAAAKSjefKtWrWyJk2auKB4xowZLoi/+uqrg4Fvr169XAPAzp07XVL0Rx991K1+poaBnNi7d6899dRT7hirV692jQJpPfTQQzZ//nz78MMPbdasWa7R4Ztvvslwdbdzzz3XlatDhw6unAr+b7jhBrf/qaee6u57CeP3799vzZo1c6u5qZHj9ttvd89ZvHhx2HEnT57sVoL7+uuvXeOGVoObPXu2xRqG8QMAAAAA0nn++eddoP/EE08Et02YMMGqV69uP/30k9WpU8f1nivQVWCsAFlLoV9xxRXHPLaWRA/122+/ub+HDh1yy62fccYZGT5v9+7d9sorr9hrr71mrVu3DgbfaY8nl156qd1xxx3u3wMGDLCxY8faWWedZVdddZXb9vDDD7sl3NWAoR569eg/+OCD5rn33ntt5syZNnXqVDv77LOD2zUi4LHHHnP/rl27trtOc+bMsYsvvthiCcE+AAAAACCdFStW2Ny5czOcT//LL7+4YF/D+F9//XUXAGuovHrTs0JD/0uVKhW8X65cOfdXx9OxMqPXPXjwoDVv3jy4TcPp69atm27fRiHH8UYaaGh+2m3Jycku2FeOADVsKLj/888/3escOHDADenP7LiiqQQ6Rqwh2AcAAAAAZNiLfvnll7th9WkpwPUsWLDA/d22bZu7aYj7sdSsWdPNz0+rWLFiLilfJBQqVCj4b++YGW1LTU11f0eMGOEy/2v5PjUK6Dw0TUFBf2bH9Y7jHSOWMGcfAAAAAJBO06ZN3bx5JburVatW2M0L6NXT3rt3b3v55Zddb7uG8edl4Kt59gq2NV/es337djetILe++uorl7xPc/o1jeCUU06JyHGjhWAfAAAAAI5jSq63fPnysNvvv/9uPXv2dD31ylC/ZMkSF9hrDvvNN9/shrzrpsC4Xbt2btvEiRPtu+++yzA7fqRoSkGPHj1ckr7PP//c5QlQtv7ExNyHtrVr13b5BzRS4YcffnDz/Y+1qkAsYxg/AAAAAOSly0dbLFM2eyXiC6WAWhnx1dutRHZt27Z189c1L799+/YuuB4yZIhLrDdt2rTg0P6XXnrJNQ5o/8yS7OWWhtt7Uww07/+BBx5wDRa51b9/f1u3bp1rvNA8fSUd1JJ/kTh2NCQEvHUGkKmUlBQrU6aMe5NLly5tsUxDZpKnDbUk22aJCQHffiHBf1zdTU52y6tEomUWyE/UX8Qr6i7iWazVXy3btn79ejcXvWjRotEuDmKYQvDDhw9bwYIFXQNKZvUmt3Fo9D8VAAAAAAAgogj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAgAgh/zlipb4Q7AMAAABALhUqVMj93bt3b7SLgjhy8OBB97dAgQIRP3bBiB8RAAAAAI4zCtbKli3rlgMUrdOekJAQ7WIhhpfe05KRW7dudXVFy/BFGsE+AAAAAERA5cqV3V8v4AcyC/ZTU1NdsK9GopNOOilPGoYI9gEAAAAgAhSwValSxZKSkuzQoUPRLg5ilAL9v//+2ypUqGBFihRxQX9eINgHAAAAgAhSb21ezMGGf4L9QoUK5WmgLyToAwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnoh7s//nnn3bDDTfYCSecYMWKFbOGDRva0qVLg48HAgEbMGCAValSxT3epk0bW7t2bdgxtm3bZtdff72VLl3aypYtaz169LDdu3eH7fPdd9/Z+eefb0WLFrXq1avb8OHD8+0cAQAAAAA4boL97du327nnnmuFChWyTz/91L7//nsbOXKklStXLriPgvIxY8bYuHHj7Ouvv7YSJUpYu3btbP/+/cF9FOivXr3aZs+ebdOmTbMvvvjCbr/99uDjKSkp1rZtW6tRo4YtW7bMRowYYQMHDrSXXnop388ZAAAAAIC8VtCi6KmnnnK97BMnTgxuq1mzZliv/qhRo6x///7WsWNHt+3VV1+1SpUq2QcffGBdu3a1H374wWbMmGFLliyxM8880+3z3HPP2aWXXmpPP/20Va1a1V5//XU7ePCgTZgwwQoXLmwNGjSw5cuX2zPPPBPWKAAAAAAAgB9ENdj/6KOPXC/9VVddZfPnz7cTTzzR7r77brvtttvc4+vXr7fNmze7ofueMmXKWPPmzW3hwoUu2NdfDd33An3R/omJiW4kwJVXXun2ueCCC1yg79HrqrFBowtCRxLIgQMH3C10ZICkpqa6WyxT+QIBs1RLyO2BIlUkIBt1NxDznzEgI9RfxCvqLuIZ9Rd+r7upuazbUQ32161bZ2PHjrU+ffrYv/71L9c7f99997mgvHv37i7QF/Xkh9J97zH9TUpKCnu8YMGCVr58+bB9QkcMhB5Tj6UN9ocNG2aDBg1KV96tW7eGTR+IRaoQO620BSzBEi2Q8wMlJ0eyWEDW6u7One6LT411QDyh/iJeUXcRz6i/8Hvd3bVrV/wG+zpJ9cg/8cQT7n6TJk1s1apVbn6+gv1o6devn2uACO3Z13SDihUruiSAsUzXNMFSrKJtz12wn6YBBciXupuQ4D5n/A8b8Yb6i3hF3UU8o/7C73W3aNGi8RvsK8N+/fr1w7bVq1fP3n33XffvypUru79btmxx+3p0v3HjxsF9ktP0Qh8+fNhl6Peer796TijvvrdPqCJFirhbWnoj4uGLJCFBmRcDlpiQi2A/Ds4T/qMvvXj5nAFpUX8Rr6i7iGfUX/i57ibmsl5H9VOhTPxr1qwJ2/bTTz+5rPmiofcKxufMmRPWy665+C1atHD39XfHjh0uy77n888/d60lmtvv7aMM/YcOHQruo8z9devWTTeEHwAAAACAeBfVYL937962aNEiN4z/559/tilTprjl8Hr27Bls7ejVq5cNHTrUJfNbuXKldevWzWXY79SpU3AkQPv27V1Sv8WLF9tXX31l99xzj0vep/3kuuuuc3kAevTo4Zboe+utt2z06NFhQ/UBAAAAAPCLqA7jP+uss+z99993c+QHDx7sevK11N71118f3Kdv3762Z88et0SeevDPO+88t9Re6PwFLa2nAL9169ZuqEOXLl1szJgxYRn8Z82a5RoRmjVrZhUqVLABAwaw7B4AAAAAwJcSAkoBiKPS1AE1GChjYjwk6EueNtSSbFvu5uxfPjqSxQKyVneTk93qGsy7Q7yh/iJeUXcRz6i/8HvdTcllHMqnAgAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwmagG+wMHDrSEhISw22mnnRZ8fP/+/dazZ0874YQTrGTJktalSxfbsmVL2DE2bNhgHTp0sOLFi1tSUpI99NBDdvjw4bB95s2bZ02bNrUiRYpYrVq1bNKkSfl2jgAAAAAAHHc9+w0aNLBNmzYFb19++WXwsd69e9vHH39sb7/9ts2fP982btxonTt3Dj5+5MgRF+gfPHjQFixYYJMnT3aB/IABA4L7rF+/3u3TsmVLW758ufXq1ctuvfVWmzlzZr6fKwAAAAAA+aFg1AtQsKBVrlw53fadO3faK6+8YlOmTLFWrVq5bRMnTrR69erZokWL7JxzzrFZs2bZ999/b5999plVqlTJGjdubEOGDLGHH37YjRooXLiwjRs3zmrWrGkjR450x9Dz1aDw7LPPWrt27fL9fAEAAAAA8H2wv3btWqtataoVLVrUWrRoYcOGDbOTTjrJli1bZocOHbI2bdoE99UQfz22cOFCF+zrb8OGDV2g71EAf9ddd9nq1autSZMmbp/QY3j7qIc/MwcOHHA3T0pKivubmprqbrFM5QsEzFItIbcHilSRgGzU3UDMf8aAjFB/Ea+ou4hn1F/4ve6m5rJuRzXYb968uRt2X7duXTeEf9CgQXb++efbqlWrbPPmza5nvmzZsmHPUWCvx0R/QwN973HvsaPtowB+3759VqxYsXTlUoODypLW1q1bXR6BWKYKsdNKW8ASLNECOT9QcnIkiwVkre7u3Om++BIToz7DCMgW6i/iFXUX8Yz6C7/X3V27dsVvsH/JJZcE/92oUSMX/NeoUcOmTp2aYRCeX/r162d9+vQJ3lfDQPXq1a1ixYpWunRpi/WKk2ApVtG25y7YT0qKZLGArNXdhAT3OeN/2Ig31F/EK+ou4hn1F36vu0WLFo3vYfyh1Itfp04d+/nnn+3iiy92ifd27NgR1ruvbPzeHH/9Xbx4cdgxvGz9ofukzeCv+wraM2tQUNZ+3dLSGxEPXyQJCcq8GLDEhFwE+3FwnvAffenFy+cMSIv6i3hF3UU8o/7Cz3U3MZf1OqY+Fbt377ZffvnFqlSpYs2aNbNChQrZnDlzgo+vWbPGLbWnuf2ivytXrrTkkCHns2fPdoF8/fr1g/uEHsPbxzsGAAAAAAB+E9Vg/8EHH3RL6v36669u6bwrr7zSChQoYNdee62VKVPGevTo4YbTz5071yXsu/nmm12QruR80rZtWxfU33jjjbZixQq3nF7//v2tZ8+ewZ75O++809atW2d9+/a1H3/80V588UU3TUDL+gEAAAAA4EdRHcb/xx9/uMD+77//dvMVzjvvPLesnv4tWh5PQxe6dOnisuMri76CdY8aBqZNm+ay76sRoESJEta9e3cbPHhwcB8tuzd9+nQX3I8ePdqqVatm48ePZ9k9AAAAAIBvJQSUAhBHpQR9GmmgjInxkKAvedpQS7JtuZuzf/noSBYLyFrdTU62pKQk5t0h7lB/Ea+ou4hn1F/4ve6m5DIO5VMBAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD6To2B/3bp1kS8JAAAAAACIXrBfq1Yta9mypb322mu2f//+yJQEAAAAAABEL9j/5ptvrFGjRtanTx+rXLmy3XHHHbZ48eLIlAgAAAAAAOR/sN+4cWMbPXq0bdy40SZMmGCbNm2y8847z04//XR75plnbOvWrbkrFQAAAAAAiE6CvoIFC1rnzp3t7bfftqeeesp+/vlne/DBB6169erWrVs31wgAAAAAAADiKNhfunSp3X333ValShXXo69A/5dffrHZs2e7Xv+OHTtGrqQAAAAAACBLCloOKLCfOHGirVmzxi699FJ79dVX3d/ExP9rO6hZs6ZNmjTJTj755JwcHgAAAAAA5HewP3bsWLvlllvspptucr36GUlKSrJXXnklN2UDAAAAAAD5FeyvXbv2mPsULlzYunfvnpPDAwAAAACA/J6zryH8SsqXlrZNnjw5N+UBAAAAAADRCPaHDRtmFSpUyHDo/hNPPJHbMgEAAAAAgPwO9jds2OCS8KVVo0YN91hOPPnkk5aQkGC9evUKbtu/f7/17NnTTjjhBCtZsqR16dLFtmzZkq4sHTp0sOLFi7vGhoceesgOHz4cts+8efOsadOmVqRIEatVq5ZLHggAAAAAgF/lKNhXUP3dd9+l275ixQoXmGfXkiVL7D//+Y81atQobHvv3r3t448/dtMD5s+f75bz69y5c/DxI0eOuED/4MGDtmDBAjeFQIH8gAEDgvusX7/e7dOyZUtbvny5a0y49dZbbebMmdkuJwAAAAAAvg32r732Wrvvvvts7ty5LuDW7fPPP7f777/funbtmq1j7d69266//np7+eWXrVy5csHtO3fudNn8tcxfq1atrFmzZi5XgIL6RYsWuX1mzZpl33//vb322mvWuHFju+SSS2zIkCH2wgsvuAYAGTdunBuFMHLkSKtXr57dc8899s9//tOeffbZnJw6AAAAAAD+zMavgPrXX3+11q1bW8GC/3eI1NRU69atW7bn7GuYvnre27RpY0OHDg1uX7ZsmR06dMht95x22ml20kkn2cKFC+2cc85xfxs2bGiVKlUK7tOuXTu76667bPXq1dakSRO3T+gxvH1CpwukdeDAAXfzpKSkBM9Rt1im8gUCZqmWkNsDRapIQDbqbiDmP2NARqi/iFfUXcQz6i/8XndTc1m3cxTsa1m9t956ywX9GrpfrFgxF3Rrzn52vPnmm/bNN9+4Yfxpbd682b1O2bJlw7YrsNdj3j6hgb73uPfY0fZRAL9v3z5X9owSEA4aNCjd9q1bt7o8ArFMFWKnlbaAJViiBXJ+oOTkSBYLyFrd3bnTffElJuZo0BEQNdRfxCvqLuIZ9Rd+r7u7du3K/2DfU6dOHXfLid9//90N+589e7YVLVrUYkm/fv2sT58+wftqGKhevbpVrFjRSpcubbFecRIsxSra9twF+0lJkSwWkLW6m5DgPmf8DxvxhvqLeEXdRTyj/sLvdbdoLuPkHAX7mqOvRHhz5syx5OTkdMMLNH//WDRMX89VlvzQ437xxRf2/PPPuwR6mne/Y8eOsN59ZeOvXLmy+7f+Ll68OOy4Xrb+0H3SZvDXfQXtGfXqi7L265aW3oh4+CJJSFAyhoAlJuQi2I+D84T/6EsvXj5nQFrUX8Qr6i7iGfUXfq67ibms1zkK9tUjr2Bfc+1PP/10V9Ds0nz/lStXhm27+eab3bz8hx9+2PWkFypUyDUoaMk9WbNmjVtqr0WLFu6+/j7++OOu0UArBIhGCiiQr1+/fnCfTz75JOx1tI93DAAAAAAA/CZHwb7m2k+dOtUuvfTSHL9wqVKlXENBqBIlSril+7ztPXr0cMPpy5cv7wL4e++91wXpSs4nbdu2dUH9jTfeaMOHD3fz8/v37++S/nk983feeacbKdC3b1+75ZZb3KgDlX369Ok5LjsAAAAAALEsxwn6atWqZXlNy+Np6IJ69pUdX1n0X3zxxeDjBQoUsGnTprns+2oEUGNB9+7dbfDgwcF9tOyeAvvevXvb6NGjrVq1ajZ+/Hh3LAAAAAAA/CghoBSA2aQ169etW+d6zHMyhD/eKEFfmTJlXMbEeEjQlzxtqCXZttzN2b98dCSLBWSt7v6/KTnMu0O8of4iXlF3Ec+ov/B73U3JZRyao579L7/80ubOnWuffvqpNWjQwM2tD/Xee+/l5LAAAAAAACACchTsKzv+lVdeGYnXBwAAAAAAsRDsT5w4MdLlAAAAAAAAEZLjyS2HDx+2zz77zP7zn//Yrl273LaNGzfa7t27I1U2AAAAAACQXz37v/32m7Vv396tea8s+RdffLFbSu+pp55y98eNG5eTwwIAAAAAgGj17N9///125pln2vbt261YsWLB7ZrHP2fOnEiUCwAAAAAA5GfP/v/+9z9bsGCBFS5cOGz7ySefbH/++WdOywIAAAAAAKLVs691AY8cOZJu+x9//OGG8wMAAAAAgDgL9tu2bWujRo0K3k9ISHCJ+R577DG79NJLI1k+AAAAAACQH8P4R44cae3atbP69evb/v377brrrrO1a9dahQoV7I033sjJIQEAAAAAQDSD/WrVqtmKFSvszTfftO+++8716vfo0cOuv/76sIR9AAAAAAAgToJ998SCBe2GG26IbGkAAAAAAEB0gv1XX331qI9369Ytp+UBAAAAAADRCPbvv//+sPuHDh2yvXv3uqX4ihcvTrAPAAAAAEC8ZePfvn172E1z9tesWWPnnXceCfoAAAAAAIjHYD8jtWvXtieffDJdrz8AAAAAAIjTYN9L2rdx48ZIHhIAAAAAAOTHnP2PPvoo7H4gELBNmzbZ888/b+eee25ODgkAAAAAAKIZ7Hfq1CnsfkJCglWsWNFatWplI0eOjFTZAAAAAABAfgX7qampOXkaAAAAAACItzn7AAAAAAAgTnv2+/Tpk+V9n3nmmZy8BAAAAAAAyM9g/9tvv3W3Q4cOWd26dd22n376yQoUKGBNmzYNm8sPAAAAAADiINi//PLLrVSpUjZ58mQrV66c27Z9+3a7+eab7fzzz7cHHngg0uUEAAAAAAB5OWdfGfeHDRsWDPRF/x46dCjZ+AEAAAAAiMdgPyUlxbZu3Zpuu7bt2rUrEuUCAAAAAAD5GexfeeWVbsj+e++9Z3/88Ye7vfvuu9ajRw/r3LlzTssCAAAAAACiNWd/3Lhx9uCDD9p1113nkvS5AxUs6IL9ESNGRKJcAAAAAAAgP4P94sWL24svvugC+19++cVtO/XUU61EiRI5LQcAAAAAAIjmMH7Ppk2b3K127dou0A8EApEqFwAAAAAAyM9g/++//7bWrVtbnTp17NJLL3UBv2gYP8vuAQAAAAAQh8F+7969rVChQrZhwwY3pN9zzTXX2IwZMyJZPgAAAAAAkB9z9mfNmmUzZ860atWqhW3XcP7ffvstJ4cEAAAAAADR7Nnfs2dPWI++Z9u2bVakSJFIlAsAAAAAAORnsH/++efbq6++GryfkJBgqampNnz4cGvZsmVOywIAAAAAAKI1jF9BvRL0LV261A4ePGh9+/a11atXu579r776KhLlAgAAAAAA+dmzf/rpp9tPP/1k5513nnXs2NEN6+/cubN9++23duqpp+a0LAAAAAAAIBo9+4cOHbL27dvbuHHj7N///nckygAAAAAAAKLZs68l97777rtIlgEAAAAAAER7GP8NN9xgr7zySiTLAQAAAAAAopmg7/DhwzZhwgT77LPPrFmzZlaiRImwx5955plIlQ8AAAAAAORlsL9u3To7+eSTbdWqVda0aVO3TYn6QmkZPgAAAAAAECfBfu3atW3Tpk02d+5cd/+aa66xMWPGWKVKlfKqfAAAAAAAIC/n7AcCgbD7n376qVt2DwAAAAAAxHmCvsyCfwAAAAAAEGfBvubjp52Tzxx9AAAAAADieM6+evJvuukmK1KkiLu/f/9+u/POO9Nl43/vvfciW0oAAAAAAJA3wX737t3D7t9www3ZeToAAAAAAIi1YH/ixIl5VxIAAAAAABD9BH0AAAAAACD2EOwDAAAAAOAzBPsAAAAAAPhMVIP9sWPHWqNGjax06dLu1qJFC/v000+Djyvbf8+ePe2EE06wkiVLWpcuXWzLli1hx9iwYYN16NDBihcvbklJSfbQQw/Z4cOHw/aZN2+eNW3a1K0iUKtWLZs0aVK+nSMAAAAAAMdVsF+tWjV78sknbdmyZbZ06VJr1aqVdezY0VavXu0e7927t3388cf29ttv2/z5823jxo3WuXPn4POPHDniAv2DBw/aggULbPLkyS6QHzBgQHCf9evXu31atmxpy5cvt169etmtt95qM2fOjMo5AwAAAACQ1xICgUDAYkj58uVtxIgR9s9//tMqVqxoU6ZMcf+WH3/80erVq2cLFy60c845x40CuOyyy1wjQKVKldw+48aNs4cffti2bt1qhQsXdv+ePn26rVq1KvgaXbt2tR07dtiMGTMyLMOBAwfczZOSkmLVq1e37du3uxEIsSw1NdW2Tn/CKtp2S0zIxVt72TORLBaQtbq7dav73CcmMsMI8YX6i3hF3UU8o/7C73U3JSXFypUrZzt37sxRHJqtpffyknrp1YO/Z88eN5xfvf2HDh2yNm3aBPc57bTT7KSTTgoG+/rbsGHDYKAv7dq1s7vuusuNDmjSpInbJ/QY3j7q4c/MsGHDbNCgQem26w3R1IJYrzg7rbQFLMESLRfBfnJyJIsFZK3u7txpan/kf9iIN9RfxCvqLuIZ9Rd+r7u7du3K1etEPdhfuXKlC+4VRGte/vvvv2/169d3Q+7VM1+2bNmw/RXYb9682f1bf0MDfe9x77Gj7aNWkn379lmxYsXSlalfv37Wp0+fdD37anmJh579BEv5v5793AT7SUmRLBaQtbqbkEDrPOIS9RfxirqLeEb9hd/rbtGiReM72K9bt64L7NWy8c4771j37t3d/PxoUiI/3dLSGxEPXyQJCUrGEMjdMP44OE/4j7704uVzBqRF/UW8ou4inlF/4ee6m5jLeh31YF+998qQL82aNbMlS5bY6NGj7ZprrnGJ9zS3PrR3X9n4K1eu7P6tv4sXLw47npetP3SftBn8dV899Bn16gMAAAAAEO8SY3FIg5LjKfAvVKiQzZkzJ/jYmjVr3FJ7GvYv+qtpAMkh88tnz57tAnlNBfD2CT2Gt493DAAAAAAA/CaqPfuaG3/JJZe4pHtKPqDM+/PmzXPL4pUpU8Z69Ojh5s4rQ78C+HvvvdcF6UrOJ23btnVB/Y033mjDhw938/P79+9vPXv2DA7Dv/POO+3555+3vn372i233GKff/65TZ061WXoBwAAAADAj6Ia7KtHvlu3brZp0yYX3Ddq1MgF+hdffLF7/Nlnn3XzFLp06eJ6+5VF/8UXXww+v0CBAjZt2jSXfV+NACVKlHBz/gcPHhzcp2bNmi6w7927t5seUK1aNRs/frw7FgAAAAAAfpQQUL5/HJWy8asxIqfrG+b3NIjkaUMtybblLkHf5aMjWSwga3U3OdmSkpJIsoO4Q/1FvKLuIp5Rf+H3upuSyziUTwUAAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPhMwWgXAJG3Zstu+3XfNkuwQLae17xm+TwrEwAAAAAg/9CzDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAAAAD4DME+AAAAAAA+Q7APAAAAAIDPEOwDAAAAAOAzBPsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgM1EN9ocNG2ZnnXWWlSpVypKSkqxTp062Zs2asH32799vPXv2tBNOOMFKlixpXbp0sS1btoTts2HDBuvQoYMVL17cHeehhx6yw4cPh+0zb948a9q0qRUpUsRq1aplkyZNypdzBAAAAADguAr258+f7wL5RYsW2ezZs+3QoUPWtm1b27NnT3Cf3r1728cff2xvv/2223/jxo3WuXPn4ONHjhxxgf7BgwdtwYIFNnnyZBfIDxgwILjP+vXr3T4tW7a05cuXW69evezWW2+1mTNn5vs5AwAAAACQ1wpaFM2YMSPsvoJ09cwvW7bMLrjgAtu5c6e98sorNmXKFGvVqpXbZ+LEiVavXj3XQHDOOefYrFmz7Pvvv7fPPvvMKlWqZI0bN7YhQ4bYww8/bAMHDrTChQvbuHHjrGbNmjZy5Eh3DD3/yy+/tGeffdbatWsXlXMHAAAAAMCXwX5aCu6lfPny7q+CfvX2t2nTJrjPaaedZieddJItXLjQBfv627BhQxfoexTA33XXXbZ69Wpr0qSJ2yf0GN4+6uHPyIEDB9zNk5KS4v6mpqa6WyxT+QJmFrCE7D83EPKcGD9P+I+ru4FAzH/GgIxQfxGvqLuIZ9Rf+L3upuaybsdMsK8TUfB97rnn2umnn+62bd682fXMly1bNmxfBfZ6zNsnNND3HvceO9o+CuL37dtnxYoVS5dLYNCgQenKuHXrVpdDIJbpOh4uXM4SXLCvsD/rkq1kyJ3kyBcOOEbdVYOfvvgSE8kdivhC/UW8ou4inlF/4fe6u2vXLn8E+5q7v2rVKje8Ptr69etnffr0Cd5Xo0D16tWtYsWKVrp0aYv1ivPTwe1WeN8WS8hmsJ9k5UPuJEW+cMAx6m5CQoL7nPE/bMQb6i/iFXUX8Yz6C7/X3aJFi8Z/sH/PPffYtGnT7IsvvrBq1aoFt1euXNkl3tuxY0dY776y8esxb5/FixeHHc/L1h+6T9oM/rqvwD1tr74oY79uaemNiIcvEvXpK9DPbrCfmBCyfxycJ/xHX3rx8jkD0qL+Il5RdxHPqL/wc91NzGW9juqnQsMWFOi///779vnnn7skeqGaNWtmhQoVsjlz5gS3aWk+LbXXokULd19/V65cackhw86V2V+BfP369YP7hB7D28c7BgAAAAAAflIw2kP3lWn/ww8/tFKlSgXn2JcpU8b1uOtvjx493JB6Je1TAH/vvfe6IF3J+URL9Smov/HGG2348OHuGP3793fH9nrn77zzTnv++eetb9++dsstt7iGhalTp9r06dOjefoAAAAAAOSJqPbsjx071iUmuOiii6xKlSrB21tvvRXcR8vjXXbZZdalSxe3HJ+G5L/33nvBxwsUKOCmAOivGgFuuOEG69atmw0ePDi4j0YMKLBXb/4ZZ5zhluAbP348y+4BAAAAAHypYLSH8R+LkhK88MIL7paZGjVq2CeffHLU46hB4dtvv81ROQEAAAAAiCdksgAAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfCaqwf4XX3xhl19+uVWtWtUSEhLsgw8+CHs8EAjYgAEDrEqVKlasWDFr06aNrV27Nmyfbdu22fXXX2+lS5e2smXLWo8ePWz37t1h+3z33Xd2/vnnW9GiRa169eo2fPjwfDk/AAAAAACOu2B/z549dsYZZ9gLL7yQ4eMKyseMGWPjxo2zr7/+2kqUKGHt2rWz/fv3B/dRoL969WqbPXu2TZs2zTUg3H777cHHU1JSrG3btlajRg1btmyZjRgxwgYOHGgvvfRSvpwjAAAAAAD5raBF0SWXXOJuGVGv/qhRo6x///7WsWNHt+3VV1+1SpUquREAXbt2tR9++MFmzJhhS5YssTPPPNPt89xzz9mll15qTz/9tBsx8Prrr9vBgwdtwoQJVrhwYWvQoIEtX77cnnnmmbBGAQAAAAAA/CKqwf7RrF+/3jZv3uyG7nvKlCljzZs3t4ULF7pgX381dN8L9EX7JyYmupEAV155pdvnggsucIG+R6MDnnrqKdu+fbuVK1cu3WsfOHDA3UJHB0hqaqq7xTKVL6DGEkvI/nMDIc+J8fOE/7i6GwjE/GcMyAj1F/GKuot4Rv2F3+tuai7rdswG+wr0RT35oXTfe0x/k5KSwh4vWLCglS9fPmyfmjVrpjuG91hGwf6wYcNs0KBB6bZv3bo1bApBLFKFOFy4nCW4YF9hf9YlW8mQO8mRLxxwjLq7c+dO98WnBjsgnlB/Ea+ou4hn1F/4ve7u2rXLn8F+NPXr18/69OkT1rOvxH4VK1Z0iQBjveL8dHC7Fd63xRKyGewnWfmQO+GNKEB+1F0l6tTnjP9hI95QfxGvqLuIZ9RfxKus1l0lmPdlsF+5cmX3d8uWLS4bv0f3GzduHNwnOU0P9OHDh12Gfu/5+qvnhPLue/ukVaRIEXdLS29EPHyRqE9fgX52g/3EhJD94+A84T/60ouXzxmQFvUX8Yq6i3hG/YWf625iLut1zH4qNPRewficOXPCetg1F79Fixbuvv7u2LHDZdn3fP75566lRHP7vX2Uof/QoUPBfZS5v27duhkO4QcAAAAAIN5FNdjfvXu3y4yvm5eUT//esGGDa+no1auXDR061D766CNbuXKldevWzWXY79Spk9u/Xr161r59e7vtttts8eLF9tVXX9k999zjkvdpP7nuuutccr4ePXq4JfreeustGz16dNgwfQAAAAAA/CSqw/iXLl1qLVu2DN73AvDu3bvbpEmTrG/fvrZnzx63RJ568M877zy31F7o3AUtracAv3Xr1m6YQ5cuXWzMmDFhGfxnzZplPXv2tGbNmlmFChVswIABLLsHAAAAAPCtqAb7F110kctAmBn17g8ePNjdMqPM+1OmTDnq6zRq1Mj+97//5aqsAAAAAADEi5idsw8AAAAAAHKGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGcI9gEAAAAA8BmCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2AQAAAADwGYJ9AAAAAAB8hmAfAAAAAACfIdgHAAAAAMBnCPYBAAAAAPAZgn0AAAAAAHyGYB8AAAAAAJ8h2AcAAAAAwGeOq2D/hRdesJNPPtmKFi1qzZs3t8WLF0e7SAAAAAAARNxxE+y/9dZb1qdPH3vsscfsm2++sTPOOMPatWtnycnJ0S4aAAAAAAARVdCOE88884zddtttdvPNN7v748aNs+nTp9uECRPskUceiXbxAAA+0u+9lTl+7rDODSNaFgAAcHw6LoL9gwcP2rJly6xfv37BbYmJidamTRtbuHBhuv0PHDjgbp6dO3e6vzt27LDU1FSLZSrf7n0H7ND+Q5ZggWw9d8eegyF3dkS+cMAx6m5KSooVLlzYfT6BaBs87fts7B2wMrbPdloxM0twWzpsHJOj150z3GLK9Kr35foYOb0Wfi1HJETuXNLX3dwacFn9fPrcHb9yc439hN8O8HvdTUlJcX8DgezFdZ6EQE6fGUc2btxoJ554oi1YsMBatGgR3N63b1+bP3++ff3112H7Dxw40AYNGhSFkgIAAAAA8P/7/fffrVq1apZdx0XPfnZpBIDm94e2vGzbts1OOOEES0iITKt3XlHrT/Xq1V2FKF26dLSLA2QZdRfxjPqLeEXdRTyj/sLvdTcQCNiuXbusatWqOXqd4yLYr1ChghUoUMC2bNkStl33K1eunG7/IkWKuFuosmXLWjxRpeFLD/GIuot4Rv1FvKLuIp5Rf+HnulumTJkcH/+4mNyiuRDNmjWzOXPmhPXW637osH4AAAAAAPzguOjZFw3L7969u5155pl29tln26hRo2zPnj3B7PwAAAAAAPjFcRPsX3PNNbZ161YbMGCAbd682Ro3bmwzZsywSpUqmZ9o+sFjjz2WbhoCEOuou4hn1F/EK+ou4hn1F/GqSD7V3eMiGz8AAAAAAMeT42LOPgAAAAAAxxOCfQAAAAAAfIZgHwAAAAAAnyHYBwAAAADAZwj2Y9wLL7xgJ598shUtWtSaN29uixcvPur+b7/9tp122mlu/4YNG9onn3wS9rjyMWpFgipVqlixYsWsTZs2tnbt2jw+CxyvIl1/b7rpJktISAi7tW/fPo/PAsej7NTd1atXW5cuXdz+qpNa2jW3xwRiqf4OHDgw3XevvquBaNbdl19+2c4//3wrV66cu+k3bdr9+d2LeK6/kfjdS7Afw9566y3r06ePW5bhm2++sTPOOMPatWtnycnJGe6/YMECu/baa61Hjx727bffWqdOndxt1apVwX2GDx9uY8aMsXHjxtnXX39tJUqUcMfcv39/Pp4Zjgd5UX9FX3KbNm0K3t544418OiMcL7Jbd/fu3WunnHKKPfnkk1a5cuWIHBOIpforDRo0CPvu/fLLL/PwLHA8ym7dnTdvnvvdMHfuXFu4cKFVr17d2rZta3/++WdwH373Ip7rb0R+92rpPcSms88+O9CzZ8/g/SNHjgSqVq0aGDZsWIb7X3311YEOHTqEbWvevHngjjvucP9OTU0NVK5cOTBixIjg4zt27AgUKVIk8MYbb+TZeeD4FOn6K927dw907NgxD0sNZL/uhqpRo0bg2WefjegxgWjX38ceeyxwxhlnRLysQCS/Jw8fPhwoVapUYPLkye4+v3sRz/U3Ur976dmPUQcPHrRly5a5IR2exMREd1+tPxnR9tD9RS1K3v7r16+3zZs3h+1TpkwZN8wks2MCsVJ/Q1tCk5KSrG7dunbXXXfZ33//nUdngeNRTupuNI4J5Hdd09DnqlWrulEA119/vW3YsCECJQYiV3c1SuXQoUNWvnx5d5/fvYjn+hup370E+zHqr7/+siNHjlilSpXCtuu+vrgyou1H29/7m51jArFSf72hTK+++qrNmTPHnnrqKZs/f75dcskl7rWAaNXdaBwTyM+6puBo0qRJNmPGDBs7dqwLojTXdNeuXREoNRCZuvvwww+7Bikv4OJ3L+K5/kbqd2/BbJwHAERV165dg/9WAr9GjRrZqaee6lo9W7duHdWyAYBf6celR9+7Cv5r1KhhU6dOdXlWgGhTzok333zT/R5QcjTAD/W3awR+99KzH6MqVKhgBQoUsC1btoRt1/3MEuho+9H29/5m55hArNTfjGg4qV7r559/jlDJcbzLSd2NxjGBaNa1smXLWp06dfjuRUzU3aefftoFS7NmzXLBkIffvYjn+hup370E+zGqcOHC1qxZMzdsw5Oamurut2jRIsPnaHvo/jJ79uzg/jVr1nQVLnSflJQUl500s2MCsVJ/M/LHH3+4uUtaUgeIVt2NxjGBaNa13bt32y+//MJ3L6Jed5Vtf8iQIW6KyZlnnhn2GL97Ec/1N2K/e3OV3g956s0333QZQydNmhT4/vvvA7fffnugbNmygc2bN7vHb7zxxsAjjzwS3P+rr74KFCxYMPD0008HfvjhB5c9t1ChQoGVK1cG93nyySfdMT788MPAd9995zI81qxZM7Bv376onCP8K9L1d9euXYEHH3wwsHDhwsD69esDn332WaBp06aB2rVrB/bv3x+184T/ZLfuHjhwIPDtt9+6W5UqVVw91b/Xrl2b5WMCsVx/H3jggcC8efPcd6++q9u0aROoUKFCIDk5OSrnCH/Kbt3Vb9rChQsH3nnnncCmTZuCN/1eCN2H372Ix/obqd+9BPsx7rnnngucdNJJrjJoSYdFixYFH7vwwgvdkgyhpk6dGqhTp47bv0GDBoHp06eHPa5lSB599NFApUqVXIVs3bp1YM2aNfl2Pji+RLL+7t27N9C2bdtAxYoVXSOAloi67bbbCJYQ9bqr/wmr7TztTftl9ZhALNffa665xjUE6Hgnnniiu//zzz/n+3nB/7JTd/U7IKO6q84CD797Ea/1N1K/exP0n6yPAwAAAAAAALGOOfsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAADgMwT7AAAgZv3666+WkJBgy5cvj3ZRAACIKwT7AADksZtuusk6deqUbvu8efNcILtjx44sH+uiiy6yXr16RaRcL7/8sp1xxhlWsmRJK1u2rDVp0sSGDRtmfrBixQq74oorLCkpyYoWLWonn3yyXXPNNZacnBztogEAkC8K5s/LAACAWDJhwgTXaDBmzBi78MIL7cCBA/bdd9/ZqlWrLN5t3brVWrdubZdddpnNnDnTNWRohMBHH31ke/bsybPXPXTokBUqVCjPjg8AQHbQsw8AQIz4+++/7dprr7UTTzzRihcvbg0bNrQ33ngjbITA/PnzbfTo0W5EgG4KYkVB+iWXXOJ66StVqmQ33nij/fXXX5m+lgLfq6++2nr06GG1atWyBg0auNd+/PHH041IGDRokFWsWNFKly5td955px08eDC4T2pqqhsNULNmTStWrJgbKfDOO++EvdaxyqZjDB8+3JWjSJEidtJJJ4WVQ9atW2ctW7Z010WvsXDhwkzP7auvvrKdO3fa+PHj3WgFlU3PffbZZ92/PatXr3YNAjqvUqVK2fnnn2+//PJLsEyDBw+2atWquTI1btzYZsyYkW56wVtvveUaSzR64PXXX3eP6XXr1avntp122mn24osvZlpWAADyCsE+AAAxYv/+/dasWTObPn26C5Bvv/12FxgvXrzYPa4gv0WLFnbbbbfZpk2b3K169epuGkCrVq1cYLt06VIXlG7ZssUF85mpXLmyLVq0yH777bejlmnOnDn2ww8/uCkHanh47733XPDvUaD/6quv2rhx41zw3Lt3b7vhhhtco4RkpWz9+vWzJ5980h599FH7/vvvbcqUKa5RINS///1ve/DBB93c/Tp16riGicOHD2d6bnrs/ffft0AgkOE+f/75p11wwQUukP/8889t2bJldssttwSPqWs9cuRIe/rpp92Ih3bt2rlpAWvXrg07ziOPPGL333+/u0baRwH/gAEDXGOFtj3xxBPuvCZPnnzU6wwAQMQFAABAnurevXugQIECgRIlSoTdihYtqkg0sH379kyf26FDh8ADDzwQvH/hhRcG7r///rB9hgwZEmjbtm3Ytt9//90de82aNRked+PGjYFzzjnH7VOnTh1Xxrfeeitw5MiRsHKXL18+sGfPnuC2sWPHBkqWLOn2279/f6B48eKBBQsWhB27R48egWuvvTZLZUtJSQkUKVIk8PLLL2dYzvXr17t9x48fH9y2evVqt+2HH37I9Lr961//ChQsWNCVv3379oHhw4cHNm/eHHy8X79+gZo1awYOHjyY4fOrVq0aePzxx8O2nXXWWYG77747rFyjRo0K2+fUU08NTJkyJWybrkGLFi0yLSsAAHmBOfsAAOQDDSMfO3Zs2Lavv/7a9YJ7jhw54nqCp06d6nqeNVxec+k1dP1Yyejmzp3rhsmnpWHp6glPq0qVKm4ovEYQfPHFF7ZgwQLr3r27G4Ku3vfExP8b/Kch86Gvr5EFu3fvtt9//9393bt3r1188cVhx1a51ZOflbKp51/nqDn2R9OoUaOwsouS7WmYfEbUs96nTx/Xa6/rrJEHurY6V02P0AgBDdvPaI59SkqKbdy40c4999yw7bqv8wl15plnBv+tfAA6J02N0OgLj0YLlClT5qjnBwBApBHsAwCQD0qUKOHmpIf6448/wu6PGDHCDR8fNWqUC0j1HCXRC50jnxEF3Zdffrk99dRT6R7zAuPMnH766e529913u/n4CoA1BF+NE8ei1xVNO1CegVAaHp+VsmkuflaEBuWaK+/Nqz+aE044wa666ip3U6CvBggNy9eQeuUXiAS9R2mvh1Y5aN68edh+BQoUiMjrAQCQVQT7AADECCWW69ixY7C3X8HsTz/9ZPXr1w/uU7hwYTcCIFTTpk3t3XffdcvLFSyY8/+1e68TmrFePdn79u0LBsea569eeuUKKF++vAvqN2zY4JLUZeRYZatdu7Y7tnID3HrrrZZXdN1OPfXU4LlppICC/owy6CthX9WqVd37EXpeun/22Wdn+hrKM6DnqQHj+uuvz7NzAQAgK0jQBwBAjFDgO3v2bDekXsnd7rjjDpfMLpSCZg1LVzZ4ZbRXg0DPnj1t27ZtLmndkiVL3FByLTl38803p2sY8Nx11102ZMgQF8AqSZ+C+G7durms+xqq79GoAg1LV+K8Tz75xB577DG755573DB/ZbBX0jwl5VPgrNf95ptv7LnnngsmpDtW2ZSx/uGHH7a+ffu6RH96XGV55ZVXcnwdp02b5hpM9FeNJWvWrHE9+iq/GlNE56Dh+l27dnWJA5V477///a/bVx566CE3GkHZ9rVNifg09F/J+I5GyQuVtFBLGuq1V65caRMnTrRnnnkmx+cDAEBO0LMPAECM6N+/v+sVVlZ3zZNXNn4tfadl5DwKrjW3Xr3w6nFfv369awBQ0K6guW3btm4OfI0aNax9+/bBufdptWnTxiZMmODyCGjJvwoVKrggXz3sGv7u0Vx6NUIoc72Oq6B94MCBwcfVYKAGAgW4KrvWtFdv/r/+9S/3uNdDfrSyKVu9ev2VxV5z5TW8X1MKckrXRtfvgQcecLkFNPpA56B8BFrdQHSOms+voF699xpmr+X1vHn69913n7vuOoZyA+iYWq5QxzkajU7Qa2tKho6tYf6akqHpGAAA5KcEZenL11cEAABx4aabbnIJ9D744INoFwUAAGQTw/gBAAAAAPAZgn0AAAAAAHyGYfwAAAAAAPgMPfsAAAAAAPgMwT4AAAAAAD5DsA8AAAAAgM8Q7AMAAAAA4DME+wAAAAAA+AzBPgAAAAAAPkOwDwAAAACAzxDsAwAAAABg/vL/ATHCNGcQOJ1PAAAAAElFTkSuQmCC", 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62MmuX5+fAg5N/6PUPV2cqE+lUmsVWGe+mM2JLsbDGcROFySZL4DUXzWv/pUFpf7TGv05+AaC6CLf//tgmS+g/RdNeV3Iaj96j5mPX06vE87NEqWRa1Fqty5idcGp/uO6iFVfSgUAujBXurSW7PgHa1I5dJMic5CizyI7SvvPvK0GLFOfz8zrxH+cwilTTsfeHwiEEkSo/27m8oTaTWPlypUulTmnbg3+cqo7g1KM1f1AwZc+G12Eq798cBCv/Wlgybz2Fy59Ftn1M1bKbmbbt293qeLTpk3L8noKePOi96AAJPP3NpwBJNVvWMdHg7cpkH/rrbdcSrvOMwqgggd3zO51WrRo4VKUVZf0vQq1PoV6/JWqrv2GMshkfs8L/rqomxha9P2bM2eOG5NDg1Pqd7pJ578ZkFvXAx0HHQ/dPMhM5xkFg+rOEpzer3T0YDo2klMKub+Pvuq2Uu11s1OBrW4AqVuNbsYqwMxL5vFGQhXqeVT/6+aFxjEI5TyWHdUvdS3QzZGUlBQ3iKg+I3Vl0HHTd80/pkFeXULUxUA3g3SDKnOdyO37lp/vmbbP71zzAAqGIB5AodEFvS4acruYUXCjVh/d5ddAYWrVUOuiLuzU0hPccpPbPiItpwsTXWSFUqZIyOl18ntRGkm6aFWruAJJXagrkFfLnj+bQq1KOfUJDZ5yMBLHI6/jlJ8yFeTY69gEe+GFF7IdJDE7KqtajtXHOKeLfT8df/VBVgaHWu70GajFV4NvBe+vdevWbrrH7GS+AVIY1LKnvslqAdY4EMqoULnUzzyU7Btto++j3md2n0s4YxHoZp8Cei06lsq4UGt5OPNxh1OfCuP4R+q8oCwatbarP7Na0zW2hIL4wpL5PO0/jgpeswvGg8fd0BgDynxQxoIyItRve+zYsa4feW793dUnPNzW+2jSZ6v6okUZIZoWT59LqAPz6e+Tzh+6caYxX3Q+0ICBGmND56Dcvm/5+Z7p2OYU9AMoXATxAAqNLs5EKaa5UUuHWuC16GL3gQcecK29Cux18RLpO/3+FqDgi18NXhQcyKllK7tp09TqEpw+Gk7Z/AMYKSUyuDVeo0D7fx8J2o9ac3VRFtyKFOnX8bfO6LjpmCqtUi2Oem+6mMzrwlPlUKtT5tac4FHgIyGcMoUjp88+82jNuQ02lpkyE5S9Eko5NdiUbhjoppcGAlNAk3n0cO1PLc/6bkWjxUwX+WrtVUu8Wgdz+g5KTuXTe1AdUYtk8E2MgvJPi6mskmDZlU2D0Kml1d+iHmp9CvX4azt9X/V90I2OoqRznV5fA/+J//zmf5wdHQcdj+XLl2f5nc4zOu/kdYPC31VImVqh1Hdtr9Z4LfqMdJw0T3nw7CeZKYhVEKybyf4MnUifR/W//lb5pzaN1Hksc/30Hy99LjndGNegiaqrGkFfmQp+oYwgH+737MiRIy7bIrg7FICiQ594AIVCAcW9997rLgg0qndO1GKQmf8i1t9H0z+fcKTmItfozMH99DVNmS6UNMJ98AWNRs4OnjtZKY6Zp9oJp2znnnuuu/BX6mowpfXqAj/49QtCr6OU7uD+0rrg0ijDak3R6Ofh0kWz0jQz0/tWf2MFArqwVwuOWufVZzi7IEBpuH66uaMWouB+yeozHdwPNBLCKVM49Nlnl56qgCR4ydwyn1ertY6nWhuzO9b6HP0UWCgdXCOG64aZfhecSu/fn45xdsdUo2/7RwkvLP4WvcytxBplP7OcvkvqXqP96EZA5v3ocfBUddlRgJVdK7V/DIzMKeE6/sF99fWd15gdGiFe5QinPoV6/JVJoc9To9Jnbi2NVOaNbiZk139ZNyZ188B/HPQ91g0ijXqf+TvvL4uOgY6HjktwNySN2q8uHrqplHnKusz0/dc2ummrbiA5HUcFxzovBNP5WTdS8pomUa3ZKrOmIC2s86jeh8of/BnrM0xOTg7pdTQlXHbvP3P91PHWe1YGQubjEfy5BD/2/6xuT3kJ93umOqNy5DQbC4DCRUs8gAJT+p1aJ3SBo4s4BfC6868WCgVomQfhCaaLVqXTa25gba8+oprSSX1udSHov2DTAGaTJk1yFzG62NfAR5n7WIZKc99q30qlVXkVUKhlI3hgIvXRV3CvlF9diKs/olp8ggeaC7dsSgdVeqRaS3Xhq7RndRnQhbDSRTPvO7/Uj1Jp1kqf1MWrpmDSe9FUQHqvmfvkhxoAaMAq3WjQoFo6hgpO1OKzYcMGt1//BaSmZ1PgpOOgY6p+vrpZo8BImQj+GzeaQk03NDRwlKZrUrCrVjN/fYlky3GoZQqHBj/TBf6wYcNcerYu7PUZ50XBn781L5hSs5Vyru+M+vz6pw1ToKcWNn2Gqjf+6ddEQbuCCqWDKwXX3183eJpHpdlr+j29f00PqBtJen2t180Cf4tfYVCQpoBQ/XsVqGj8CtX5NWvWZNnWP5icvh/qKqAsDx1PfS+U5q0ptfT+FfCqDmsf6tuu+q6pAHOiQdsUCCptXC2zujGn9H59dvpuZB4UU32OFZgFTzEnCm7CrU+hHn+df/S+deNT3y8FVHrtBQsWuHE0FLgVlM7JqidqOVW/ctVX9X9XsK5gWNPb+Wk6S50jTz755EC/bB17dXnSGAKiz0T71HaaPk7p7zrvaF/6vEOpG5pOTsdIr6PPXDcQdONAr6NjpfODWpaVyaDzsI6zXkefu87dwQNdZkdlU0q9PpPs+t4rSyRzQCyqY6GeR7Vtx44dXYaAWt9Vx/Qd9teBvM5j6u+v/esz92eDqR7pZrPOs/7pCnW8dMNXf5t0vtH5WDdPdW5W/da5WK+t74u+Dzo/6zk634TSpSDc75k+e2UeKH0fQBQU0qj3AOKAf+og/6LphurWres7++yz3ZRcwVOZ5TStzZw5c3x9+vTx1a9f3z1f/2v6ssxTbL399tu+li1buqlugqft0dRjJ5xwQrbly2mKOU1vNWrUKF/t2rXd1D6aKiy7qdI0BZqmo9P0TaeeeqqbdirzPnMrW+Yp5vzTKt18883ufZYuXdrXvHlzN81R8BRBov0kJSVlKVOo04Bp2qhBgwb5atas6Y5r69ats53qKNQp5rS/Bx980L13Ta2l91qtWjU3jdIbb7yR7fYqf8OGDd37VL3o1q2b75lnnsmw3erVq93r63OoVauWmybLP2VT8LRxOX3OOZU/u+MXSplymgbOPyVV8DHcs2eP79JLL3XTZPmn7cqNf985Lf5pyFRHVD+POeYY99npMzzllFN8Dz/8cJZp0VRv9H6ym7rQT8956KGH3PFTXdbn1q5dO9/dd9/t27VrV76mmMuubuY07eT69evd1Gk6Tpo67MILL3RTkWk7nQ8yT7Gn75ymW8s83ZzqhaYp0xR8WjQVocqxfPnyXMv7wQcf+AYPHuy219RgOqY6ttdff32W6dX8703Tium7qeOlKTKDpzsMt46Hevxl8uTJ7vX826nef/TRR3nW9+zOS5npuzZ69GjfX/7yF3fu03dY3znt75NPPsl26jP/56Yp9jQ93Z133plhm8WLF/t69Ojhjmv58uV9Xbt29X399ddhTUWqY6t9qG7odZo1a+amv/NP86dp/HSc9fnpc9d2mmZNU4OG4oYbbnCfd3bf55wWTQ0YznlUU6TqXKAp4FQ+lV/TB2pf06ZNy7V82k7vr1WrVu65qkuNGjVy+wievs/vnXfececDnTMrV67s69ixo/ub5peSkuLr3r27+0xUbk3V55+eNLjsmf8Wh/s902egKfwAREeC/onGzQMAALKjVq6bb77ZDYyollugqKjVVFODZe7yAu9StoFaqJUxphb9oqJB+JT9oen2lFVQnCgbQ9kTyhgo6jEcAPwPfeIBAFGjfsHBlNqqFFaNeEwAD6CgNFDfkCFDXBeIojqPqcuEurkonV3BbnGjY6nxOAjggeihTzwAIGrUD1TzXutiUIPEadwB9RdW33gAiAT1vS9MGndBgbwG0tOYADNmzHDjLmjQvsKYAjXapk2bFu0iAHGPIB4AEDUaQOy5555zQbtarzRwlS4QM4+yDgCxSoPmabo7zWCibCINVKiW+Ouuuy7aRQNQTNEnHgAAAAAAj6BPPAAAAAAAHkEQDwAAAACARxSbPvH79u2z448/3i688EJ7+OGHQ35eenq6bdiwwSpVquSmlgEAAAAAoDCpV/vu3butfv36VqJEifgM4u+//377y1/+EvbzFMA3bNiwUMoEAAAAAEBOfvvtN2vQoIHFXRC/cuVKNyVR79697YcffgjruWqB9x88zecZq5QxsHXrVqtVq1bYd2qAaKLuwsuov/Aq6i68jPqLeKi7qamprjHZH496KoifO3eujR8/3hYtWmQbN260t956y/r27Zthm+TkZLfNpk2brE2bNm7ajo4dOwZ+f8stt7jfa07OcPlT6BXAx3oQr2lLVEZOZvAS6i68jPoLr6Luwsuov4inupuQjy7dUQ/i9+7d6wLzwYMHW79+/bL8fvr06TZs2DCbNGmSderUySZOnOjmFV6+fLnVrl3b3n77bWvRooVbQgniDx486JbgOyD+A64lVqls6jcRy2UEskPdhZdRf+FV1F14GfUX8VB30wtQv6MexPfq1cstOZkwYYINHTrUBg0a5B4rmH/vvfds8uTJNnLkSPvmm29s2rRp9vrrr9uePXvs8OHD7s7H6NGjs93f2LFj7e67786yXmkPumsSq/Qh79q1y1UK7kjCS6i78DLqL7yKugsvo/4iHuru7t278/06CT69QoxQKkFwOv2hQ4esfPny9sYbb2RIsR8wYIDt3LnTtcIHmzJliusTn9vo9Nm1xKsvwo4dO2I+nZ6+QfAi6i68jPoLr6Luwsuov4iXPvHVqlVzQX+4cWjUW+Jzs23bNktLS7M6depkWK/HGsguPxITE92SmQ5yrJ8kdJPDC+UEMqPuwsuov/Aq6i68LBbrr9o+jxw54uITIKcgXnVEjdGlS5e2UqVK5djnvSB1O6aD+HANHDgw2kUAAAAAUMwoKNMg3Pv27Yt2URDD/P3hlSqv4F1Z5fXq1bMyZcpE9HViOoivWbOmlSxZ0jZv3pxhvR7XrVs3auUCAAAAEB8UlK1Zs8bFJfXr13cBWX5GFEfx5/szW0N1RWO1KbVedad58+YRzSqJ6SBeX5B27drZnDlzAn3i9SXS4+uuu65A+9a0dVpIhwEAAACQWyu8YhCNo6WWVSCvIF5p9KorSqlfu3atq0Nly5a1YhPEa0T5VatWBR7rTsV3331n1atXt0aNGrnp5TSQXfv27d3c8JpiTtPS+Uerz6+kpCS3aECBKlWqROCdAAAAACiuYql/PuK7zkQ9iF+4cKF17do18FhBuyhw12jz/fv3d2kImjJu06ZN1rZtW5s1a1aWwe4AAAAAACjuoh7Ed+nSxaUd5Eap8wVNnwcAAAAAwOuiHsQDAAAAgBeNmvF9kb7e2H6ti/T14snAgQNt586dNnPmzJC2//XXX61p06a2ZMkSly1elOjYAQAAAADFlLokX3/99Xb00UdbYmKiG6Cvd+/ebrDwSGZX33TTTeZljz32mOvO7QVx2xLP6PQAAAAAijO1Fp966qlWtWpVGz9+vLVu3dpNffbhhx+6Qb5//vnnIiuLulAr9tLI7UXt0KFDec7V7qXBzuO2JV6VNiUlxRYsWBDtogAAAABAxF177bVuTvv58+fb3/72N2vRooWdcMIJbjDxb775xm2zbt0669Onj1WsWNEqV65sF110kW3evDmwj7vuusuli7/00kvWpEkTF+xefPHFtnv37kAa+ueff+5asvVaWnTz4LPPPnM/f/DBB27acGUBfPnll3bw4EG74YYbrHbt2m7atdNOOy0Qk2kqvwYNGtjTTz+d4X0oZV0jvWu6NlHa+z/+8Q+rVauWK/NZZ51lS5cutcxlfu6551zKu396tzfeeMPdyChXrpzVqFHDunfv7mY+878P/7TmosHUVTbdANG2f/3rX+2XX36xWBC3QTwAAAAAFFfbt293gagaLytUqJDl9wpOFTQrgNe2CsQ/+ugjW716tZshLJiCV/UVf/fdd92ibR988EH3OwXvnTt3tqFDh9rGjRvdopR9v5EjR7ptf/rpJzvxxBNtxIgR9uabb9rUqVNt8eLFdswxx1iPHj1cGRSoX3LJJfbKK69keP2XX37ZZRQ0btzYPb7wwgtty5Yt7gbBokWL7OSTT7Zu3bq5ffhpGnO9zowZM9wU5iqX9j148GBXFt1k6NevX46DrCu4180Ozaamrgcq2wUXXOCOWbTFbTo9AAAAABRXCmIVoB533HE5bqPg9Pvvv7c1a9YEAu8XX3zRtdardbxDhw5unQJX9RevVKmSe3zFFVe4595///2uZV6p6uXLl7e6detmeY177rnHzj777EBgrFZ27atXr15u3bPPPutuHjz//PN266232mWXXWaPPPKIyxBo1KiRe+1p06bZHXfc4bb/8ssvXWaBgni17svDDz/sbjKopf2qq64KpNDrvai1XnTD4MiRIy5w998MUKt8TpS5EGzy5MluX8rmbtWqlUUTLfEAAAAAUMzkNY23qEVawXtwy3nLli1dK71+56c0en8AL/Xq1XNBdCjat2+foUVfffLVqu5XunRp69ixY+D1lAZ//PHHB1rj1eqv11LruyxdutT27NnjUtzVBcC/6EZEcLq7AnV/AC9t2rRxrfUK3LUv3TzYsWNHjuVeuXKla7nXgIBK2dcxEN1ciDZa4gEAAACgmGnevLnrkx6JwesUaAfTfkNNK88ulT8vao1XEK9UfP3fs2dPF7TLnj173E0EpcNnppsPOb1uyZIlXYv/119/bbNnz7YnnnjCbr/9dvv2229dv/nMNIK/bgQo2K9fv757v2qBVwt/tMVtEO/J0ennP6PeLWYJed9Vy1HvxyJZIgAAAAAxqHr16q6vuWIeDSSXOajV4HBq8f7tt9/c4m+NV7q4fqcW+VApnT6UuKpZs2Zu26+++iqQ0q6WeaXuB09Rd+mll7r0efV3V4r8pEmTAr87+eST3bR5GuXe3zoeKt18UBaAltGjR7syvPXWW67ve7A//vjDli9f7gL4008/PZDGHyviNp2e0ekBAAAAFGf+Rkulq2uQN6WIK2398ccfd4PRaXR2pZer5Vt9xtXX/Morr7QzzzwzQxp8XhRMq0Vbo9Jv27Ytx1Z63Ui45pprXN93DbqneEwD4u3bt8+GDBmSYX+nnHKKW6fyn3/++YHfde/e3ZVdI8mrRV2vqdZ1taprELqcqHwPPPCA20Yp8RrwbuvWre5GRmbVqlVzLf/PPPOMG1vgk08+yRLoR1PctsQDAAAAQEGM7ZfzwGixQP25FZxrALrhw4e7EdrVT1xTvmmAObVMv/3223b99dfbGWec4UZgV+q6Us3Dccstt9iAAQNc6/3+/ftd//ScaKR6BfkaHE/T1OlmgeatV+AcTDcWNEWebipoSji/hIQEe//9913QPmjQIBeIa0A9lb9OnTo5vq76tc+dO9cmTpxoqamprhVeA+j5B9gLpuOgwfSUwaAU+mOPPdbd+OjSpYvFggRfKCMeFGP6ADWi4q5du9wHG6tU0be8e5/Vtu1WgnR6eIiru1u2uLlAdUIEvIT6C6+i7sLLYq3+HjhwwAWlwfONA9lRaK0R8JXqr5sNudWdgsSh0f9WAAAAAACAkBDEAwAAAADgEQTxAAAAAAB4BEE8AAAAAAAeUSKep1vQ6IkdOnSIdlEAAAAAAAhJ3AbxzBMPAAAAAPCauA3iAQAAAADwGoJ4AAAAAAA8giAeAAAAAACPKBXtAgAAAACAJ/33xqJ9vd6PWXEzcOBA27lzp82cOTPHbbp06WJt27a1iRMnFmnZYhUt8QAAAABQDClA7tu3b5G93meffWYJCQlZljvuuCPH5zz22GM2ZcqUIitjcUBLPAAAAAAgYpYvX26VK1cOPK5YsWKWbdLS0lyAX6VKlSIunffFbUs888QDAAAAiGc//PCD9erVywXZderUsSuuuMK2bdsWaFUvU6aMffHFF4Htx40bZ7Vr17bNmzfnul9tU7du3cCi/au1vWrVqvbOO++4OCwxMdHWrVuXJVtg7969duWVV7rn1KtXzx555JEs+2/SpIndd999ge0aN27s9rt161br06ePW3fiiSfawoULA8/5448/7JJLLrGjjjrKypcvb61bt7ZXX301S9r+DTfcYCNGjLDq1au7st91110Wa+I2iGeeeAAAAADxSv3QzzrrLDvppJNcsDtr1iwXnF900UWBgPamm25ygf2uXbtsyZIlduedd9pzzz3nAv782Ldvnz300ENuHz/++KML9jO79dZb7fPPP7e3337bZs+e7W4mLF68OMt2jz76qJ166qmuXOedd54rp4L6yy+/3G3frFkz99jn87ntDxw4YO3atbP33nvP3by46qqr3HPmz5+fYb9Tp061ChUq2LfffutuWtxzzz320UcfWSwhnR4AAAAA4syTTz7pAvgHHnggsG7y5MnWsGFDW7FihbVo0cK1diuAVcCrwHfAgAF2/vnn57nvBg0aZHi8du1a9//hw4ftqaeesjZt2mT7vD179tjzzz9v//nPf6xbt26BoDrz/uTcc8+1q6++2v08evRoe/rpp12W9YUXXujW/etf/7LOnTu7GxNqUVcL/C233GJ+119/vX344Yf22muvWceOHQPr1YI/ZswY93Pz5s3dcZozZ46dffbZFisI4gEAAAAgzixdutQ+/fTTbPur//LLLy6IVzr9yy+/7AJbpayr9TsUSsGvVKlS4HG1atXc/9qf9pUTve6hQ4esU6dOgXVKaz/22GOzbHti0H78mQFKkc+8bsuWLS6IVx983bBQ0P7777+71zl48KBLrc9pv6KUfu0jlhDEAwAAAECcUat37969XXp7Zgpc/b7++mv3//bt292iVPO8NG3a1PV/z6xcuXJuMLtIKF26dOBn/z6zW5eenu7+Hz9+vBsJX9PUKdjX+1B3AQXzOe3Xvx//PmJF3PaJBwAAAIB4dfLJJ7t+6Rok7phjjsmw+AN1tYzffPPN9uyzz7rWcaXTF2ZAq37sCqLVH91vx44dLr2/oL766is36J36zCud/+ijj47IfqOBIB4AAAAAiikNSvfdd99lWH777Tc30Lda1jViuwb7VsCuPuKDBg1yqedaFPD26NHDrXvhhRds2bJl2Y4WHylK7R8yZIgb3O6TTz5x/fA1en2JEgUPW5s3b+769yuz4KeffnL96fMaZT9WkU4PAAAAAPnR+zGLdRrdXQPYBVOgrBHi1TqtAeDOOecc1z9c/d579uzpguZ7773XDUj37rvvBlLsn3nmGRf0a/ucBqcrKKW9+1P91a9++PDh7kZEQd1xxx22evVqd1NC/eA1WJ+mtovEvotags8/5n6cSk1NtSpVqrgPr3LlyharlLay5d37rLZttxIJvmJ9okHx4uruli1uCpFI3EUFihL1F15F3YWXxVr91dRka9ascf28y5YtG+3iIIYptD5y5IiVKlXK9aXPre4UJA6N/rcCAAAAAACEhCAeAAAAAACPiNsgPjk52Vq2bGkdOnSIdlEAAAAAAAhJ3AbxGo0xJSXFjcQIAAAAAIAXxG0QDwAAAAChivPxwBFDdYYgHgAAAAByULp0aff/vn37ol0UeMy+P+uMvw5FCvPEAwAAAEAOSpYsaVWrVnXT3onmGNf0YUBOU8ypzuzfv9/VGdUdPY4kgngAAAAAyEXdunXd//5AHsgpiE9PT7cSJUq4Gz0K4P11J5II4gEAAAAgFwrI6tWrZ7Vr17bDhw9HuziIUQrg//jjD6tRo4YlJiZGvAXejyAeAAAAAEKgoKywAjMUjyC+dOnSVrZsWdcaX1gY2A4AAAAAAI8giAcAAAAAwCMI4gEAAAAA8AiCeAAAAAAAPIIgHgAAAAAAj4jbID45OdlatmxpHTp0iHZRAAAAAAAISdwG8UlJSZaSkmILFiyIdlEAAAAAAAhJ3AbxAAAAAAB4DUE8AAAAAAAeQRAPAAAAAIBHEMQDAAAAAOARBPEAAAAAAHgEQTwAAAAAAB5BEA8AAAAAgEeUinYBELrlm/fYr/u3W4L5wn5up6bVC6VMAAAAAICiQ0s8AAAAAAAeQRAPAAAAAIBHEMQDAAAAAOARBPEAAAAAAHgEQTwAAAAAAB5BEA8AAAAAgEcQxAMAAAAA4BEE8QAAAAAAeARBPAAAAAAAHkEQDwAAAACAR8RtEJ+cnGwtW7a0Dh06RLsoAAAAAACEJG6D+KSkJEtJSbEFCxZEuygAAAAAAIQkboN4AAAAAAC8hiAeAAAAAACPIIgHAAAAAMAjCOIBAAAAAPAIgngAAAAAADyCIB4AAAAAAI8giAcAAAAAwCMI4gEAAAAA8AiCeAAAAAAAPIIgHgAAAAAAjyCIBwAAAADAIwjiAQAAAADwCIJ4AAAAAAA8giAeAAAAAACPIIgHAAAAAMAjCOIBAAAAAPAIgngAAAAAADyCIB4AAAAAAI8giAcAAAAAwCMI4gEAAAAA8AiCeAAAAAAAPIIgHgAAAAAAjyCIBwAAAADAIwjiAQAAAADwCIJ4AAAAAAA8wvNB/M6dO619+/bWtm1ba9WqlT377LPRLhIAAAAAAIWilHlcpUqVbO7cuVa+fHnbu3evC+T79etnNWrUiHbRAAAAAACIKM+3xJcsWdIF8HLw4EHz+XxuAQAAAACguIl6EK9W9N69e1v9+vUtISHBZs6cmWWb5ORka9KkiZUtW9Y6depk8+fPz5JS36ZNG2vQoIHdeuutVrNmzSJ8BwAAAAAAxEk6vVLgFYAPHjzYpcFnNn36dBs2bJhNmjTJBfATJ060Hj162PLly6127dpum6pVq9rSpUtt8+bNbh9///vfrU6dOtm+nlrrtfilpqa6/9PT090Sq1Q25Rf4LCF/z/f9+bwYfo8onlzd9fli+vsF5IT6C6+i7sLLqL+Ih7qbXoD6HfUgvlevXm7JyYQJE2zo0KE2aNAg91jB/HvvvWeTJ0+2kSNHZthWgbtuCHzxxRcukM/O2LFj7e67786yfuvWrXbgwAGLVfqQj5SpZgkuiA+/u8AWq/jnD1siXzggj7q7a9cud0IrUSLqyT9AWKi/8CrqLryM+ot4qLu7d+/2bhCfm0OHDtmiRYts1KhRgXU6GN27d7d58+a5x2p9V594DXCnA6b0/GuuuSbHfWpfatkPbolv2LCh1apVyypXrmyxXCFWHNphZfZvtoR8BPG1rfqfP/wvewEoyrqrrjL6jvGHGF5D/YVXUXfhZdRfxEPdLVu2bPEM4rdt22ZpaWlZUuP1+Oeff3Y/r1271q666qrAgHbXX3+9tW7dOsd9JiYmuiUzHeRYP0moDV4BfH6C+BIJfz4nxt8jiiedzLzwHQOyQ/2FV1F34WXUXxT3uluiAHU7poP4UHTs2NG+++67aBcDAAAAAIBCF9O3tjTKvKaQU8p8MD2uW7du1MoFAAAAAEA0xHQQX6ZMGWvXrp3NmTMnQz8DPe7cuXOB9q1p61q2bGkdOnSIQEkBAAAAACh8UU+n37Nnj61atSrweM2aNS49vnr16taoUSM3CN2AAQOsffv2LnVeU8xpWjr/aPX5lZSU5BYNbFelSpUIvBMAAAAAAIp5EL9w4ULr2rVr4LF/5HgF7lOmTLH+/fu76d9Gjx5tmzZtsrZt29qsWbNynAceAAAAAIDiKupBfJcuXdyo8rm57rrr3AIAAAAAQDyL6T7xAAAAAADg/xHEAwAAAADgEXEbxDM6PQAAAADAa+I2iNfI9CkpKbZgwYJoFwUAAAAAgJDEbRAPAAAAAIDXEMQDAAAAAOARBPEAAAAAAHgEQTwAAAAAAB4Rt0E8o9MDAAAAALwmboN4RqcHAAAAAHhN3AbxAAAAAAB4DUE8AAAAAAAeQRAPAAAAAIBHEMQDAAAAAOARBPEAAAAAAHgEQTwAAAAAAB4Rt0E888QDAAAAALwmboN45okHAAAAAHhN3AbxAAAAAAB4DUE8AAAAAAAeQRAPAAAAAIBHEMQDAAAAAOARBPEAAAAAAHgEQTwAAAAAAB4Rt0E888QDAAAAALwmboN45okHAAAAAHhN3AbxAAAAAAB4DUE8AAAAAAAeQRAPAAAAAIBHEMQDAAAAAOARBPEAAAAAAHgEQTwAAAAAAB5BEA8AAAAAgEcQxAMAAAAA4BEE8QAAAAAAeETcBvHJycnWsmVL69ChQ7SLAgAAAABASOI2iE9KSrKUlBRbsGBBtIsCAAAAAEBISlk+HD582DZt2mT79u2zWrVqWfXq1fOzGwAAAAAAUBgt8bt377ann37azjzzTKtcubI1adLEjj/+eBfEN27c2IYOHUqrNgAAAAAA0Q7iJ0yY4IL2F154wbp3724zZ8607777zlasWGHz5s2zMWPG2JEjR+ycc86xnj172sqVKwuzzAAAAAAAxKWQ0unVwj537lw74YQTsv19x44dbfDgwTZp0iQX6H/xxRfWvHnzSJcVAAAAAIC4FlIQ/+qrr4a0s8TERPvnP/9Z0DIBAAAAAIBIjk6/atUq+/DDD23//v3usc/ny++uAAAAAABAYQTxf/zxh+sX36JFCzv33HNt48aNbv2QIUNs+PDh4e4OAAAAAAAUVhB/8803W6lSpWzdunVWvnz5wPr+/fvbrFmzwt0dAAAAAAAorHniZ8+e7dLoGzRokGG9BrJbu3ZtuLsDAAAAAACF1RK/d+/eDC3wftu3b3cD2wEAAAAAgBgJ4k8//XR78cUXA48TEhIsPT3dxo0bZ127do10+QAAAAAAQH7T6RWsd+vWzRYuXGiHDh2yESNG2I8//uha4r/66qtwdwcAAAAAAAqrJb5Vq1a2YsUKO+2006xPnz4uvb5fv362ZMkSa9asmXlFcnKytWzZ0jp06BDtogAAAAAAUDgt8VKlShW7/fbbzcuSkpLckpqa6t4PAAAAAADFIohftmxZyDs88cQTC1IeAAAAAABQkCC+bdu2bgA7n8+X63baJi0tLZRdAgAAAACAwgji16xZE+5+AQAAAABANIL4xo0bR/p1AQAAAABAUQxsJykpKbZu3To3zVyw888/P7+7BAAAAAAAkQziV69ebRdccIF9//33GfrJ62ehTzwAAAAAADEyT/yNN95oTZs2tS1btlj58uXtxx9/tLlz51r79u3ts88+K5xSAgAAAACA8Fvi582bZ5988onVrFnTSpQo4ZbTTjvNxo4dazfccIMtWbKkcEoKAAAAAECcC7slXunylSpVcj8rkN+wYUNg8Lvly5dHvoQAAAAAACB/LfGtWrWypUuXupT6Tp062bhx46xMmTL2zDPP2NFHHx3u7gAAAAAAQGEF8XfccYft3bvX/XzPPffYX//6Vzv99NOtRo0aNn369HB3BwAAAAAACiuI79GjR+DnY445xn7++Wfbvn27VatWLTBCPQAAAAAAiIE+8bt27XJBe7Dq1avbjh07LDU1NZJlAwAAAAAABQniL774Yps2bVqW9a+99pr7HQAAAAAAiJEg/ttvv7WuXbtmWd+lSxf3OwAAAAAAECNB/MGDB+3IkSNZ1h8+fNj2798fqXIBAAAAAICCBvEdO3Z008llNmnSJGvXrl24uwMAAAAAAIU1Ov19991n3bt3d3PFd+vWza2bM2eOLViwwGbPnm1ekZyc7Ja0tLRoFwUAAAAAgMJpiT/11FNt3rx51rBhQzeY3X//+1831dyyZcvcfPFekZSUZCkpKe7mAwAAAAAAxbIlXtq2bWsvv/xy5EsDAAAAAAAi1xK/ePFi+/777wOP3377bevbt6/ddtttdujQoXB3BwAAAAAACiuIv/rqq23FihXu59WrV1v//v2tfPny9vrrr9uIESPC3R0AAAAAACisIF4BvNLpRYH7mWeeaa+88opNmTLF3nzzzXB3BwAAAAAACiuI9/l8lp6e7n7++OOP7dxzz3U/a6C7bdu2hbs7AAAAAABQWEF8+/bt3TRzL730kn3++ed23nnnufVr1qyxOnXqhLs7AAAAAABQWEH8xIkT3eB21113nd1+++1uejl544037JRTTgl3dwAAAAAAoLCmmDvxxBMzjE7vN378eCtZsmS4uwMAAAAAAIU5T3x2ypYtG6ldAQAAAACASKTTAwAAAACA6CCIBwAAAADAIwjiAQAAAADwCIJ4AAAAAACK68B2w4YNy3Z9QkKCG9xOU8716dPHqlevHonyAQAAAACA/AbxS5YscfPEp6Wl2bHHHuvWrVixwk0vd9xxx9lTTz1lw4cPty+//NJatmwZ7u4BAAAAAECk0unVyt69e3fbsGGDLVq0yC3r16+3s88+2y655BL7/fff7YwzzrCbb7453F0DAAAAAIBIBvHjx4+3e++91ypXrhxYV6VKFbvrrrts3LhxVr58eRs9erQL7gEAAAAAQBSD+F27dtmWLVuyrN+6daulpqa6n6tWrWqHDh2KTAkBAAAAAED+0+kHDx5sb731lkuj16KfhwwZYn379nXbzJ8/31q0aBHurgEAAAAAQCQHtvv3v//t+rtffPHFduTIkf/tpFQpGzBggD366KPusQa4e+6558LdNQAAAAAAiGQQX7FiRXv22WddwL569Wq37uijj3br/dq2bRvubgEAAAAAQKSDeD8F7f654IMDeAAAAAAAECN94tPT0+2ee+5xI9I3btzYLRrITiPW63cAAAAAACBGWuJvv/12e/755+3BBx+0U0891a378ssv3RRzBw4csPvvv78wygkAAAAAQNwLO4ifOnWqG7Tu/PPPD6w78cQT7aijjrJrr72WIB4AAAAAgFhJp9++fbsbfT4zrdPvitpvv/1mXbp0sZYtW7qbCa+//nqRlwEAAAAAgJgM4tu0aWNPPvlklvVap98VNU1vN3HiREtJSbHZs2fbTTfdZHv37i3ycgAAAAAAEHPp9OPGjbPzzjvPPv74Y+vcubNbN2/ePNci/v7771tRq1evnlukbt26VrNmTZcRUKFChSIvCwAAAAAAMdUSf+aZZ9qKFSvsggsusJ07d7qlX79+tnz5cjv99NPDLsDcuXOtd+/eVr9+fUtISLCZM2dm2SY5OdmaNGliZcuWtU6dOtn8+fOz3deiRYssLS3NGjZsGHY5AAAAAAAolvPEK+CO1AB2Sn1XGv7gwYPdzYDMpk+fbsOGDbNJkya5AF6p8z169HA3DWrXrh3YTq3vV155pT377LMRKRcAAAAAAJ4M4pctWxbyDjW4XDh69erllpxMmDDBhg4daoMGDXKPFcy/9957NnnyZBs5cqRbd/DgQevbt697fMopp+T6etpWi19qaqr7X3Pcx/I89yqbz8x8lpC/5/v+fF4Mv0cUT67u+nwx/f0CckL9hVdRd+Fl1F/EQ91NL0D9DimIb9u2rUt1V4Fyo22Uzh4phw4dcinyo0aNCqwrUaKEde/e3fXDF5Vp4MCBdtZZZ9kVV1yR5z7Hjh1rd999d5b1W7dudfPcxyp9yEfKVLMEF8Tn/jlkZ4tV/POHLZEvHJBH3d21a5f7rur7C3gJ9RdeRd2Fl1F/EQ91d/fu3YUbxK9Zs8aiYdu2be6mQJ06dTKs1+Off/7Z/fzVV1+5lHtlAPj707/00kvWunXrbPepGwJKzw9uiVcf+lq1alnlypUtlivEikM7rMz+zZaQjyC+tlX/84f/74IAFFXd1Q0+fcf4Qwyvof7Cq6i78DLqL+Kh7pYtW7Zwg/jGjRtbrDrttNPCSkVITEx0S2Y6yLF+klAbvAL4/ATxJRL+fE6Mv0cUTzqZeeE7BmSH+guvou7Cy6i/KO51t0QB6nZIz/zmm29C3uG+ffvsxx9/tEjQdHElS5a0zZs3Z1ivx5pODgAAAACAeBJSEK++5hoR/vXXX3ejyWcnJSXFbrvtNmvWrJnrxx4JZcqUsXbt2tmcOXMC69Tqrsf+OeoBAAAAAIgXIaXTK0B/+umn7Y477rBLL73UWrRo4aaZUx7/jh07XP/0PXv2uLnjZ8+enWN/9OzoeatWrcrQ//67776z6tWrW6NGjVz/9QEDBlj79u2tY8eOboo53Ujwj1afX5p7XkskB+IDAAAAAKAwJfjyGnI+k4ULF9qXX35pa9eutf3797uU95NOOsm6du3qAu9wffbZZ+65mSlwnzJlivv5ySeftPHjx9umTZvcSPmPP/64mzM+EjSwXZUqVdwogrE+sN0Xz4+0xP2b8tUnvlPTPz+b3o9FvnBAHnV3y5YtVrt2bfq1wXOov/Aq6i68jPqLeKi7qQWIQ0NqiQ+mFnEtkdKlS5c8p6677rrr3AIAAAAAQDzj1hYAAAAAAB5BEA8AAAAAgEfEbRCvQe1atmxpHTp0iHZRAAAAAAAISdwG8UlJSW7U/QULFkS7KAAAAAAAFE4Qv3r16nCfAgAAAAAAohHEH3PMMW5KuP/85z924MCBSJQBAAAAAAAURhC/ePFiO/HEE23YsGFWt25du/rqq23+/Pnh7gYAAAAAABR2EN+2bVt77LHHbMOGDTZ58mTbuHGjnXbaadaqVSubMGGCbd26NdxdAgAAAACAwhzYrlSpUtavXz97/fXX7aGHHrJVq1bZLbfcYg0bNrQrr7zSBfexjNHpAQAAAABxE8QvXLjQrr32WqtXr55rgVcA/8svv9hHH33kWun79OljsYzR6QEAAAAAXlMq3CcoYH/hhRds+fLldu6559qLL77o/i9R4n/3A5o2bWpTpkyxJk2aFEZ5AQAAAACIW2EH8U8//bQNHjzYBg4c6Frhs1O7dm17/vnnI1E+AAAAAACQ3yB+5cqVeW5TpkwZGzBgQLi7BgAAAAAAkewTr1R6DWaXmdZNnTo13N0BAAAAAIDCCuLHjh1rNWvWzDaF/oEHHgh3dwAAAAAAoLCC+HXr1rnB6zJr3Lix+x0AAAAAAIiRIF4t7suWLcuyfunSpVajRg3zCuaJBwAAAAAU+yD+kksusRtuuME+/fRTS0tLc8snn3xiN954o1188cXmFcwTDwAAAAAo9qPT33vvvfbrr79at27drFSp/z09PT3drrzySvrEAwAAAAAQS0G8po+bPn26C+aVQl+uXDlr3bq16xMPAAAAAABiKIj3a9GihVsAAAAAAECMBvHqAz9lyhSbM2eObdmyxaXSB1P/eAAAAAAAEANBvAawUxB/3nnnWatWrSwhIaEQigUAAAAAAAocxE+bNs1ee+01O/fcc8N9KgAAAAAAKMop5jSw3THHHGNexzzxAAAAAIBiH8QPHz7cHnvsMfP5fOZlzBMPAAAAACj26fRffvmlffrpp/bBBx/YCSecYKVLl87w+xkzZkSyfAAAAAAAIL9BfNWqVe2CCy4I92kAAAAAAKCog/gXXnihoK8JAAAAAACKok+8HDlyxD7++GP797//bbt373brNmzYYHv27MnP7gAAAAAAQGG0xK9du9Z69uxp69ats4MHD9rZZ59tlSpVsoceesg9njRpUri7BAAAAAAAhdESf+ONN1r79u1tx44dVq5cucB69ZOfM2dOuLsDAAAAAACF1RL/xRdf2Ndff+3miw/WpEkT+/3338PdHQAAAAAAKKyW+PT0dEtLS8uyfv369S6tHgAAAAAAxEgQf84559jEiRMDjxMSEtyAdmPGjLFzzz030uUDAAAAAAD5Tad/5JFHrEePHtayZUs7cOCAXXrppbZy5UqrWbOmvfrqq+YVycnJbskuqwAAAAAAgGIRxDdo0MCWLl1q06ZNs2XLlrlW+CFDhthll12WYaC7WJeUlOSW1NRUq1KlSrSLAwAAAABA5IN496RSpezyyy/Pz1MBAAAAAEBRBfEvvvhirr+/8sor81sWAAAAAAAQySBe88QHO3z4sO3bt89NOVe+fHmCeAAAAAAAYmV0+h07dmRY1Cd++fLldtppp3lqYDsAAAAAAIp9EJ+d5s2b24MPPpillR4AAAAAAMRYEO8f7G7Dhg2R2h0AAAAAAChon/h33nknw2Ofz2cbN260J5980k499dRwdwcAAAAAAAoriO/bt2+GxwkJCVarVi0766yz7JFHHgl3dwAAAAAAoLCC+PT09HCfAgAAAAAAYqlPPAAAAAAAiLGW+GHDhoW87YQJE8LdPQAAAAAAiFQQv2TJErccPnzYjj32WLduxYoVVrJkSTv55JMz9JUHAAAAAABRDOJ79+5tlSpVsqlTp1q1atXcuh07dtigQYPs9NNPt+HDh5sXJCcnuyUtLS3aRQEAAAAAoHCCeI1AP3v27EAAL/r5vvvus3POOcczQXxSUpJbUlNTrUqVKtEuDgDAA0bN+D7fzx3br3VEywIAAOJT2APbKejdunVrlvVat3v37kiVCwAAAAAAFDSIv+CCC1zq/IwZM2z9+vVuefPNN23IkCHWr1+/cHcHAAAAAAAKK51+0qRJdsstt9ill17qBrdzOylVygXx48ePD3d3AAAAAACgsIL48uXL21NPPeUC9l9++cWta9asmVWoUCHcXQEAAAAAgMJMp/fbuHGjW5o3b+4CeJ/Pl99dAQAAAACAwgji//jjD+vWrZu1aNHCzj33XBfIi9LpvTIyPQAAAAAAcRHE33zzzVa6dGlbt26dS63369+/v82aNSvS5QMAAAAAAPntE6854j/88ENr0KBBhvVKq1+7dm24uwMAAAAAAIXVEr93794MLfB+27dvt8TExHB3BwAAAAAACiuIP/300+3FF18MPE5ISLD09HQbN26cde3aNdzdAQAAAACAwkqnV7Cuge0WLlxohw4dshEjRtiPP/7oWuK/+uqrcHcHAAAAAAAKqyW+VatWtmLFCjvttNOsT58+Lr2+X79+tmTJEjdfPAAAAAAAiIGW+MOHD1vPnj1t0qRJdvvttxdSkQAAAAAAQIFb4jW13LJly8J5CgAAAAAAiFY6/eWXX27PP/98pF4fAAAAAAAU1sB2R44cscmTJ9vHH39s7dq1swoVKmT4/YQJE8LdJQAAAAAAKIwg/ocffrCTTz7Z/awB7oJpujkAAAAAABDlIH716tXWtGlT+/TTT604SE5OdktaWlq0iwIAAAAAQGT7xDdv3ty2bt0aeNy/f3/bvHmzeVVSUpKlpKTYggULol0UAAAAAAAi2xLv8/kyPH7//fdt7NixoT4dAADk06gZ3+f7uWP7tfbMawIAgEIYnR4AAAAAAMR4EK9B6zIPXMdAdgAAAAAAxGg6/cCBAy0xMdE9PnDggP3zn//MMsXcjBkzIl9KAAAQF0jjBwAgQkH8gAEDMjy+/PLLQ30qAAAAAAAoyiD+hRdeiMTrAQAAAACAwg7iAQBAfKWne+k1AQCIF4xODwAAAACAR9ASDwBAEaB1GgAARAIt8QAAAAAAeARBPAAAAAAAHkEQDwAAAACARxDEAwAAAADgEQTxAAAAAAB4BEE8AAAAAAAeQRAPAAAAAIBHEMQDAAAAAOARBPEAAAAAAHhEqWgXAACAeNN3/bgC72NmgxERKQsAAPAWWuIBAAAAAPAIgngAAAAAADyCIB4AAAAAAI8giAcAAAAAwCMI4gEAAAAA8AiCeAAAAAAAPKJYBPEXXHCBVatWzf7+979HuygAAAAAABSaYjFP/I033miDBw+2qVOnRrsoAAAUCeaaBwAgPhWLlvguXbpYpUqVol0MAAAAAACKdxA/d+5c6927t9WvX98SEhJs5syZWbZJTk62Jk2aWNmyZa1Tp042f/78qJQVAAAAAIC4DuL37t1rbdq0cYF6dqZPn27Dhg2zMWPG2OLFi922PXr0sC1bthR5WQEAAAAAiOs+8b169XJLTiZMmGBDhw61QYMGuceTJk2y9957zyZPnmwjR44M+/UOHjzoFr/U1FT3f3p6ultilcrmMzOfJeTv+b4/nxfD7xHFk6u7Pl9Mf7+A0OuvzsQFl99zeeRF5v3ECs4z/49zL7yM+ot4qLvpBajfUQ/ic3Po0CFbtGiRjRo1KrCuRIkS1r17d5s3b16+9jl27Fi7++67s6zfunWrHThwwGKVPuQjZapZgrvwC/+ia4tV/PMHMhhQ9HV3165d7oSm7y/g5fpb1fZFZL8Hy9W1WBCp9xMryNL7f5x74WXUX8RD3d29e3fxDOK3bdtmaWlpVqdOnQzr9fjnn38OPFZQv3TpUpea36BBA3v99detc+fO2e5TNwSUnh/cEt+wYUOrVauWVa5c2WK5Qqw4tMPK7N9sCfkI4mtb9T9/qB35wgF51F2Nd6HvGH+I4fX6u9MiEyQm7t9ksWCnlbfipDZ/4wI498LLqL+Ih7pbtmzZ4hnEh+rjjz8OedvExES3ZKaDHOsnCbXBK4DPTxBfIuHP58T4e0TxpJOZF75jQN71NzJp8Pk5jxeOWEnrjwzOMRlx7oWXUX9R3OtuiQLU7Zj+VtSsWdNKlixpmzdvzrBej+vWjY1URAAAAAAAikpMB/FlypSxdu3a2Zw5czKkKOhxTunyAAAAAAAUV1FPp9+zZ4+tWrUq8HjNmjX23XffWfXq1a1Ro0au//qAAQOsffv21rFjR5s4caLr++4frT6/NKWdFvW5BwDEl1Ezvg9xS58b/O1/feGLV+p5fH+ukTO2X+uolLcgrwsA8LaoB/ELFy60rl27Bh77B51T4D5lyhTr37+/Gzl+9OjRtmnTJmvbtq3NmjUry2B34UpKSnKLBrarUqVKgd8HAAAAAADFPojv0qWLG4I/N9ddd51bAAAAAACIZzHdJx4AAAAAAPw/gngAAAAAADwi6un00cLAdgAAAMWb1wY7BIBQxG1LvAa1S0lJsQULFkS7KAAAAAAAhCRug3gAAAAAALyGIB4AAAAAAI8giAcAAAAAwCMI4gEAAAAA8AiCeAAAAAAAPIIp5phiDgAAeGgKs2iVOVpTp3ntGBekvExPByAUcdsSzxRzAAAAAACvidsgHgAAAAAAryGIBwAAAADAIwjiAQAAAADwCIJ4AAAAAAA8giAeAAAAAACPYIo5ppgDACDf+q4fV+B9zGwwIiJlQezy2jRxABDL4rYlninmAAAAAABeE7dBPAAAAAAAXkMQDwAAAACARxDEAwAAAADgEQTxAAAAAAB4BEE8AAAAAAAeQRAPAAAAAIBHME8888QDAIp4XnREFnPVAwDiSdy2xDNPPAAAAADAa+I2iAcAAAAAwGsI4gEAAAAA8AiCeAAAAAAAPIIgHgAAAAAAjyCIBwAAAADAIwjiAQAAAADwCIJ4AAAAAAA8giAeAAAAAACPIIgHAAAAAMAjSlmcSk5OdktaWlq0iwIAAOD0XT8upO2+fTzn381sMMLMfFbV9tlO22JmCZErIAAg6uK2JT4pKclSUlJswYIF0S4KAAAAAAAhidsgHgAAAAAAryGIBwAAAADAIwjiAQAAAADwCIJ4AAAAAAA8giAeAAAAAACPIIgHAAAAAMAjCOIBAAAAAPAIgngAAAAAADyCIB4AAAAAAI8giAcAAAAAwCMI4gEAAAAA8AiCeAAAAAAAPKKUxank5GS3pKWlRbsoAABERd/146JdhJjBscj7eHz7eHj7mNlgROQKhDyNmvF9vp43tl/riJcFQOGK25b4pKQkS0lJsQULFkS7KAAAAAAAhCRug3gAAAAAALyGIB4AAAAAAI8giAcAAAAAwCMI4gEAAAAA8AiCeAAAAAAAPIIgHgAAAAAAjyCIBwAAAADAIwjiAQAAAADwCIJ4AAAAAAA8giAeAAAAAACPIIgHAAAAAMAjCOIBAAAAAPAIgngAAAAAADyCIB4AAAAAAI8giAcAAAAAwCMI4gEAAAAA8AiCeAAAAAAAPKKUxank5GS3pKWlRbsoAICi8t8b3X99128PaXOfJdjBcnUtcf8mSzBfIRcufvVdPy7aRUAxrhszG4wwrxg14/toFwGAB8RtS3xSUpKlpKTYggULol0UAAAAAABCErdBPAAAAAAAXkMQDwAAAACARxDEAwAAAADgEQTxAAAAAAB4BEE8AAAAAAAeQRAPAAAAAIBHEMQDAAAAAOARBPEAAAAAAHgEQTwAAAAAAB5BEA8AAAAAgEcQxAMAAAAA4BEE8QAAAAAAeARBPAAAAAAAHkEQDwAAAACARxDEAwAAAADgEQTxAAAAAAB4BEE8AAAAAAAeQRAPAAAAAIBHEMQDAAAAAOARBPEAAAAAAHgEQTwAAAAAAB5BEA8AAAAAgEcQxAMAAAAA4BEE8QAAAAAAeARBPAAAAAAAHlEsgvh3333Xjj32WGvevLk999xz0S4OAAAAAACFopR53JEjR2zYsGH26aefWpUqVaxdu3Z2wQUXWI0aNaJdNAAAAAAAIsrzLfHz58+3E044wY466iirWLGi9erVy2bPnh3tYgEAAAAAUPyC+Llz51rv3r2tfv36lpCQYDNnzsyyTXJysjVp0sTKli1rnTp1coG734YNG1wA76eff//99yIrPwAAAAAAcRPE792719q0aeMC9exMnz7dpcuPGTPGFi9e7Lbt0aOHbdmypcjLCgAAAABAXPeJV/q7lpxMmDDBhg4daoMGDXKPJ02aZO+9955NnjzZRo4c6Vrwg1ve9XPHjh1z3N/Bgwfd4peamur+T09Pd0usUtl8ZuazhPw93/fn82L4PaJ4cnXX54vp7xfiyJ/nwlDPpdquIOdeIDp8mZb87CESdT5/rx2L5SjOYvHvM9cO8Kr0MOpuQep31IP43Bw6dMgWLVpko0aNCqwrUaKEde/e3ebNm+ceK2D/4YcfXPCuge0++OADu/POO3Pc59ixY+3uu+/Osn7r1q124MABi1X6kI+UqWYJ7o9Z+H+MtljFP38ggwFFX3d37drlTmj6/gLRVd39e7BcmRC3T7AjZarm+9wLRENV2+f+r2iHXB3Oj4Pl6ha4HD3/+E+B9xGJcviPR0H85Y8ZFm3f1OhXKPsdP/P/u6mGeyyOrfPn9WW4Ol5lU7/+NddNKtpB22Nrs/3dgFOa5Otl83rN3OT3NRGm+c8UfB8drzIvXPfu3r27eAbx27Zts7S0NKtTp06G9Xr8888/u59LlSpljzzyiHXt2tUdtBEjRuQ6Mr1uCCg9P7glvmHDhlarVi2rXLmyxSq9txWHdliZ/Zv/bBcKT+0/L1ytdu3IFw7Io+5qvAt9xwjiEX3b3b+/7v/f/6G1xPvyfe4FomGnlQ+0wu+0cvkK5BP3b7LidTwKJhaORyTeRyQEH4vA9WW4ate2nZZbw1Lu9bd2Pq9nc3/N3OX3NRGu0P4+5yqKn1U4170a761YBvGhOv/8890SisTERLdkpoMc6wGGTmG6iMzPhWSJhD+fE+PvEcWTTmZe+I4hDvx5LgznPFqQcy8QHQkZam9+gvjiVd8LnpIfG8cjNrr1BB+LwPVluNz1QF7vJ+f6m//rifwfQ65hikh+61SwKH9WoV73FqROxXRtrFmzppUsWdI2b96cYb0e161b8PQqAAAAAAC8JKaD+DJlyli7du1szpw5GVIU9Lhz585RLRsAAAAAAEUt6un0e/bssVWrVgUer1mzxr777jurXr26NWrUyPVfHzBggLVv394NYjdx4kQ3LZ1/tPr80pR2WtTnHgAAAAAAL4h6EL9w4UI3KJ2ff9A5Be5Tpkyx/v37u5HjR48ebZs2bbK2bdvarFmzsgx2F66kpCS3aGA7jWoPAAAAAECsi3oQ36VLFzcEf26uu+46twAAAAAAEM9iuk88AAAAAAD4fwTxAAAAAAB4RNwG8RrUrmXLltahQ4doFwUAAAAAgJDEbRCvQe1SUlJswYIF0S4KAAAAAAAhidsgHgAAAAAAryGIBwAAAADAIwjiAQAAAADwCIJ4AAAAAAA8giAeAAAAAACPiNsgninmAAAAAABeE7dBPFPMAQAAAAC8Jm6DeAAAAAAAvKaUxTmfz+f+T01NtViWnp5ue/cftCMHDlmC/a/M4Ujdd/DPH2L7faL4Ud3dvXu3lS1b1kqU4L4houzPc+HeA4dC2txnCXbQ8n/uBaLh4L49rvYesP120NLNLCHsfYT6HfHO8SiYWDgekXgfkRB8LALXl+FKTc3j/eRef/N73V6QYxjrsUKxkd86FSyKn1U4173+OuWPR8OR4MvPs4qR9evXW8OGDaNdDAAAAABAnPntt9+sQYMGYT0n7oN43S3ZsGGDVapUyRISwr9TXVR0p0Y3G/QhV65cOdrFAUJG3YWXUX/hVdRdeBn1F/FQd30+n2u1r1+/ftjZqnGfTq8DFu6dj2hSZeBkBi+i7sLLqL/wKuouvIz6i+Jed6tUqZKv/dNBFQAAAAAAjyCIBwAAAADAIwjiPSIxMdHGjBnj/ge8hLoLL6P+wquou/Ay6i+8KrGI6m7cD2wHAAAAAIBX0BIPAAAAAIBHEMQDAAAAAOARBPEAAAAAAHgEQTwAAAAAAB5BEB8lycnJ1qRJEytbtqx16tTJ5s+fn+v2r7/+uh133HFu+9atW9v777+f4fcan3D06NFWr149K1eunHXv3t1WrlxZyO8C8SrS9XfgwIGWkJCQYenZs2chvwvEo3Dq7o8//mh/+9vf3PaqkxMnTizwPoFYqr933XVXlnOvztVANOvus88+a6effrpVq1bNLbqmzbw9173wcv2NxHUvQXwUTJ8+3YYNG+amH1i8eLG1adPGevToYVu2bMl2+6+//touueQSGzJkiC1ZssT69u3rlh9++CGwzbhx4+zxxx+3SZMm2bfffmsVKlRw+zxw4EARvjPEg8Kov6KT18aNGwPLq6++WkTvCPEi3Lq7b98+O/roo+3BBx+0unXrRmSfQCzVXznhhBMynHu//PLLQnwXiEfh1t3PPvvMXTd8+umnNm/ePGvYsKGdc8459vvvvwe24boXXq6/Ebnu1RRzKFodO3b0JSUlBR6npaX56tev7xs7dmy221900UW+8847L8O6Tp06+a6++mr3c3p6uq9u3bq+8ePHB36/c+dOX2Jiou/VV18ttPeB+BTp+isDBgzw9enTpxBLDYRfd4M1btzY9+ijj0Z0n0C06++YMWN8bdq0iXhZgUieJ48cOeKrVKmSb+rUqe4x173wcv2N1HUvLfFF7NChQ7Zo0SKXWuFXokQJ91h3a7Kj9cHbi+4A+bdfs2aNbdq0KcM2VapUcekeOe0TiJX6G3znsnbt2nbsscfaNddcY3/88UchvQvEo/zU3WjsEyjquqYU5Pr167tW+8suu8zWrVsXgRIDkau7yio5fPiwVa9e3T3muhderr+Ruu4liC9i27Zts7S0NKtTp06G9XqsE1J2tD637f3/h7NPIFbqrz+l6MUXX7Q5c+bYQw89ZJ9//rn16tXLvRYQrbobjX0CRVnXFPRMmTLFZs2aZU8//bQLjtSXc/fu3REoNRCZuvuvf/3L3WjyB1Jc98LL9TdS172lwngfAFAoLr744sDPGvjuxBNPtGbNmrm7lN26dYtq2QCguNJFo5/OuwrqGzdubK+99pobxwSINo3pMG3aNHc9oEHFgOJQfy+OwHUvLfFFrGbNmlayZEnbvHlzhvV6nNPAM1qf2/b+/8PZJxAr9Tc7SuvUa61atSpCJUe8y0/djcY+gWjWtapVq1qLFi049yIm6u7DDz/sgqDZs2e7IMeP6154uf5G6rqXIL6IlSlTxtq1a+fSJ/zS09Pd486dO2f7HK0P3l4++uijwPZNmzZ1FSl4m9TUVDdaZ077BGKl/mZn/fr1rm+Qpo4BolV3o7FPIJp1bc+ePfbLL79w7kXU665Gn7/33ntdV4/27dtn+B3XvfBy/Y3YdW+BhsVDvkybNs2NoDllyhRfSkqK76qrrvJVrVrVt2nTJvf7K664wjdy5MjA9l999ZWvVKlSvocfftj3008/udFkS5cu7fv+++8D2zz44INuH2+//bZv2bJlbsTDpk2b+vbv3x+V94jiK9L1d/fu3b5bbrnFN2/ePN+aNWt8H3/8se/kk0/2NW/e3HfgwIGovU8UP+HW3YMHD/qWLFnilnr16rl6qp9XrlwZ8j6BWK6/w4cP93322Wfu3Ktzdffu3X01a9b0bdmyJSrvEcVTuHVX17RlypTxvfHGG76NGzcGFl0vBG/DdS+8WH8jdd1LEB8lTzzxhK9Ro0buQ9bUBd98803gd2eeeaabeiDYa6+95mvRooXb/oQTTvC99957GX6v6TbuvPNOX506dVxF69atm2/58uVF9n4QXyJZf/ft2+c755xzfLVq1XLBvaZCGjp0KEEQol539cdV97ozL9ou1H0CsVx/+/fv7wJ87e+oo45yj1etWlXk7wvFXzh1V9cB2dVdNQL4cd0Lr9bfSF33Juif0NvtAQAAAABAtNAnHgAAAAAAjyCIBwAAAADAIwjiAQAAAADwCIJ4AAAAAAA8giAeAAAAAACPIIgHAAAAAMAjCOIBAAAAAPAIgngAAAAAADyCIB4AABS5X3/91RISEuy7776LdlEAAPAUgngAAPJp4MCB1rdv3yzrP/vsMxeg7ty5M+R9denSxW666aaIlOvZZ5+1Nm3aWMWKFa1q1ap20kkn2dixY604WLp0qZ1//vlWu3ZtK1u2rDVp0sT69+9vW7ZsiXbRAAAoEqWK5mUAAEBRmDx5srsZ8Pjjj9uZZ55pBw8etGXLltkPP/xgXrd161br1q2b/fWvf7UPP/zQ3aBQi/4777xje/fuLbTXPXz4sJUuXbrQ9g8AQDhoiQcAoJD98ccfdskll9hRRx1l5cuXt9atW9urr76aoUX/888/t8cee8y14GtRcCoKvnv16uVa1evUqWNXXHGFbdu2LcfXUkB70UUX2ZAhQ+yYY46xE044wb32/fffnyWD4O6777ZatWpZ5cqV7Z///KcdOnQosE16erprvW/atKmVK1fOtey/8cYbGV4rr7JpH+PGjXPlSExMtEaNGmUoh6xevdq6du3qjoteY968eTm+t6+++sp27dplzz33nMsuUNn03EcffdT97Pfjjz+6QF/vq1KlSnb66afbL7/8EijTPffcYw0aNHBlatu2rc2aNStLmv/06dPdTRC19r/88svud3rd448/3q077rjj7KmnnsqxrAAAFBaCeAAACtmBAwesXbt29t5777nA96qrrnIB7/z5893vFbx37tzZhg4dahs3bnRLw4YNXTr+WWed5QLWhQsXumBz8+bNLkjPSd26de2bb76xtWvX5lqmOXPm2E8//eRS/3VDYcaMGS6o91MA/+KLL9qkSZNcUHzzzTfb5Zdf7m42SChlGzVqlD344IN25513WkpKir3yyisu2A92++232y233OL6xrdo0cLdcDhy5EiO702/e+utt8zn82W7ze+//25nnHGGC9A/+eQTW7RokQ0ePDiwTx3rRx55xB5++GGXodCjRw+Xnr9y5coM+xk5cqTdeOON7hhpGwXyo0ePdjchtO6BBx5w72vq1Km5HmcAACLOBwAA8mXAgAG+kiVL+ipUqJBhKVu2rCJM344dO3J87nnnnecbPnx44PGZZ57pu/HGGzNsc++99/rOOeecDOt+++03t+/ly5dnu98NGzb4/vKXv7htWrRo4co4ffp0X1paWoZyV69e3bd3797AuqefftpXsWJFt92BAwd85cuX93399dcZ9j1kyBDfJZdcElLZUlNTfYmJib5nn30223KuWbPGbfvcc88F1v34449u3U8//ZTjcbvtttt8pUqVcuXv2bOnb9y4cb5NmzYFfj9q1Chf06ZNfYcOHcr2+fXr1/fdf//9GdZ16NDBd+2112Yo18SJEzNs06xZM98rr7ySYZ2OQefOnXMsKwAAhYE+8QAAFIDSuZ9++ukM67799lvXau2XlpbmWm5fe+0111KstHX1VVcKeV6DuH366acuXT0zpYer5TqzevXquZR0tfjPnTvXvv76axswYIBLBVdreYkS/0vCU+p68OsrE2DPnj3222+/uf/37dtnZ599doZ9q9xqeQ+lbGqp13tUH/bcnHjiiRnKLhqkTunq2VFL+LBhw1wru46zMgV0bPVe1U1BLfpKn8+uD3tqaqpt2LDBTj311Azr9VjvJ1j79u0DP6u/vd6TuigoW8JPrftVqlTJ9f0BABBpBPEAABRAhQoVXJ/vYOvXr8/wePz48S6Ne+LEiS7Q1HM0+FxwH/TsKJju3bu3PfTQQ1l+5w94c9KqVSu3XHvtta6/uwJbpcLrpkNe9Lqi9H/14w+mNPVQyqa+7qEIDrbVF93fbz03NWrUsAsvvNAtCuB1Y0Hp8UptV//9SNBnlPl4aNT/Tp06ZdiuZMmSEXk9AABCRRAPAEAh04Bsffr0CbTOK0hdsWKFtWzZMrBNmTJlXIt9sJNPPtnefPNNN41aqVL5/5Ptf53gEdzV8rx///5A0Kt+9GpVV1/86tWru2B93bp1bnC37ORVtubNm7t9q+/9P/7xDyssOm7NmjULvDe17CuYz25EeQ10V79+ffd5BL8vPe7YsWOOr6F+/HqebkxcdtllhfZeAAAIBQPbAQBQyBTQfvTRRy61XYOiXX311W4QuGAKhpUertHRNcK7Av2kpCTbvn27G+xtwYIFLqVbU6sNGjQoS8Dvd80119i9997rAlMNbqfg/Morr3Sj0Ctl3k9ZAEoP14Bz77//vo0ZM8auu+46l26vEd012JwGs1NArNddvHixPfHEE4GB3PIqm0Zw/9e//mUjRoxwA+Tp9yrL888/n+/j+O6777obIfpfN0GWL1/uWuBVft0kEb0Hpc1ffPHFbsA9DVj30ksvuW3l1ltvddkDGn1e6zSAnVLwNYhdbjTonwb709R9eu3vv//eXnjhBZswYUK+3w8AAPlBSzwAAIXsjjvucK24GuVc/dA1Or2meNN0aX4KmtV3Xa3maiFfs2aNC+wVjCsYPuecc1wf88aNG1vPnj0Dfdsz6969u5srXv30NbVdzZo1XfCuFnGlofupr7puLmgkd+1Xwfhdd90V+L1uBCjwV+CqsmtOdrW+33bbbe73/hbt3Mqm0dvVSq9R3dUXXWn2Su3PLx0bHb/hw4e7vvvKFtB7UH9/jfYveo/qL69gXa3tSnfXNHL+fvA33HCDO+7ah/rea5+alk/7yY2yCfTa6hqhfSvdXl0j1C0CAICilKDR7Yr0FQEAQFRpnngNPDdz5sxoFwUAAISJdHoAAAAAADyCIB4AAAAAAI8gnR4AAAAAAI+gJR4AAAAAAI8giAcAAAAAwCMI4gEAAAAA8AiCeAAAAAAAPIIgHgAAAAAAjyCIBwAAAADAIwjiAQAAAADwCIJ4AAAAAADMG/4PhD0V4ZH94U4AAAAASUVORK5CYII=", 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+ "text/plain": [ + "
" + ] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Segment-level statistics for hate speech scores:\n", + "\n", + "Controversial:\n", + " Count: 2731\n", + " Mean: 0.0113\n", + " Median: 0.0000\n", + " Std Dev: 0.0376\n", + " Min: 0.0000\n", + " Max: 0.2104\n", + " 25th Percentile: 0.0000\n", + " 75th Percentile: 0.0000\n", + " 90th Percentile: 0.0000\n", + " 95th Percentile: 0.1193\n", + " 99th Percentile: 0.1689\n", + " High risk segments (> 0.5): 0 (0.00%)\n", + " Medium risk segments (0.1-0.5): 236 (8.64%)\n", + "\n", + "Lex Fridman:\n", + " Count: 7153\n", + " Mean: 0.0012\n", + " Median: 0.0000\n", + " Std Dev: 0.0126\n", + " Min: 0.0000\n", + " Max: 0.2409\n", + " 25th Percentile: 0.0000\n", + " 75th Percentile: 0.0000\n", + " 90th Percentile: 0.0000\n", + " 95th Percentile: 0.0000\n", + " 99th Percentile: 0.0000\n", + " High risk segments (> 0.5): 0 (0.00%)\n", + " Medium risk segments (0.1-0.5): 67 (0.94%)\n" + ] + } + ], + "id": "74e25bf5" }, { - "data": { - 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Tuning aggregation strategies with a simulation\n", + "\n", + "So far every episode was scored with the default aggregator (`skewness`). But Sentinel ships several \"summarize metrics\", and which one works best depends on your data. This section runs a small **simulation** (using `sentinel.simulation`) to compare them systematically on our labeled episodes.\n", + "\n", + "Two very different \"metrics\" are involved, and it helps to keep them separate:\n", + "\n", + "- **Summarize metric (the aggregation):** how an episode's many per-segment scores become a single number. Sentinel offers `skewness`, `mean_of_positives`, `top_k_mean`, `percentile_score`, `softmax_weighted_mean`, and `max_score`. This is *not* a ranking.\n", + "- **Evaluation metric:** given one number per episode plus a known label (controversial = 1, Lex Fridman = 0), how well are the two classes separated? Ranking is only one option. The harness reports **three families** so you can tune for whatever matters to you:\n", + " - **Ranking** (`roc_auc`, `recall_at_n`, `rank_ratio`): where do the known-positive episodes land in the leaderboard?\n", + " - **Threshold / classification** (`precision`, `recall`, `f1`): pick a cutoff on the per-episode score and count hits and misses.\n", + " - **Separation / distribution** (`mean_separation`, `cohens_d`, `ks_statistic`): threshold-free distance between the two score distributions.\n", + "\n", + "The expensive part (running the sentence model) is done once by `score_groups`; comparing aggregators and thresholds afterward is cheap." + ], + "id": "fb98a1fd" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Statistics for hate affinity scores:\n", - "\n", - "Controversial:\n", - " Mean: 0.0796\n", - " Median: 0.0000\n", - " Std Dev: 0.1947\n", - " Min: -0.5694\n", - " Max: 0.7799\n", - "\n", - "Lex Fridman:\n", - " Mean: 0.0639\n", - " Median: 0.0594\n", - " Std Dev: 0.0754\n", - " Min: 0.0000\n", - " Max: 0.3032\n" - ] - } - ], - "source": [ - "# Create a comparison visualization of the hate affinity scores between the 2 pdocasts\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Save the dataframes to CSV files\n", - "episode_scores_df.to_csv(\"data/controversial_podcast.csv\", index=False)\n", - "lex_episode_scores_df.to_csv(\"data/lex_fridman_episode_scores.csv\", index=False)\n", - "print(\"Saved episode scores to CSV files:\")\n", - "print(\"- controversial_podcast.csv\")\n", - "print(\"- lex_fridman_episode_scores.csv\")\n", - "\n", - "# Create a combined dataset for comparison\n", - "controversial_scores = pd.DataFrame({\n", - " 'platform': ['Controversial'] * len(episode_scores_df),\n", - " 'hate_affinity_score': episode_scores_df['hate_affinity_score']\n", - "})\n", - "\n", - "lex_scores = pd.DataFrame({\n", - " 'platform': ['Lex Fridman'] * len(lex_episode_scores_df),\n", - " 'hate_affinity_score': lex_episode_scores_df['hate_affinity_score']\n", - "})\n", - "\n", - "all_scores = pd.concat([controversial_scores, lex_scores], ignore_index=True)\n", - "all_scores.to_csv(\"data/all_platform_scores.csv\", index=False)\n", - "print(\"- all_platform_scores.csv\")\n", - "\n", - "# Create a boxplot comparison using matplotlib\n", - "plt.figure(figsize=(10, 6))\n", - "\n", - "# Extract data for each platform\n", - "platforms = all_scores['platform'].unique()\n", - "data = [all_scores[all_scores['platform'] == platform]['hate_affinity_score'] for platform in platforms]\n", - "\n", - "# Create box plot\n", - "box = plt.boxplot(data, patch_artist=True, labels=platforms)\n", - "colors = ['#ff9999', '#66b3ff']\n", - "for patch, color in zip(box['boxes'], colors):\n", - " patch.set_facecolor(color)\n", - "\n", - "plt.title('Hate Speech Affinity Score Comparison')\n", - "plt.ylabel('Hate Affinity Score')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.show()\n", - "\n", - "# Create histograms to compare distributions\n", - "plt.figure(figsize=(12, 6))\n", - "\n", - "# Extract data for each platform\n", - "for i, platform in enumerate(platforms):\n", - " platform_data = all_scores[all_scores['platform'] == platform]['hate_affinity_score']\n", - " plt.hist(platform_data, bins=20, alpha=0.6, label=platform)\n", - " \n", - "\n", - "plt.title('Distribution of Hate Speech Affinity Scores')\n", - "plt.xlabel('Hate Affinity Score')\n", - "plt.ylabel('Frequency')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "# Print statistics summary\n", - "print(\"\\nStatistics for hate affinity scores:\")\n", - "for platform in all_scores['platform'].unique():\n", - " platform_scores = all_scores[all_scores['platform'] == platform]['hate_affinity_score']\n", - " print(f\"\\n{platform}:\")\n", - " print(f\" Mean: {platform_scores.mean():.4f}\")\n", - " print(f\" Median: {platform_scores.median():.4f}\")\n", - " print(f\" Std Dev: {platform_scores.std():.4f}\")\n", - " print(f\" Min: {platform_scores.min():.4f}\")\n", - " print(f\" Max: {platform_scores.max():.4f}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "55dbe289", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved segment-level scores to CSV files:\n", - "- controversial_segment_scores.csv\n", - "- lex_fridman_segment_scores.csv\n", - "\n", - "High risk segments (score > 0.5):\n", - "- Controversial: 0 segments (0.00% of all segments)\n", - "- Lex Fridman: 0 segments (0.00% of all segments)\n", - "\n", - "Medium risk segments (0.1 < score <= 0.5):\n", - "- Controversial: 236 segments (8.64% of all segments)\n", - "- Lex Fridman: 67 segments (0.94% of all segments)\n" - ] - } - ], - "source": [ - "# Save segment-level scores to CSV files for further analysis\n", - "segment_scores_df.to_csv(\"data/controversial_segment_scores.csv\", index=False)\n", - "lex_segment_scores_df.to_csv(\"data/lex_fridman_segment_scores.csv\", index=False)\n", - "\n", - "print(\"Saved segment-level scores to CSV files:\")\n", - "print(\"- controversial_segment_scores.csv\")\n", - "print(\"- lex_fridman_segment_scores.csv\")\n", - "\n", - "# Count high risk segments in both datasets (score > 0.5)\n", - "controversial_high_risk = len(segment_scores_df[segment_scores_df['score'] > 0.5])\n", - "lex_high_risk = len(lex_segment_scores_df[lex_segment_scores_df['score'] > 0.5])\n", - "\n", - "print(f\"\\nHigh risk segments (score > 0.5):\")\n", - "print(f\"- Controversial: {controversial_high_risk} segments ({controversial_high_risk/len(segment_scores_df)*100:.2f}% of all segments)\")\n", - "print(f\"- Lex Fridman: {lex_high_risk} segments ({lex_high_risk/len(lex_segment_scores_df)*100:.2f}% of all segments)\")\n", - "\n", - "# Count medium risk segments (score between 0.1 and 0.5)\n", - "controversial_medium_risk = len(segment_scores_df[(segment_scores_df['score'] > 0.1) & (segment_scores_df['score'] <= 0.5)])\n", - "lex_medium_risk = len(lex_segment_scores_df[(lex_segment_scores_df['score'] > 0.1) & (lex_segment_scores_df['score'] <= 0.5)])\n", - "\n", - "print(f\"\\nMedium risk segments (0.1 < score <= 0.5):\")\n", - "print(f\"- Controversial: {controversial_medium_risk} segments ({controversial_medium_risk/len(segment_scores_df)*100:.2f}% of all segments)\")\n", - "print(f\"- Lex Fridman: {lex_medium_risk} segments ({lex_medium_risk/len(lex_segment_scores_df)*100:.2f}% of all segments)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "74e25bf5", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved all segment-level data to CSV files\n" - ] + "cell_type": "code", + "metadata": {}, + "source": [ + "import random\n", + "\n", + "import pandas as pd\n", + "\n", + "from sentinel.simulation import (\n", + " LabeledGroup,\n", + " score_groups,\n", + " compare_aggregators,\n", + " run_grid_search,\n", + ")\n", + "\n", + "# Build \"labeled groups\": each podcast episode is one group of text segments.\n", + "# label = 1 -> controversial (rare / positive class)\n", + "# label = 0 -> Lex Fridman (common / negative class)\n", + "controversial_groups = [\n", + " LabeledGroup(name=str(file_path), label=1, observations=group[\"segment\"].tolist())\n", + " for file_path, group in results_df.groupby(\"file\")\n", + "]\n", + "lex_groups = [\n", + " LabeledGroup(name=str(file_path), label=0, observations=group[\"segment\"].tolist())\n", + " for file_path, group in lex_results_df.groupby(\"file\")\n", + "]\n", + "\n", + "# Subsample the controversial episodes so the demo runs quickly (the model has to\n", + "# embed every segment of every group). Feel free to raise these caps.\n", + "random.seed(0)\n", + "controversial_sample = random.sample(\n", + " controversial_groups, min(30, len(controversial_groups))\n", + ")\n", + "groups = controversial_sample + lex_groups\n", + "\n", + "n_pos = sum(g.label == 1 for g in groups)\n", + "n_neg = sum(g.label == 0 for g in groups)\n", + "print(f\"Built {len(groups)} labeled groups: {n_pos} controversial (label=1), {n_neg} Lex Fridman (label=0)\")\n", + "\n", + "# EXPENSIVE STEP (runs the sentence model): score every segment once. The result\n", + "# is reused by every aggregator below, so we don't re-encode anything.\n", + "scored = score_groups(index, groups, top_k=5, show_progress_bar=True)\n", + "\n", + "# CHEAP STEP: compare all six summarize metrics on the same scores.\n", + "comparison = pd.DataFrame(compare_aggregators(scored))\n", + "comparison_view = (\n", + " comparison[\n", + " [\n", + " \"aggregator\",\n", + " \"roc_auc\",\n", + " \"recall_at_n\",\n", + " \"precision_at_n\",\n", + " \"rank_ratio\",\n", + " \"f1\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"mean_separation\",\n", + " \"cohens_d\",\n", + " \"ks_statistic\",\n", + " ]\n", + " ]\n", + " .sort_values(\"roc_auc\", ascending=False)\n", + " .reset_index(drop=True)\n", + ")\n", + "comparison_view" + ], + "execution_count": null, + "outputs": [], + "id": "a9f35cf0" }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/ph/f73k11ns3_v8n6g3lrdb792w0000gr/T/ipykernel_89229/3057986402.py:33: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", - " box = plt.boxplot(segment_data, patch_artist=True, labels=platforms)\n" - ] + "cell_type": "code", + "metadata": {}, + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Show one representative metric from each evaluation family, per aggregator.\n", + "# We use separate subplots because the families live on different scales\n", + "# (roc_auc and f1 are in [0, 1], while Cohen's d can exceed 1).\n", + "families = [\"roc_auc\", \"f1\", \"cohens_d\"]\n", + "family_titles = {\n", + " \"roc_auc\": \"Ranking (ROC-AUC)\",\n", + " \"f1\": \"Threshold (best F1)\",\n", + " \"cohens_d\": \"Separation (Cohen's d)\",\n", + "}\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "for ax, family in zip(axes, families):\n", + " values = comparison.set_index(\"aggregator\")[family]\n", + " values.plot(kind=\"bar\", ax=ax, color=\"steelblue\", alpha=0.85, edgecolor=\"white\")\n", + " ax.set_title(family_titles[family])\n", + " ax.set_xlabel(\"\")\n", + " ax.tick_params(axis=\"x\", rotation=30)\n", + " ax.grid(True, axis=\"y\", alpha=0.3)\n", + " ax.set_axisbelow(True)\n", + "\n", + "fig.suptitle(\"Aggregator comparison across the three evaluation families\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [], + "id": "da17416d" }, { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "cell_type": "code", + "metadata": {}, + "source": [ + "# Grid search: sweep top_k (each value re-scores the segments) and the\n", + "# per-observation threshold min_score_to_consider (applied cheaply), for every\n", + "# aggregator. Returns one row per (top_k, min_score, aggregator) combination.\n", + "grid = pd.DataFrame(\n", + " run_grid_search(\n", + " index,\n", + " groups,\n", + " top_k_values=[3, 5, 10],\n", + " min_score_values=[0.0, 0.1, 0.25],\n", + " )\n", + ")\n", + "\n", + "# Which single configuration separates the two classes best (by ROC-AUC)?\n", + "best = grid.sort_values(\"roc_auc\", ascending=False).reset_index(drop=True)\n", + "print(\"Top configurations by ROC-AUC:\")\n", + "best[[\"aggregator\", \"top_k\", \"min_score_to_consider\", \"roc_auc\", \"f1\", \"cohens_d\"]].head(10)" + ], + "execution_count": null, + "outputs": [], + "id": "b196a11d" }, { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" + "cell_type": "code", + "metadata": {}, + "source": [ + "# Heatmap of ROC-AUC over (top_k x min_score_to_consider) for the best aggregator,\n", + "# so we can pick a robust configuration rather than a single lucky point.\n", + "best_aggregator = best.iloc[0][\"aggregator\"]\n", + "sub = grid[grid[\"aggregator\"] == best_aggregator]\n", + "pivot = sub.pivot(index=\"top_k\", columns=\"min_score_to_consider\", values=\"roc_auc\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "im = ax.imshow(pivot.values, cmap=\"viridis\", aspect=\"auto\")\n", + "ax.set_xticks(range(len(pivot.columns)))\n", + "ax.set_xticklabels(pivot.columns)\n", + "ax.set_yticks(range(len(pivot.index)))\n", + "ax.set_yticklabels(pivot.index)\n", + "ax.set_xlabel(\"min_score_to_consider\")\n", + "ax.set_ylabel(\"top_k\")\n", + "ax.set_title(f\"ROC-AUC heatmap for '{best_aggregator}'\")\n", + "for i in range(len(pivot.index)):\n", + " for j in range(len(pivot.columns)):\n", + " ax.text(j, i, f\"{pivot.values[i, j]:.3f}\", ha=\"center\", va=\"center\", color=\"white\")\n", + "fig.colorbar(im, ax=ax, label=\"ROC-AUC\")\n", + "plt.tight_layout()\n", + "plt.show()" + ], + "execution_count": null, + "outputs": [], + "id": "20e57208" }, { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Reading the results\n", + "\n", + "- The **aggregator comparison** shows how the choice of summarize metric changes how cleanly controversial and Lex Fridman episodes separate. If `skewness` is not the top row, another aggregator may fit this dataset better.\n", + "- The three families can disagree: an aggregator can rank well (high `roc_auc`) yet have a modest best-`f1`, or vice versa. Choose the family that matches how you will use the score - triage/ranking (ranking family), a hard yes/no cutoff (threshold family), or distribution monitoring (separation family).\n", + "- The **grid search** reports the best `(top_k, min_score_to_consider)` per aggregator, and the heatmap helps you pick a setting that is good across neighboring values (robust) rather than a single lucky cell.\n", + "\n", + "This ties directly into the \"wolf in sheep's clothing\" appendix below: aggregators such as `skewness`, `top_k_mean`, and `percentile_score` are built to catch a few high-scoring segments hidden inside otherwise benign content - exactly the signal that simple averaging would wash out." + ], + "id": "8054d065" }, { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Conclusion and Key Findings\n", + "\n", + "In this notebook, we've built and evaluated a hate speech detection system using Sentinel with minimal training data. The key aspects of our approach include:\n", + "\n", + "1. **Efficient Model Creation**: We constructed a hate speech detection model using only ~1,500 extremist quotes from SPLC as positive examples, balanced against ~15,000 neutral Lex Fridman podcast segments. This demonstrates Sentinel's ability to work effectively with limited training data.\n", + "\n", + "2. **Cross-Dataset Evaluation**: By testing on a controversial podcast content (not seen during training) and comparing against Lex Fridman episodes, we validated the model's generalization capabilities and resistance to domain-specific biases.\n", + "\n", + "3. **Multi-level Analysis**: We analyzed content at both segment level (individual text chunks) and episode level (aggregated scores using skewness), showing how patterns of concerning language emerge even when individual segments might not cross thresholds.\n", + "\n", + "4. **Statistical Validation**: The distributions and percentile analysis demonstrate clear differentiation between the two content sources, with the other podcast showing significantly higher percentages of high-risk segments compared to Lex Fridman's podcast.\n", + "\n", + "5. **Practical Application**: The CSV exports and visualization tools enable further analysis and potential integration into content moderation workflows.\n", + "\n", + "This methodology showcases Sentinel's capability to detect rare class patterns in text with minimal examples, highlighting its potential for practical applications in online safety, content moderation, and harmful content detection across diverse platforms." + ], + "id": "9c91aa37" }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Segment-level statistics for hate speech scores:\n", - "\n", - "Controversial:\n", - " Count: 2731\n", - " Mean: 0.0113\n", - " Median: 0.0000\n", - " Std Dev: 0.0376\n", - " Min: 0.0000\n", - " Max: 0.2104\n", - " 25th Percentile: 0.0000\n", - " 75th Percentile: 0.0000\n", - " 90th Percentile: 0.0000\n", - " 95th Percentile: 0.1193\n", - " 99th Percentile: 0.1689\n", - " High risk segments (> 0.5): 0 (0.00%)\n", - " Medium risk segments (0.1-0.5): 236 (8.64%)\n", - "\n", - "Lex Fridman:\n", - " Count: 7153\n", - " Mean: 0.0012\n", - " Median: 0.0000\n", - " Std Dev: 0.0126\n", - " Min: 0.0000\n", - " Max: 0.2409\n", - " 25th Percentile: 0.0000\n", - " 75th Percentile: 0.0000\n", - " 90th Percentile: 0.0000\n", - " 95th Percentile: 0.0000\n", - " 99th Percentile: 0.0000\n", - " High risk segments (> 0.5): 0 (0.00%)\n", - " Medium risk segments (0.1-0.5): 67 (0.94%)\n" - ] + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "markdown" + } + }, + "source": [ + "## Appendix: Detecting the \"Wolf in Sheep's Clothes\" - The Power of Skewness\n", + "\n", + "Our analysis reveals an important pattern in how harmful content manifests across different sources, demonstrating Sentinel's ability to detect what we might call the \"wolf in sheep's clothes\" phenomenon.\n", + "\n", + "### Understanding the Distribution Patterns\n", + "\n", + "Looking at the segment-level histograms and statistics, we observe:\n", + "\n", + "1. **Low Central Tendency in Both Sources**: Both Lex Fridman podcasts and the other podcast have segment score distributions with relatively low central tendency (mean/median) - the majority of content segments from both sources receive low hate speech scores.\n", + "\n", + "2. **Critical Difference in Distribution Shape**: While both distributions have similar centers, the other podcast' distribution is notably **right-skewed with a heavy tail** - showing a relatively small number of highly toxic segments that score much higher than the average content.\n", + "\n", + "3. **The \"Hidden Toxicity\" Problem**: This pattern represents a common challenge in content moderation - harmful content often appears as occasional \"spikes\" within otherwise benign material. Simple averaging methods would dilute these spikes, potentially missing concerning content.\n", + "\n", + "### How Sentinel's Episode-Level Scoring Captures This Pattern\n", + "\n", + "Sentinel's methodology effectively addresses this challenge through:\n", + "\n", + "1. **Statistical Sensitivity to Skewness**: Rather than simple averaging, the episode-level scores account for distribution shape, giving higher weight to right-skewed distributions where even a few high-scoring segments exist.\n", + "\n", + "2. **Capturing Rare but Significant Signals**: This approach successfully identifies content sources that occasionally \"show their teeth\" with harmful rhetoric, even when such rhetoric represents a small percentage of the total content.\n", + "\n", + "3. **Practical Moderation Value**: The resulting episode-level scores place the other podcast episodes significantly higher than Lex Fridman episodes, creating a clear prioritization signal for content moderation efforts.\n", + "\n", + "This approach demonstrates why robust hate speech detection requires attention not just to average content characteristics, but to distribution patterns that might reveal occasional but significant harmful content within otherwise unremarkable material." + ], + "id": "8ac615b3" } - ], - "source": [ - "# Create segment-level visualizations and comparisons\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Save the completed segment dataframes to CSV files\n", - "segment_scores_df.to_csv(\"data/controversial_segment_scores.csv\", index=False)\n", - "lex_segment_scores_df.to_csv(\"data/lex_fridman_segment_scores.csv\", index=False)\n", - "\n", - "# Create dataframes with platform labels for combined analysis\n", - "controversial_segments = pd.DataFrame({\n", - " 'platform': ['Controversial'] * len(segment_scores_df),\n", - " 'score': segment_scores_df['score']\n", - "})\n", - "\n", - "lex_segments = pd.DataFrame({\n", - " 'platform': ['Lex Fridman'] * len(lex_segment_scores_df),\n", - " 'score': lex_segment_scores_df['score']\n", - "})\n", - "\n", - "# Combine segment scores\n", - "all_segment_scores = pd.concat([controversial_segments, lex_segments], ignore_index=True)\n", - "all_segment_scores.to_csv(\"data/all_segment_scores.csv\", index=False)\n", - "print(\"Saved all segment-level data to CSV files\")\n", - "\n", - "# Create a boxplot comparison for segment-level scores\n", - "plt.figure(figsize=(10, 6))\n", - "\n", - "# Extract data for each platform\n", - "platforms = all_segment_scores['platform'].unique()\n", - "segment_data = [all_segment_scores[all_segment_scores['platform'] == platform]['score'] for platform in platforms]\n", - "\n", - "# Create box plot\n", - "box = plt.boxplot(segment_data, patch_artist=True, labels=platforms)\n", - "colors = ['#ff9999', '#66b3ff']\n", - "for patch, color in zip(box['boxes'], colors):\n", - " patch.set_facecolor(color)\n", - "\n", - "plt.title('Segment-Level Hate Speech Score Comparison')\n", - "plt.ylabel('Hate Speech Score')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.show()\n", - "\n", - "# Create histograms to compare segment-level score distributions\n", - "plt.figure(figsize=(12, 6))\n", - "\n", - "# Extract data for each platform\n", - "for i, platform in enumerate(platforms):\n", - " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", - " # Plot histogram with log scale for y-axis to better see the distribution tail\n", - " plt.hist(platform_data, bins=50, alpha=0.6, label=platform)\n", - "\n", - "plt.title('Distribution of Segment-Level Hate Speech Scores')\n", - "plt.xlabel('Hate Speech Score')\n", - "plt.ylabel('Frequency')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "# Create a second histogram with log scale for better comparison of tails\n", - "plt.figure(figsize=(12, 6))\n", - "for i, platform in enumerate(platforms):\n", - " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", - " # Plot histogram with log scale for y-axis\n", - " plt.hist(platform_data, bins=50, alpha=0.6, label=platform)\n", - "\n", - "plt.title('Distribution of Segment-Level Hate Speech Scores (Log Scale)')\n", - "plt.xlabel('Hate Speech Score')\n", - "plt.ylabel('Frequency (log scale)')\n", - "plt.yscale('log')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "# Create cumulative distribution function (CDF) plot\n", - "plt.figure(figsize=(12, 6))\n", - "for i, platform in enumerate(platforms):\n", - " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", - " # Sort the data\n", - " sorted_data = np.sort(platform_data)\n", - " # Get the cumulative probabilities\n", - " p = 1. * np.arange(len(sorted_data)) / (len(sorted_data) - 1)\n", - " # Plot the CDF\n", - " plt.plot(sorted_data, p, label=platform, color=colors[i], linewidth=2)\n", - "\n", - "plt.title('Cumulative Distribution of Segment-Level Hate Speech Scores')\n", - "plt.xlabel('Hate Speech Score')\n", - "plt.ylabel('Cumulative Probability')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "# Print detailed statistics for segment-level scores\n", - "print(\"\\nSegment-level statistics for hate speech scores:\")\n", - "for platform in all_segment_scores['platform'].unique():\n", - " platform_scores = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", - " print(f\"\\n{platform}:\")\n", - " print(f\" Count: {len(platform_scores)}\")\n", - " print(f\" Mean: {platform_scores.mean():.4f}\")\n", - " print(f\" Median: {platform_scores.median():.4f}\")\n", - " print(f\" Std Dev: {platform_scores.std():.4f}\")\n", - " print(f\" Min: {platform_scores.min():.4f}\")\n", - " print(f\" Max: {platform_scores.max():.4f}\")\n", - " print(f\" 25th Percentile: {platform_scores.quantile(0.25):.4f}\")\n", - " print(f\" 75th Percentile: {platform_scores.quantile(0.75):.4f}\")\n", - " print(f\" 90th Percentile: {platform_scores.quantile(0.9):.4f}\")\n", - " print(f\" 95th Percentile: {platform_scores.quantile(0.95):.4f}\")\n", - " print(f\" 99th Percentile: {platform_scores.quantile(0.99):.4f}\")\n", - " \n", - " # Count high and medium risk segments\n", - " high_risk = len(platform_scores[platform_scores > 0.5])\n", - " high_risk_pct = high_risk / len(platform_scores) * 100\n", - " medium_risk = len(platform_scores[(platform_scores > 0.1) & (platform_scores <= 0.5)])\n", - " medium_risk_pct = medium_risk / len(platform_scores) * 100\n", - " \n", - " print(f\" High risk segments (> 0.5): {high_risk} ({high_risk_pct:.2f}%)\")\n", - " print(f\" Medium risk segments (0.1-0.5): {medium_risk} ({medium_risk_pct:.2f}%)\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "9c91aa37", - "metadata": {}, - "source": [ - "## Conclusion and Key Findings\n", - "\n", - "In this notebook, we've built and evaluated a hate speech detection system using Sentinel with minimal training data. The key aspects of our approach include:\n", - "\n", - "1. **Efficient Model Creation**: We constructed a hate speech detection model using only ~1,500 extremist quotes from SPLC as positive examples, balanced against ~15,000 neutral Lex Fridman podcast segments. This demonstrates Sentinel's ability to work effectively with limited training data.\n", - "\n", - "2. **Cross-Dataset Evaluation**: By testing on a controversial podcast content (not seen during training) and comparing against Lex Fridman episodes, we validated the model's generalization capabilities and resistance to domain-specific biases.\n", - "\n", - "3. **Multi-level Analysis**: We analyzed content at both segment level (individual text chunks) and episode level (aggregated scores using skewness), showing how patterns of concerning language emerge even when individual segments might not cross thresholds.\n", - "\n", - "4. **Statistical Validation**: The distributions and percentile analysis demonstrate clear differentiation between the two content sources, with the other podcast showing significantly higher percentages of high-risk segments compared to Lex Fridman's podcast.\n", - "\n", - "5. **Practical Application**: The CSV exports and visualization tools enable further analysis and potential integration into content moderation workflows.\n", - "\n", - "This methodology showcases Sentinel's capability to detect rare class patterns in text with minimal examples, highlighting its potential for practical applications in online safety, content moderation, and harmful content detection across diverse platforms." - ] - }, - { - "cell_type": "markdown", - "id": "8ac615b3", - "metadata": { - "vscode": { - "languageId": "markdown" + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" } - }, - "source": [ - "## Appendix: Detecting the \"Wolf in Sheep's Clothes\" - The Power of Skewness\n", - "\n", - "Our analysis reveals an important pattern in how harmful content manifests across different sources, demonstrating Sentinel's ability to detect what we might call the \"wolf in sheep's clothes\" phenomenon.\n", - "\n", - "### Understanding the Distribution Patterns\n", - "\n", - "Looking at the segment-level histograms and statistics, we observe:\n", - "\n", - "1. **Low Central Tendency in Both Sources**: Both Lex Fridman podcasts and the other podcast have segment score distributions with relatively low central tendency (mean/median) - the majority of content segments from both sources receive low hate speech scores.\n", - "\n", - "2. **Critical Difference in Distribution Shape**: While both distributions have similar centers, the other podcast' distribution is notably **right-skewed with a heavy tail** - showing a relatively small number of highly toxic segments that score much higher than the average content.\n", - "\n", - "3. **The \"Hidden Toxicity\" Problem**: This pattern represents a common challenge in content moderation - harmful content often appears as occasional \"spikes\" within otherwise benign material. Simple averaging methods would dilute these spikes, potentially missing concerning content.\n", - "\n", - "### How Sentinel's Episode-Level Scoring Captures This Pattern\n", - "\n", - "Sentinel's methodology effectively addresses this challenge through:\n", - "\n", - "1. **Statistical Sensitivity to Skewness**: Rather than simple averaging, the episode-level scores account for distribution shape, giving higher weight to right-skewed distributions where even a few high-scoring segments exist.\n", - "\n", - "2. **Capturing Rare but Significant Signals**: This approach successfully identifies content sources that occasionally \"show their teeth\" with harmful rhetoric, even when such rhetoric represents a small percentage of the total content.\n", - "\n", - "3. **Practical Moderation Value**: The resulting episode-level scores place the other podcast episodes significantly higher than Lex Fridman episodes, creating a clear prioritization signal for content moderation efforts.\n", - "\n", - "This approach demonstrates why robust hate speech detection requires attention not just to average content characteristics, but to distribution patterns that might reveal occasional but significant harmful content within otherwise unremarkable material." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file diff --git a/src/sentinel/__init__.py b/src/sentinel/__init__.py index dca2c35..4b937ba 100644 --- a/src/sentinel/__init__.py +++ b/src/sentinel/__init__.py @@ -28,6 +28,15 @@ softmax_weighted_mean, max_score, ) +from sentinel.simulation import ( + DEFAULT_AGGREGATORS, + LabeledGroup, + GroupObservationScores, + score_groups, + evaluate_groups, + compare_aggregators, + run_grid_search, +) __all__ = [ "SentinelLocalIndex", @@ -38,4 +47,11 @@ "percentile_score", "softmax_weighted_mean", "max_score", + "DEFAULT_AGGREGATORS", + "LabeledGroup", + "GroupObservationScores", + "score_groups", + "evaluate_groups", + "compare_aggregators", + "run_grid_search", ] diff --git a/src/sentinel/simulation.py b/src/sentinel/simulation.py new file mode 100644 index 0000000..03d9f68 --- /dev/null +++ b/src/sentinel/simulation.py @@ -0,0 +1,572 @@ +# Copyright 2025 Roblox Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Simulation and tuning harness for Sentinel. + +This module helps you answer a practical question: *which aggregation +("summarize metric") and which hyperparameters work best for my data?* + +It mirrors the workflow used to evaluate Sentinel in production, but with none +of the production infrastructure (no Ray, S3, Hydra, or experiment trackers) so +it stays easy to read and depends only on numpy. + +There are two very different "metrics" involved, and it helps to keep them +separate: + +1. The **summarize metric** (a.k.a. the aggregation function): how a group's + many per-observation scores are collapsed into a single affinity number. + Sentinel ships several of these in :mod:`sentinel.score_formulae` + (``skewness``, ``mean_of_positives``, ``top_k_mean``, ``percentile_score``, + ``softmax_weighted_mean``, ``max_score``). This is *not* a ranking. + +2. The **evaluation metric**: once every group has one affinity number and a + known label, how good is that separation? Ranking (where do the known + positives land in the leaderboard?) is only one option. This harness reports + three families so you can tune for whatever you care about: + + - ranking (ROC-AUC, recall@N / precision@N, average-rank ratio), + - threshold / classification (precision, recall, F1 at a cutoff), + - separation / distribution (mean separation, Cohen's d, KS statistic). + +Typical usage:: + + from sentinel.simulation import LabeledGroup, score_groups, compare_aggregators + + groups = [ + LabeledGroup(name="episode_a", label=1, observations=[...segments...]), + LabeledGroup(name="episode_b", label=0, observations=[...segments...]), + ... + ] + + # Expensive step (runs the sentence model) -- done once. + scored = score_groups(index, groups, top_k=5) + + # Cheap step -- compare every aggregator without re-encoding. + rows = compare_aggregators(scored) +""" + +from dataclasses import dataclass +from typing import TYPE_CHECKING, Callable, Dict, List, Mapping, Optional, Sequence + +import numpy as np + +from sentinel.score_formulae import ( + max_score, + mean_of_positives, + percentile_score, + skewness, + softmax_weighted_mean, + top_k_mean, +) + +if TYPE_CHECKING: + # Imported only for type hints. Kept out of runtime imports so this module + # stays lightweight (numpy-only) and does not pull in torch / transformers. + from sentinel.sentinel_local_index import SentinelLocalIndex + + +# A convenient name -> function map of the built-in summarize metrics, so a +# caller (or a notebook) can iterate over "all aggregators" in one line. +DEFAULT_AGGREGATORS: Dict[str, Callable[[np.ndarray], float]] = { + "skewness": skewness, + "mean_of_positives": mean_of_positives, + "top_k_mean": top_k_mean, + "percentile_score": percentile_score, + "softmax_weighted_mean": softmax_weighted_mean, + "max_score": max_score, +} + + +@dataclass +class LabeledGroup: + """A collection of observations from a single source, with a known label. + + Think of one group as, for example, one podcast episode (its observations + are the episode's text segments) or one user (their recent messages). + + Attributes: + name: A human-readable identifier for the group. + label: ``1`` if this group belongs to the rare/positive class (e.g. a + hateful episode), ``0`` if it belongs to the common/negative class. + observations: The individual texts that make up this group. + """ + + name: str + label: int + observations: Sequence[str] + + +@dataclass +class GroupObservationScores: + """Raw per-observation scores for one group (computed once, reused often). + + Attributes: + name: The group's identifier (copied from :class:`LabeledGroup`). + label: The group's label (copied from :class:`LabeledGroup`). + observation_scores: 1-D array of per-observation scores, *before* any + ``min_score_to_consider`` threshold is applied. Keeping them raw lets + us sweep thresholds later without re-running the sentence model. + """ + + name: str + label: int + observation_scores: np.ndarray + + +def score_groups( + index: "SentinelLocalIndex", + groups: Sequence[LabeledGroup], + *, + top_k: int = 5, + show_progress_bar: bool = False, + encoding_additional_kwargs: Optional[Mapping[str, object]] = None, +) -> List[GroupObservationScores]: + """Score every observation in every group, once. + + This is the expensive part of a simulation because it runs the sentence + model to embed and score each observation. Aggregation and evaluation happen + separately and cheaply, so you can compare many summarize metrics and + thresholds afterward without paying the encoding cost again. + + Scores are computed with ``min_score_to_consider=0.0`` so the raw values are + preserved; :func:`evaluate_groups` applies any threshold later. + + Args: + index: A loaded :class:`~sentinel.sentinel_local_index.SentinelLocalIndex`. + groups: The labeled groups to score. + top_k: Number of nearest neighbors used per observation. Changing this + changes the per-observation scores, so it requires re-scoring. + show_progress_bar: Whether to show the encoder progress bar. + encoding_additional_kwargs: Extra keyword arguments forwarded to the + encoder. + + Returns: + One :class:`GroupObservationScores` per input group, in the same order. + """ + if encoding_additional_kwargs is None: + encoding_additional_kwargs = {} + + scored: List[GroupObservationScores] = [] + for group in groups: + observations = list(group.observations) + + if len(observations) == 0: + scored.append( + GroupObservationScores( + name=group.name, + label=int(group.label), + observation_scores=np.array([], dtype=float), + ) + ) + continue + + # We only need the per-observation scores here, so we disable the + # explainability extras for speed and pass a trivial aggregation + # function (its output is ignored). + result = index.calculate_rare_class_affinity( + observations, + top_k=top_k, + min_score_to_consider=0.0, + aggregation_function=max_score, + show_progress_bar=show_progress_bar, + explain=False, + include_neighbors=False, + encoding_additional_kwargs=encoding_additional_kwargs, + ) + + observation_scores = np.asarray( + list(result.observation_scores.values()), dtype=float + ) + scored.append( + GroupObservationScores( + name=group.name, + label=int(group.label), + observation_scores=observation_scores, + ) + ) + + return scored + + +def _apply_threshold(scores: np.ndarray, min_score_to_consider: float) -> np.ndarray: + """Zero out per-observation scores below the threshold (cheap, no encoding). + + This reproduces exactly what ``calculate_rare_class_affinity`` does with its + ``min_score_to_consider`` argument, but applied to already-computed scores. + """ + if scores.size == 0 or min_score_to_consider <= 0.0: + return scores + return np.where(scores < min_score_to_consider, 0.0, scores) + + +def _average_ranks(values: np.ndarray) -> np.ndarray: + """Return 1-based ranks of ``values`` (smallest = rank 1), averaging ties. + + Ties receive the average of the ranks they span (the standard convention + used by the Mann-Whitney statistic), which keeps metrics stable when many + groups share the same score (e.g. lots of zeros). + """ + n = values.size + order = np.argsort(values, kind="mergesort") + sorted_vals = values[order] + ranks = np.empty(n, dtype=float) + + i = 0 + while i < n: + j = i + while j + 1 < n and sorted_vals[j + 1] == sorted_vals[i]: + j += 1 + # Average 1-based rank for the tied block [i, j]. + avg_rank = (i + j) / 2.0 + 1.0 + ranks[order[i : j + 1]] = avg_rank + i = j + 1 + + return ranks + + +def _roc_auc(affinities: np.ndarray, labels: np.ndarray) -> float: + """ROC-AUC via the rank-based (Mann-Whitney) formula. numpy-only. + + Equals the probability that a randomly chosen positive group scores higher + than a randomly chosen negative one. Ties count as 0.5. Returns ``nan`` if + either class is missing. + """ + n_pos = int(np.sum(labels == 1)) + n_neg = int(np.sum(labels == 0)) + if n_pos == 0 or n_neg == 0: + return float("nan") + + ranks = _average_ranks(affinities) + sum_ranks_pos = float(np.sum(ranks[labels == 1])) + return (sum_ranks_pos - n_pos * (n_pos + 1) / 2.0) / (n_pos * n_neg) + + +def _ranking_metrics( + affinities: np.ndarray, labels: np.ndarray, top_n: Optional[int] +) -> Dict[str, float]: + """Ranking family: leaderboard-style metrics (production's original choice).""" + n_groups = int(labels.size) + n_pos = int(np.sum(labels == 1)) + n_neg = int(np.sum(labels == 0)) + + metrics: Dict[str, float] = {"roc_auc": _roc_auc(affinities, labels)} + + # Leaderboard rank where 1 = highest affinity. Rank the *negated* affinities + # so the largest affinity gets the smallest rank number. Ties are averaged. + leaderboard_rank = _average_ranks(-affinities) + + # recall@N / precision@N over the top-N of the leaderboard. Default N is the + # number of positives (a.k.a. R-precision), which mirrors production intent. + top_n_eff = n_pos if top_n is None else int(top_n) + top_n_eff = max(0, min(top_n_eff, n_groups)) + metrics["top_n"] = float(top_n_eff) + if top_n_eff > 0 and n_pos > 0: + in_top = labels[leaderboard_rank <= top_n_eff] + tp_in_top = int(np.sum(in_top == 1)) + metrics["precision_at_n"] = tp_in_top / top_n_eff + metrics["recall_at_n"] = tp_in_top / n_pos + else: + metrics["precision_at_n"] = float("nan") + metrics["recall_at_n"] = float("nan") + + metrics["avg_rank_positive"] = ( + float(np.mean(leaderboard_rank[labels == 1])) if n_pos > 0 else float("nan") + ) + metrics["avg_rank_negative"] = ( + float(np.mean(leaderboard_rank[labels == 0])) if n_neg > 0 else float("nan") + ) + # Lower is better: positives should have smaller (better) ranks than negatives. + if n_pos > 0 and n_neg > 0 and metrics["avg_rank_negative"] > 0: + metrics["rank_ratio"] = ( + metrics["avg_rank_positive"] / metrics["avg_rank_negative"] + ) + else: + metrics["rank_ratio"] = float("nan") + + return metrics + + +def _classification_at_threshold( + affinities: np.ndarray, labels: np.ndarray, threshold: float +) -> Dict[str, float]: + """Confusion-matrix counts and precision/recall/F1 at a fixed cutoff.""" + predicted_positive = affinities >= threshold + tp = int(np.sum(predicted_positive & (labels == 1))) + fp = int(np.sum(predicted_positive & (labels == 0))) + fn = int(np.sum(~predicted_positive & (labels == 1))) + tn = int(np.sum(~predicted_positive & (labels == 0))) + + precision = tp / (tp + fp) if (tp + fp) > 0 else 0.0 + recall = tp / (tp + fn) if (tp + fn) > 0 else 0.0 + f1 = ( + 2 * precision * recall / (precision + recall) + if (precision + recall) > 0 + else 0.0 + ) + false_positive_rate = fp / (fp + tn) if (fp + tn) > 0 else 0.0 + + return { + "tp": float(tp), + "fp": float(fp), + "fn": float(fn), + "tn": float(tn), + "precision": precision, + "recall": recall, + "f1": f1, + "false_positive_rate": false_positive_rate, + } + + +def _threshold_metrics( + affinities: np.ndarray, labels: np.ndarray, decision_threshold: Optional[float] +) -> Dict[str, float]: + """Threshold / classification family. + + If ``decision_threshold`` is given, metrics are reported at that exact + cutoff. If it is ``None``, we sweep the candidate cutoffs (the sorted unique + affinities) and report the one that maximizes F1 -- a useful "best + achievable" signal when tuning. + """ + n_pos = int(np.sum(labels == 1)) + n_neg = int(np.sum(labels == 0)) + if n_pos == 0 or n_neg == 0: + return { + "decision_threshold": float("nan"), + "tp": float("nan"), + "fp": float("nan"), + "fn": float("nan"), + "tn": float("nan"), + "precision": float("nan"), + "recall": float("nan"), + "f1": float("nan"), + "false_positive_rate": float("nan"), + } + + if decision_threshold is not None: + metrics = _classification_at_threshold( + affinities, labels, float(decision_threshold) + ) + metrics["decision_threshold"] = float(decision_threshold) + return metrics + + best_metrics: Optional[Dict[str, float]] = None + best_threshold = float("nan") + for candidate in np.unique(affinities): + candidate_metrics = _classification_at_threshold(affinities, labels, candidate) + if best_metrics is None or candidate_metrics["f1"] > best_metrics["f1"]: + best_metrics = candidate_metrics + best_threshold = float(candidate) + + assert best_metrics is not None # np.unique is non-empty when groups exist + best_metrics["decision_threshold"] = best_threshold + return best_metrics + + +def _ks_statistic(positives: np.ndarray, negatives: np.ndarray) -> float: + """Two-sample Kolmogorov-Smirnov statistic: max gap between the two CDFs.""" + grid = np.sort(np.concatenate([positives, negatives])) + cdf_pos = np.searchsorted(np.sort(positives), grid, side="right") / positives.size + cdf_neg = np.searchsorted(np.sort(negatives), grid, side="right") / negatives.size + return float(np.max(np.abs(cdf_pos - cdf_neg))) + + +def _separation_metrics( + affinities: np.ndarray, labels: np.ndarray +) -> Dict[str, float]: + """Separation / distribution family (threshold-free, non-ranking).""" + positives = affinities[labels == 1] + negatives = affinities[labels == 0] + + if positives.size == 0 or negatives.size == 0: + return { + "mean_separation": float("nan"), + "cohens_d": float("nan"), + "ks_statistic": float("nan"), + } + + mean_pos = float(np.mean(positives)) + mean_neg = float(np.mean(negatives)) + mean_separation = mean_pos - mean_neg + + # Pooled standard deviation for Cohen's d (standardized effect size). + n1, n2 = positives.size, negatives.size + var_pos = float(np.var(positives, ddof=1)) if n1 > 1 else 0.0 + var_neg = float(np.var(negatives, ddof=1)) if n2 > 1 else 0.0 + if (n1 + n2 - 2) > 0: + pooled_var = ((n1 - 1) * var_pos + (n2 - 1) * var_neg) / (n1 + n2 - 2) + else: + pooled_var = 0.0 + pooled_std = float(np.sqrt(pooled_var)) + + return { + "mean_separation": mean_separation, + "cohens_d": (mean_separation / pooled_std) if pooled_std > 0 else float("nan"), + "ks_statistic": _ks_statistic(positives, negatives), + } + + +def evaluate_groups( + group_scores: Sequence[GroupObservationScores], + aggregator: Callable[[np.ndarray], float], + *, + aggregator_name: Optional[str] = None, + min_score_to_consider: float = 0.1, + top_n: Optional[int] = None, + decision_threshold: Optional[float] = None, +) -> Dict[str, float]: + """Aggregate each group to one affinity, then score it against the labels. + + Produces a single flat dict containing all three metric families (ranking, + threshold/classification, and separation/distribution) plus some metadata, + so you can tune for whichever family matters most to you. + + Args: + group_scores: Output of :func:`score_groups`. + aggregator: A summarize metric such as ``skewness`` or ``top_k_mean``. + aggregator_name: Optional label for the aggregator (defaults to its + ``__name__``). + min_score_to_consider: Per-observation threshold applied before + aggregating (values below it are set to 0). + top_n: Size of the top-N slice for recall@N / precision@N. Defaults to + the number of positive groups. + decision_threshold: Cutoff for the classification family. If ``None``, + the best-F1 cutoff is found automatically. + + Returns: + A dict with keys for metadata (``aggregator``, ``min_score_to_consider``, + ``n_groups``, ``n_positive``) and every metric from the three families. + """ + labels_list: List[int] = [] + affinities_list: List[float] = [] + for group in group_scores: + thresholded = _apply_threshold( + group.observation_scores, min_score_to_consider + ) + affinity = float(aggregator(thresholded)) if thresholded.size > 0 else 0.0 + labels_list.append(int(group.label)) + affinities_list.append(affinity) + + labels = np.asarray(labels_list, dtype=int) + affinities = np.asarray(affinities_list, dtype=float) + + row: Dict[str, float] = { + "aggregator": aggregator_name + or getattr(aggregator, "__name__", str(aggregator)), + "min_score_to_consider": float(min_score_to_consider), + "n_groups": int(labels.size), + "n_positive": int(np.sum(labels == 1)), + } + row.update(_ranking_metrics(affinities, labels, top_n=top_n)) + row.update(_threshold_metrics(affinities, labels, decision_threshold=decision_threshold)) + row.update(_separation_metrics(affinities, labels)) + return row + + +def compare_aggregators( + group_scores: Sequence[GroupObservationScores], + *, + aggregators: Optional[Mapping[str, Callable[[np.ndarray], float]]] = None, + min_score_to_consider: float = 0.1, + top_n: Optional[int] = None, + decision_threshold: Optional[float] = None, +) -> List[Dict[str, float]]: + """Evaluate several summarize metrics on the same pre-computed scores. + + This is cheap: it reuses the per-observation scores from + :func:`score_groups`, so no re-encoding happens. + + Args: + group_scores: Output of :func:`score_groups`. + aggregators: Name -> function map. Defaults to + :data:`DEFAULT_AGGREGATORS` (all six built-ins). + min_score_to_consider: Per-observation threshold applied before + aggregating. + top_n: Size of the top-N slice for recall@N / precision@N. + decision_threshold: Cutoff for the classification family (``None`` = + best-F1). + + Returns: + One metric dict per aggregator (see :func:`evaluate_groups`). + """ + if aggregators is None: + aggregators = DEFAULT_AGGREGATORS + + return [ + evaluate_groups( + group_scores, + fn, + aggregator_name=name, + min_score_to_consider=min_score_to_consider, + top_n=top_n, + decision_threshold=decision_threshold, + ) + for name, fn in aggregators.items() + ] + + +def run_grid_search( + index: "SentinelLocalIndex", + groups: Sequence[LabeledGroup], + *, + top_k_values: Sequence[int] = (5,), + min_score_values: Sequence[float] = (0.1,), + aggregators: Optional[Mapping[str, Callable[[np.ndarray], float]]] = None, + top_n: Optional[int] = None, + decision_threshold: Optional[float] = None, + show_progress_bar: bool = False, +) -> List[Dict[str, float]]: + """Sweep hyperparameters and summarize metrics, returning a flat result table. + + For each ``top_k`` the groups are scored once (the expensive step), then + every combination of ``min_score_values`` x ``aggregators`` is evaluated + cheaply. The returned rows are easy to turn into a ``pandas.DataFrame`` for + plotting. + + Args: + index: A loaded :class:`~sentinel.sentinel_local_index.SentinelLocalIndex`. + groups: The labeled groups to evaluate. + top_k_values: ``top_k`` values to try (each triggers a re-scoring). + min_score_values: Per-observation thresholds to try (evaluated cheaply). + aggregators: Name -> function map. Defaults to :data:`DEFAULT_AGGREGATORS`. + top_n: Size of the top-N slice for recall@N / precision@N. + decision_threshold: Cutoff for the classification family (``None`` = + best-F1). + show_progress_bar: Whether to show the encoder progress bar. + + Returns: + A list of metric dicts, one per ``(top_k, min_score_to_consider, + aggregator)`` combination. Each row also includes a ``top_k`` key. + """ + if aggregators is None: + aggregators = DEFAULT_AGGREGATORS + + rows: List[Dict[str, float]] = [] + for top_k in top_k_values: + scored = score_groups( + index, groups, top_k=top_k, show_progress_bar=show_progress_bar + ) + for min_score in min_score_values: + for name, fn in aggregators.items(): + row = evaluate_groups( + scored, + fn, + aggregator_name=name, + min_score_to_consider=min_score, + top_n=top_n, + decision_threshold=decision_threshold, + ) + row["top_k"] = int(top_k) + rows.append(row) + + return rows diff --git a/tests/test_sriracha_local_index.py b/tests/test_sentinel_local_index.py similarity index 100% rename from tests/test_sriracha_local_index.py rename to tests/test_sentinel_local_index.py diff --git a/tests/test_simulation.py b/tests/test_simulation.py new file mode 100644 index 0000000..2275288 --- /dev/null +++ b/tests/test_simulation.py @@ -0,0 +1,261 @@ +# Copyright 2025 Roblox Corporation +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# https://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +"""Tests for the simulation / tuning harness (sentinel.simulation). + +These tests are fast and require no model download: they either exercise the +pure metric logic directly on hand-crafted scores, or use a tiny stub index +that mimics ``calculate_rare_class_affinity``. +""" + +from types import SimpleNamespace + +import numpy as np +import pytest + +from sentinel.score_formulae import max_score +from sentinel.simulation import ( + DEFAULT_AGGREGATORS, + GroupObservationScores, + LabeledGroup, + compare_aggregators, + evaluate_groups, + run_grid_search, + score_groups, +) + + +def _make_group_scores(pairs): + """Build GroupObservationScores from (label, affinity) pairs. + + Each group gets a single observation equal to the desired affinity, so that + aggregating with ``max_score`` (and no threshold) reproduces that affinity. + """ + return [ + GroupObservationScores( + name=f"g{i}", label=label, observation_scores=np.array([affinity], dtype=float) + ) + for i, (label, affinity) in enumerate(pairs) + ] + + +# --------------------------------------------------------------------------- +# Ranking family +# --------------------------------------------------------------------------- + + +def test_roc_auc_separable(): + """Positives clearly above negatives -> AUC 1.0 and perfect top-N.""" + scores = _make_group_scores([(1, 0.9), (1, 0.8), (0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + + assert row["roc_auc"] == pytest.approx(1.0) + assert row["recall_at_n"] == pytest.approx(1.0) + assert row["precision_at_n"] == pytest.approx(1.0) + # Positives should rank better (smaller rank number) than negatives. + assert row["avg_rank_positive"] < row["avg_rank_negative"] + assert row["rank_ratio"] < 1.0 + assert row["n_groups"] == 4 + assert row["n_positive"] == 2 + assert row["top_n"] == 2 + + +def test_roc_auc_reversed(): + """Positives below negatives -> AUC 0.0.""" + scores = _make_group_scores([(1, 0.1), (1, 0.2), (0, 0.9), (0, 0.8)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert row["roc_auc"] == pytest.approx(0.0) + + +def test_roc_auc_tied(): + """All affinities equal -> AUC 0.5 (ties are averaged).""" + scores = _make_group_scores([(1, 0.5), (1, 0.5), (0, 0.5), (0, 0.5)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert row["roc_auc"] == pytest.approx(0.5) + + +def test_ranking_auc_undefined_single_class(): + """AUC is nan when only one class is present.""" + scores = _make_group_scores([(1, 0.9), (1, 0.8)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert np.isnan(row["roc_auc"]) + + +# --------------------------------------------------------------------------- +# Threshold / classification family +# --------------------------------------------------------------------------- + + +def test_classification_fixed_threshold(): + """A cutoff between the two classes yields perfect precision/recall/F1.""" + scores = _make_group_scores([(1, 0.9), (1, 0.8), (0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0, decision_threshold=0.5) + + assert row["decision_threshold"] == pytest.approx(0.5) + assert row["precision"] == pytest.approx(1.0) + assert row["recall"] == pytest.approx(1.0) + assert row["f1"] == pytest.approx(1.0) + assert row["false_positive_rate"] == pytest.approx(0.0) + assert (row["tp"], row["fp"], row["fn"], row["tn"]) == (2.0, 0.0, 0.0, 2.0) + + +def test_classification_flag_everything_threshold(): + """A very low cutoff flags all groups -> recall 1.0, precision = base rate.""" + scores = _make_group_scores([(1, 0.9), (0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0, decision_threshold=0.0) + assert row["recall"] == pytest.approx(1.0) + assert row["precision"] == pytest.approx(1.0 / 3.0) + + +def test_classification_best_f1_sweep(): + """With decision_threshold=None the best-F1 cutoff is found automatically.""" + scores = _make_group_scores([(1, 0.9), (1, 0.8), (0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + # Perfectly separable -> a threshold achieving F1 == 1.0 exists. + assert row["f1"] == pytest.approx(1.0) + # The best cutoff is the lowest positive value (0.8), since predictions use >=. + assert row["decision_threshold"] == pytest.approx(0.8) + + +# --------------------------------------------------------------------------- +# Separation / distribution family +# --------------------------------------------------------------------------- + + +def test_separation_metrics_basic(): + scores = _make_group_scores([(1, 0.9), (1, 0.7), (0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert row["mean_separation"] == pytest.approx(0.8 - 0.15) + assert row["cohens_d"] > 0.0 + assert 0.0 <= row["ks_statistic"] <= 1.0 + assert row["ks_statistic"] == pytest.approx(1.0) # fully separated + + +def test_cohens_d_nan_when_zero_variance(): + """Identical values within each class -> pooled std 0 -> Cohen's d is nan.""" + scores = _make_group_scores([(1, 0.5), (1, 0.5), (0, 0.2), (0, 0.2)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert row["mean_separation"] == pytest.approx(0.3) + assert np.isnan(row["cohens_d"]) + + +def test_separation_nan_single_class(): + scores = _make_group_scores([(0, 0.2), (0, 0.1)]) + row = evaluate_groups(scores, max_score, min_score_to_consider=0.0) + assert np.isnan(row["mean_separation"]) + assert np.isnan(row["ks_statistic"]) + + +# --------------------------------------------------------------------------- +# compare_aggregators +# --------------------------------------------------------------------------- + + +def test_compare_aggregators_returns_row_per_aggregator(): + scores = _make_group_scores([(1, 0.9), (1, 0.8), (0, 0.2), (0, 0.1)]) + rows = compare_aggregators(scores, min_score_to_consider=0.0) + + assert len(rows) == len(DEFAULT_AGGREGATORS) + assert {r["aggregator"] for r in rows} == set(DEFAULT_AGGREGATORS.keys()) + + expected_keys = { + # metadata + "aggregator", "min_score_to_consider", "n_groups", "n_positive", + # ranking + "roc_auc", "recall_at_n", "precision_at_n", "top_n", + "avg_rank_positive", "avg_rank_negative", "rank_ratio", + # threshold / classification + "decision_threshold", "tp", "fp", "fn", "tn", + "precision", "recall", "f1", "false_positive_rate", + # separation / distribution + "mean_separation", "cohens_d", "ks_statistic", + } + for row in rows: + assert expected_keys.issubset(row.keys()) + + +# --------------------------------------------------------------------------- +# score_groups / run_grid_search plumbing (with a stub index) +# --------------------------------------------------------------------------- + + +class _StubIndex: + """Minimal stand-in for SentinelLocalIndex. + + Interprets each observation string as its numeric score, so tests can + control per-observation scores exactly with no model. + """ + + def __init__(self): + self.calls = [] + + def calculate_rare_class_affinity(self, text_samples, **kwargs): + self.calls.append(kwargs) + observation_scores = {text: float(text) for text in text_samples} + return SimpleNamespace(observation_scores=observation_scores) + + +def test_score_groups_extracts_raw_scores(): + index = _StubIndex() + groups = [ + LabeledGroup(name="pos", label=1, observations=["0.9", "0.1"]), + LabeledGroup(name="neg", label=0, observations=["0.2", "0.05"]), + ] + + scored = score_groups(index, groups, top_k=7) + + assert [s.name for s in scored] == ["pos", "neg"] + assert [s.label for s in scored] == [1, 0] + np.testing.assert_allclose(scored[0].observation_scores, [0.9, 0.1]) + np.testing.assert_allclose(scored[1].observation_scores, [0.2, 0.05]) + + # score_groups must request raw scores (threshold 0) and disable extras. + call = index.calls[0] + assert call["top_k"] == 7 + assert call["min_score_to_consider"] == 0.0 + assert call["explain"] is False + assert call["include_neighbors"] is False + + +def test_score_groups_handles_empty_group(): + index = _StubIndex() + groups = [LabeledGroup(name="empty", label=1, observations=[])] + scored = score_groups(index, groups) + assert scored[0].observation_scores.size == 0 + # No scoring call should be made for an empty group. + assert index.calls == [] + + +def test_run_grid_search_rescoring_and_rows(): + index = _StubIndex() + groups = [ + LabeledGroup(name="pos", label=1, observations=["0.9", "0.8"]), + LabeledGroup(name="neg", label=0, observations=["0.2", "0.1"]), + ] + + rows = run_grid_search( + index, + groups, + top_k_values=[3, 5], + min_score_values=[0.0, 0.2], + top_n=1, + ) + + # 2 top_k x 2 thresholds x 6 aggregators. + assert len(rows) == 2 * 2 * len(DEFAULT_AGGREGATORS) + assert all("top_k" in row for row in rows) + assert {row["top_k"] for row in rows} == {3, 5} + # Re-scoring happens once per top_k value (each scores both groups), and NOT + # again for every threshold/aggregator combination: 2 top_k x 2 groups = 4. + assert len(index.calls) == 2 * len(groups) From ccbf5ed9a13d05c7fa00fdfa9f638f6b5ee5399e Mon Sep 17 00:00:00 2001 From: Wei Xiao <11197323+wxiao0421@users.noreply.github.com> Date: Mon, 27 Jul 2026 15:37:06 -0700 Subject: [PATCH 2/4] Run the simulation section end-to-end and record its results The tuning section previously shipped as code with no saved output, so a reader could not see what the harness actually concludes. Re-executed the notebook against the full dataset (30 controversial vs 30 Lex Fridman episodes, 28,956 segments) and committed the real tables and charts. Result: skewness is confirmed as the best aggregator, but the default min_score_to_consider=0.1 is not optimal on this data - the top four grid configurations are all skewness, led by top_k=5 with min_score_to_consider=0.0 at 0.996 ROC-AUC. - Explain why the demo subsamples to 30 controversial episodes: it matches the 30 Lex Fridman episodes so the evaluation set is balanced, which changes what top_n, precision and f1 mean. - Fix the neutral-data download cell, which failed with "poetry: command not found" because a %%bash cell starts a non-login shell that never picks up ~/.local/bin. It now reuses sys.executable, which is already the Poetry venv. - Strip tqdm progress-bar widget outputs and absolute local paths from the saved outputs. Co-authored-by: Cursor --- examples/sentinel_against_hate.ipynb | 5257 ++++++++++++++------------ 1 file changed, 2753 insertions(+), 2504 deletions(-) diff --git a/examples/sentinel_against_hate.ipynb b/examples/sentinel_against_hate.ipynb index ad0abfd..e1bbcf7 100644 --- a/examples/sentinel_against_hate.ipynb +++ b/examples/sentinel_against_hate.ipynb @@ -1,2570 +1,2819 @@ { - "cells": [ + "cells": [ + { + "cell_type": "markdown", + "id": "6bbeca2f", + "metadata": {}, + "source": [ + "# Hate Speech Detection with Sentinel: Building and Evaluating a Robust Detection Model\n", + "\n", + "This example demonstrates how to use the Sentinel library to build a robust hate speech detection model with minimal examples and evaluate it across different content sources. Sentinel excels at detecting extremely rare classes of harmful content using contrastive learning principles.\n", + "\n", + "## Data Sources and Methodology\n", + "\n", + "### Training Data:\n", + "- **Positive Examples (~1,500)**: Extracted from the Southern Poverty Law Center's (SPLC) Extremist Files database, specifically from \"In Their Own Words\" sections that contain direct quotes from individuals or groups identified as extremists. These quotes represent authentic examples of hate speech from various extremist ideologies.\n", + "- **Negative Examples (~15,000)**: Obtained from the Lex Fridman podcast dataset, which contains neutral, intellectual discussions. The 10:1 ratio of negative-to-positive examples reflects the typical imbalance in real-world content.\n", + "\n", + "### Evaluation Data:\n", + "- **Test Positives**: Podcast transcripts, known for controversial and potentially harmful content.\n", + "- **Test Negatives**: Different episodes from Lex Fridman's podcast, providing a contrast to potentially harmful content.\n", + "\n", + "### Workflow:\n", + "1. **Data Preparation**: Extract extremist quotes from SPLC and neutral content from conversational datasets. Segment longer texts into manageable chunks of 512 tokens with 128-token stride.\n", + "2. **Model Building**: Create embeddings of both positive and negative examples using the MiniLM-L6-v2 model, then build a Sentinel index to measure semantic similarity to the hate speech class.\n", + "3. **Evaluation**: Score content at both segment and episode levels using contrastive learning and statistical measures like skewness to identify patterns across observations.\n", + "4. **Analysis**: Compare the distribution of hate speech affinity scores between a controversial podcast and Lex Fridman content to validate the model's effectiveness.\n", + "\n", + "This approach demonstrates how Sentinel can detect rare patterns of concerning content with limited training examples, focusing on the semantic similarity to known extremist language rather than requiring extensive labeled datasets." + ] + }, + { + "cell_type": "markdown", + "id": "caac3997", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "Before running this notebook, ensure you have installed the example dependencies and registered the Poetry environment as a Jupyter kernel. See the project README for details.\n", + "\n", + "### Downloading the data\n", + "\n", + "There is a script for downloading the Extremist Files from the Southern Poverty Law Center (SPLC) website, the neutral dataset, and the controversial podcast examples" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d3aa300e", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "import pandas as pd\n", + "from sentinel import SentinelLocalIndex\n", + "import re\n", + "from pathlib import Path\n", + "from tqdm.notebook import tqdm\n", + "import random\n", + "import pandas as pd\n", + "from tqdm.notebook import tqdm\n", + "import os\n", + "import requests\n", + "from datasets.utils.file_utils import get_datasets_user_agent" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "64a69da0", + "metadata": {}, + "outputs": [], + "source": [ + "from transformers import AutoTokenizer\n", + "\n", + "# Use MiniLM tokenizer\n", + "tokenizer = AutoTokenizer.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n", + "\n", + "def segment_text(text, max_tokens=512, stride=128):\n", + " tokens = tokenizer.encode(text, add_special_tokens=False)\n", + " segments = []\n", + " start = 0\n", + " while start < len(tokens):\n", + " end = start + max_tokens\n", + " segment_tokens = tokens[start:end]\n", + " segment_text = tokenizer.decode(segment_tokens)\n", + " segments.append(segment_text)\n", + " if end >= len(tokens):\n", + " break\n", + " start += stride\n", + " return segments" + ] + }, + { + "cell_type": "markdown", + "id": "c2965db6-192f-45dd-8d9a-88e5b92fa88d", + "metadata": {}, + "source": [ + "## Download and parse the positive example\n", + "Let's build the hate speech dataset using the example from Southern Poverty Law Center's (SPLC)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f0401ec2", + "metadata": {}, + "outputs": [ { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Hate Speech Detection with Sentinel: Building and Evaluating a Robust Detection Model\n", - "\n", - "This example demonstrates how to use the Sentinel library to build a robust hate speech detection model with minimal examples and evaluate it across different content sources. Sentinel excels at detecting extremely rare classes of harmful content using contrastive learning principles.\n", - "\n", - "## Data Sources and Methodology\n", - "\n", - "### Training Data:\n", - "- **Positive Examples (~1,500)**: Extracted from the Southern Poverty Law Center's (SPLC) Extremist Files database, specifically from \"In Their Own Words\" sections that contain direct quotes from individuals or groups identified as extremists. These quotes represent authentic examples of hate speech from various extremist ideologies.\n", - "- **Negative Examples (~15,000)**: Obtained from the Lex Fridman podcast dataset, which contains neutral, intellectual discussions. The 10:1 ratio of negative-to-positive examples reflects the typical imbalance in real-world content.\n", - "\n", - "### Evaluation Data:\n", - "- **Test Positives**: Podcast transcripts, known for controversial and potentially harmful content.\n", - "- **Test Negatives**: Different episodes from Lex Fridman's podcast, providing a contrast to potentially harmful content.\n", - "\n", - "### Workflow:\n", - "1. **Data Preparation**: Extract extremist quotes from SPLC and neutral content from conversational datasets. Segment longer texts into manageable chunks of 512 tokens with 128-token stride.\n", - "2. **Model Building**: Create embeddings of both positive and negative examples using the MiniLM-L6-v2 model, then build a Sentinel index to measure semantic similarity to the hate speech class.\n", - "3. **Evaluation**: Score content at both segment and episode levels using contrastive learning and statistical measures like skewness to identify patterns across observations.\n", - "4. **Analysis**: Compare the distribution of hate speech affinity scores between a controversial podcast and Lex Fridman content to validate the model's effectiveness.\n", - "\n", - "This approach demonstrates how Sentinel can detect rare patterns of concerning content with limited training examples, focusing on the semantic similarity to known extremist language rather than requiring extensive labeled datasets." - ], - "id": "6bbeca2f" + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 249 markdown files.\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Setup\n", - "\n", - "Before running this notebook, ensure you have installed the example dependencies and registered the Poetry environment as a Jupyter kernel. See the project README for details.\n", - "\n", - "### Downloading the data\n", - "\n", - "There is a script for downloading the Extremist Files from the Southern Poverty Law Center (SPLC) website, the neutral dataset, and the controversial podcast examples" - ], - "id": "caac3997" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: Found another 'In Their Own Words' section in data/splc_extremist_files/matt-walsh.md at line 28, but already in one.\n", + "Previous section lines: ['Background: ‘Shock jock’ and violent rhetoric', '‘Beginning To Talk Like Us:’ Walsh’s white supremacist rhetoric', 'Targeting children’s hospitals', 'Matt Walsh is a blogger and talk show host for the Daily Wire website. He frequently demonizes LGBTQ+ people and promotes racist and anti-transgender conspiracy theories. Walsh leads a campaign against gender-affirming health care that has targeted American hospitals with harassment and has advocated executing doctors who provide health care to transgender people. Walsh is also known for perpetuating the notion of anti-white racism, which is grounded in white supremacy, and spreading conspiracy theories about supposed campaigns of anti-white violence.', 'Walsh is a self-described “', 'theocratic fascist', '” who is one of the most prominent anti-transgender voices in American right-wing media. Walsh sometimes suggests his most extreme comments are satirical or in jest, as when he explained why he describes himself as a theocratic fascist. However, his comments regularly reflect male and white supremacy and transphobia and are often used by radical right-wing extremists against marginalized communities. Walsh hosts “The Matt Walsh Show” on the Daily Wire and has written several books that promote anti-transgender pseudoscience and conspiracy theories. Walsh has advocated political violence and violence against transgender people and medical providers that give gender-affirming care. Some of these facilities have received bomb threats and have faced the suspension of care and increased security measures.']\n", + "Extracted 2515 paragraphs from 'In Their Own Words' sections.\n" + ] + } + ], + "source": [ + "# Define function to extract \"In Their Own Words\" sections from markdown files using line-by-line parsing\n", + "def extract_in_their_own_words(md_file):\n", + " try:\n", + " # Extract filename to use as identifier\n", + " filename = Path(md_file).stem\n", + " \n", + " # Read the file line by line\n", + " with open(md_file, 'r', encoding='utf-8', errors='ignore') as f:\n", + " lines = f.readlines()\n", + " \n", + " # Simple pattern to match \"In his/her/their own words\" headers\n", + " own_words_pattern = re.compile(r'^(?:#+\\s*)?[Ii]n (?:[Hh](?:is|er)|[Tt]heir) [Oo]wn [Ww]ords:?')\n", + " background_pattern = re.compile(r'^(?:#+\\s*)?(Background|BACKGROUND):?\\s*$')\n", + " \n", + " # Variables to track state\n", + " in_own_words_section = False\n", + " current_section_lines = []\n", + " all_paragraphs = []\n", + " sections_found = 0\n", + " \n", + " # Process each line\n", + " for i, line in enumerate(lines):\n", + " line = line.strip()\n", + " if not line:\n", + " continue\n", + "\n", + " # Check if this is an \"In their own words\" heading\n", + " starting_own_words_section = own_words_pattern.match(line)\n", + "\n", + " if starting_own_words_section:\n", + " if in_own_words_section:\n", + " # This shouldnot happen. Print useful debug information\n", + " print(f\"Warning: Found another 'In Their Own Words' section in {md_file} at line {i + 1}, but already in one.\")\n", + " print(f\"Previous section lines: {current_section_lines}\")\n", + " current_section_lines = [] # Reset current section lines\n", + " else:\n", + " in_own_words_section = True\n", + " sections_found += 1\n", + " # Skip this line (heading) and start collecting content \n", + " continue\n", + " \n", + " # Check if we've reached a \"Background\" or another header section\n", + " if in_own_words_section and (background_pattern.match(line) or (line.startswith('#') and len(line) > 1)):\n", + " # Process the current section\n", + " paragraphs = process_section(current_section_lines)\n", + " all_paragraphs.extend({'filename': filename, 'paragraph': p} for p in paragraphs)\n", + " current_section_lines = []\n", + " in_own_words_section = False\n", + " continue\n", + " \n", + " # If we're in a section, collect the line\n", + " if in_own_words_section:\n", + " current_section_lines.append(line)\n", + " \n", + " # Don't forget to process the last section if we're still in one at the end of the file\n", + " if current_section_lines:\n", + " if not in_own_words_section:\n", + " print(f\"Warning: Reached end of file in {md_file} without closing 'In Their Own Words' section.\")\n", + " print(f\"Collected lines: {current_section_lines}\")\n", + " paragraphs = process_section(current_section_lines)\n", + " all_paragraphs.extend({'filename': filename, 'paragraph': p} for p in paragraphs)\n", + " \n", + " # if sections_found > 0:\n", + " # print(f\"Processing {md_file}: found {sections_found} 'In Their Own Words' sections\")\n", + " \n", + " return all_paragraphs\n", + " except Exception as e:\n", + " print(f\"Error processing {md_file}: {e}\")\n", + " return []\n", + "\n", + "# Helper function to process collected section lines into paragraphs\n", + "def process_section(lines):\n", + " if not lines:\n", + " return []\n", + " \n", + " # Skip if the first line starts with \"Background\" (table of contents)\n", + " if lines and lines[0].strip().startswith(\"Background\"):\n", + " return []\n", + " \n", + " quotes = []\n", + " \n", + " for line in lines:\n", + " line = line.strip()\n", + " if not line:\n", + " continue\n", + " \n", + " # Citation patterns - typically start with a dash, quote mark, or include a year/reference\n", + " is_citation = False\n", + " \n", + " # Patterns that strongly indicate a citation rather than a quote\n", + " if (\n", + " re.match(r'^(—|â|\\xa0?—|\\xa0?–|\"|â\\x80\\x94)$', line) or\n", + " # Lines ending with a year\n", + " re.search(r' \\d{4}\\.?\\s*$', line)\n", + " ) and len(line) < 100:\n", + " # If it looks like a citation, skip it\n", + " is_citation = True\n", + " \n", + " # If not a citation, add as a quote\n", + " if not is_citation:\n", + " quotes.append(line)\n", + " \n", + " # Filter out very short quotes (likely fragments)\n", + " quotes = [q for q in quotes if len(q) > 10]\n", + " \n", + " return quotes\n", + "\n", + "# Find all markdown files in the SPLC Extremist Files scraped dataset directory\n", + "splc_data_dir = \"data/splc_extremist_files\"\n", + "md_files = []\n", + "if os.path.exists(splc_data_dir):\n", + " md_files = glob.glob(os.path.join(splc_data_dir, \"**\", \"*.md\"), recursive=True)\n", + " print(f\"Found {len(md_files)} markdown files.\")\n", + "else:\n", + " print(\"Dataset directory not found. Please run the download cell first.\")\n", + "\n", + "# If we find markdown files, extract \"In Their Own Words\" sections\n", + "if md_files:\n", + " # Process all markdown files and collect paragraphs\n", + " all_paragraphs = []\n", + " for md_file in tqdm(md_files, desc=\"Processing markdown files\"):\n", + " paragraphs = extract_in_their_own_words(md_file)\n", + " all_paragraphs.extend(paragraphs)\n", + " \n", + " # Create a DataFrame from the extracted paragraphs\n", + " own_words_df = pd.DataFrame(all_paragraphs)\n", + " \n", + " if not own_words_df.empty:\n", + " print(f\"Extracted {len(own_words_df)} paragraphs from 'In Their Own Words' sections.\")\n", + " \n", + " else:\n", + " print(\"No 'In Their Own Words' sections found in the markdown files.\")\n", + "else:\n", + " print(\"No markdown files found.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4b2ac613", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "metadata": {}, - "source": [ - "import os\n", - "import glob\n", - "import pandas as pd\n", - "from sentinel import SentinelLocalIndex\n", - "import re\n", - "from pathlib import Path\n", - "from tqdm.notebook import tqdm\n", - "import random\n", - "import pandas as pd\n", - "from tqdm.notebook import tqdm\n", - "import os\n", - "import requests\n", - "from datasets.utils.file_utils import get_datasets_user_agent" - ], - "execution_count": 1, - "outputs": [], - "id": "d3aa300e" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Removing 5 files with too many quotes: ['tucker-carlson', 'center-immigration-studies', 'matt-walsh', 'mike-cernovich', 'paul-nehlen']\n" + ] + } + ], + "source": [ + "# Remove quotes from files that produced too many quotes, since these are probably malformatted and the extraction failed\n", + "files_with_too_many_quotes = own_words_df['filename'].value_counts()[own_words_df['filename'].value_counts() > 100].index.tolist()\n", + "print(f\"Removing {len(files_with_too_many_quotes)} files with too many quotes: {files_with_too_many_quotes}\")\n", + "own_words_df = own_words_df[~own_words_df['filename'].isin(files_with_too_many_quotes)]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f95affd4", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "metadata": {}, - "source": [ - "from transformers import AutoTokenizer\n", - "\n", - "# Use MiniLM tokenizer\n", - "tokenizer = AutoTokenizer.from_pretrained(\"sentence-transformers/all-MiniLM-L6-v2\")\n", - "\n", - "def segment_text(text, max_tokens=512, stride=128):\n", - " tokens = tokenizer.encode(text, add_special_tokens=False)\n", - " segments = []\n", - " start = 0\n", - " while start < len(tokens):\n", - " end = start + max_tokens\n", - " segment_tokens = tokens[start:end]\n", - " segment_text = tokenizer.decode(segment_tokens)\n", - " segments.append(segment_text)\n", - " if end >= len(tokens):\n", - " break\n", - " start += stride\n", - " return segments" - ], - "execution_count": 2, - "outputs": [], - "id": "64a69da0" + "name": "stdout", + "output_type": "stream", + "text": [ + "DataFrame shape: (1516, 2)\n", + "Random 30 rows:\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download and parse the positive example\n", - "Let's build the hate speech dataset using the example from Southern Poverty Law Center's (SPLC)\n" + "data": { + "text/html": [ + "
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filenameparagraph
51richard-bertrand-spencer“We’re going to be back here, and we’re going to humiliate all of these people who opposed us. We’ll be back here 1,000 times if necessary. I always win. Because I have the will to win, I keep going until I win.”
168tim-wildmon“[Islam] is in fact a religion of war, violence, intolerance, and physical persecution of non-Muslims.”
2468misogynist-incels“Jews heavily pushed feminism. Roasties have such high standards because they are ‘liberated’. You probably would have gotten your d*** wet by now if it weren’t for feminism and for Jews.”
1040kyle-bristow— Quoted in the
422raymond-cattell“Suppose, as may well be the case, that one of these races is naturally courageous, self-sacrificing and enterprising and the other less so. The group will continue to prosper owing to the activities of inventors and explorers of the first race, who, as is generally the rule, will not pass on the usual number of children to the next generation. The nation will be successful in war because the same race has actively responded to the call to arms and to self-sacrifice. Throughout these activities, this first race will on an average be giving more to the group than it can itself recoup. Eventually only the second race will inherit the group advantages acquired largely by the first racial compound. Then like a huge parasite which has devoured its host, will the nation be bereft of all the qualities that gave it power, remain a monstrous frustration of evolution, a biological abortion able in virtue of its inherited wealth, to do untold damage to neighboring races naturally more capable. The hatred and abhorrence which many peoples feel for the Jewish (and to some extent Mongolian) practice of living in other nations, instead of forming an independent, self-sustained group of their own, comes from a deep intuitive feeling that somehow it is not ‘playing the game.’ Because our unbiologically-minded civilization cannot perceive or appreciate any intellectual causes for these feelings they are readily branded as ‘prejudice’ by would-be intellectuals.”
1479joseph-francis-farahPolygamy? Sure.
585bill-white— Headline for 2008 article about then-presidential candidate Barack Obama who was depicted in the middle of a rifle scope on the cover of White’s American National Socialist Workers Party’s magazine
220lydia-brimelow“As the President of the VDARE Foundation, I work alongside my husband, Editor of VDARE.com, Peter Brimelow, to defeat the post-1964 Immigration Act disaster that has set America on a course of destruction.”
2467misogynist-incels– Comment on an incel forum post titled “‘They are animals…’ ER was right,”
2439james-lindsayIf anyone causes one of these little ones—those who believe in me—to stumble, it would be better for them to have a large millstone hung around their neck and to be drowned in the depths of the sea.”)
1535jeff-berry— 1998 speech at a Klan rally in Jasper, Texas, after the truck-dragging murder there of James Byrd Jr.
49harry-cooper“We must remember that official government sources state definitely that Hitler committed suicide in the bunker and that the body found not far from the Chancellery was indeed that of Martin Bormann. … Naturally we must believe what the governments tell us, like John F. Kennedy was shot by one madman using a worthless rifle when nobody with any sense buys that story except the Warren Commission. … The list of lies by the world’s governments could by themselves, fill a major size book but you already know that.”
535david-irving— 1991 speech to a Canadian audience
175jack-posobiec“StoptheSteal 2020 is coming.” – Twitter, Sept. 7, 2020, a full two months before news outlets declared Joe Biden the winner of the 2020 presidential election
1464willis-cartoThe Barnes Review
405proud-boys“All the heroes of BLM and Antifa are degenerate criminal lowlifes or pedophile rapists. I don’t lose any sleep when they are justly removed from society.” – A Telegram channel associated with the Proud Boys, Sept. 22, 2020
846greg-johnson— “Dealing with the Holocaust
598atomwaffen-division“Pro f—– propaganda is working! AIDS is spreading like wildfire. Dead f—— couldn’t make us happier! Hail AIDS!” – Atomwaffen Division website
2493mark-weber“Around the world awareness is growing that the ‘Holocaust’ campaign is a major weapon in the Jewish-Zionist arsenal, that it is used to justify otherwise unjustifiable Israeli policies, and as a powerful tool for blackmailing enormous sums of money from Americans and Europeans.”
1052gays-against-groomers“The modern trans movement is radicalizing activists into terrorists. […] Trans terrorism is on the rise. […] This sends a message: if you don’t let us mutilate children’s genitals, then we will shoot yours.” – GAG then-Secretary David Leatherwood, “Real America with Dan Ball,” in response to the Covenant School shooting in Nashville, Tennessee,
2485john-de-nugent“They [the Jews] control the media and so it spews lies against the enemy they fear most. And the Jews now misrule our beloved country of America, bringing it down by huge bribes, media brainwashing and blackmail of our officials, and police, and military officers both high and low. And worst of all, by a national network of organized pedophiles, child molesting and child-murdering adults who once involved in that network, one orgy at a time, are photographed there, captured forever on film. That film is stored away for future blackmail use. Those officials cannot quit even if they want to.”
203andrew-anglin“Look, I hate women. I think they deserve to be beaten, raped and locked in cages.” – Daily Stomer, July 2018
1271lieutenant-general-william-g-jerry-boykin-ret“If we don’t take a stand here, it’s only a matter of time before biological men will be able to share showers and locker rooms with women and girls all across America! … Many on the Left believe that biological men have a right to share showers, locker rooms, and bathrooms with women and little girls as long as they identify as female at that particular point in time — and that anyone who disagrees with that should be punished!”
297alt-right“Immigration is a kind of proxy war—and maybe a last stand—for White Americans, who are undergoing a painful recognition that, unless dramatic action is taken, their grandchildren will live in a country that is alien and hostile.”
453garrett-hardin—“How Diversity Should be Nurtured,”
367alex-jones“Imagine how bad she [Hillary Clinton] smells, man? I’m told her and Obama, just stink, stink, stink, stink. You can’t wash that evil off, man. Told there’s a rotten smell around Hillary. I’m not kidding, people say, they say — folks, I’ve been told this by high-up folks. They say listen, Obama and Hillary both smell like sulfur. … I’ve talked to people that are in protective details, they’re scared of her. And they say listen, she’s a frickin’ demon and she stinks and so does Obama. I go, like what? Sulfur. They smell like hell.” — “The Alex Jones Show,” Oct. 10, 2016
1055gays-against-groomers“Every year, THOUSANDS of children are exposed to displays of sexual depravity at Pride events, and MILLIONS applaud it. This year, Gays Against Groomers will not stand for this destruction of childhood innocence. Be seeing you REAL soon.” – GAG Illinois X (formerly Twitter),
237chuck-baldwin“I believe homosexuality is moral perversion and deserves no special consideration under the law. … I believe the South was right in the War Between the States, and I am not a racist.”
1553craig-cobb—Nov. 16, 2013, videotaped rant against a resident while “patrolling” the streets of Leith
677family-research-council“The reality is, homosexuals have entered the Scouts in the past for predatory purposes.”
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We’ll be back here 1,000 times if necessary. I always win. Because I have the will to win, I keep going until I win.” \n", + "168 “[Islam] is in fact a religion of war, violence, intolerance, and physical persecution of non-Muslims.” \n", + "2468 “Jews heavily pushed feminism. Roasties have such high standards because they are ‘liberated’. You probably would have gotten your d*** wet by now if it weren’t for feminism and for Jews.” \n", + "1040 — Quoted in the \n", + "422 “Suppose, as may well be the case, that one of these races is naturally courageous, self-sacrificing and enterprising and the other less so. The group will continue to prosper owing to the activities of inventors and explorers of the first race, who, as is generally the rule, will not pass on the usual number of children to the next generation. The nation will be successful in war because the same race has actively responded to the call to arms and to self-sacrifice. Throughout these activities, this first race will on an average be giving more to the group than it can itself recoup. Eventually only the second race will inherit the group advantages acquired largely by the first racial compound. Then like a huge parasite which has devoured its host, will the nation be bereft of all the qualities that gave it power, remain a monstrous frustration of evolution, a biological abortion able in virtue of its inherited wealth, to do untold damage to neighboring races naturally more capable. The hatred and abhorrence which many peoples feel for the Jewish (and to some extent Mongolian) practice of living in other nations, instead of forming an independent, self-sustained group of their own, comes from a deep intuitive feeling that somehow it is not ‘playing the game.’ Because our unbiologically-minded civilization cannot perceive or appreciate any intellectual causes for these feelings they are readily branded as ‘prejudice’ by would-be intellectuals.” \n", + "1479 Polygamy? Sure. \n", + "585 — Headline for 2008 article about then-presidential candidate Barack Obama who was depicted in the middle of a rifle scope on the cover of White’s American National Socialist Workers Party’s magazine \n", + "220 “As the President of the VDARE Foundation, I work alongside my husband, Editor of VDARE.com, Peter Brimelow, to defeat the post-1964 Immigration Act disaster that has set America on a course of destruction.” \n", + "2467 – Comment on an incel forum post titled “‘They are animals…’ ER was right,” \n", + "2439 If anyone causes one of these little ones—those who believe in me—to stumble, it would be better for them to have a large millstone hung around their neck and to be drowned in the depths of the sea.”) \n", + "1535 — 1998 speech at a Klan rally in Jasper, Texas, after the truck-dragging murder there of James Byrd Jr. \n", + "49 “We must remember that official government sources state definitely that Hitler committed suicide in the bunker and that the body found not far from the Chancellery was indeed that of Martin Bormann. … Naturally we must believe what the governments tell us, like John F. Kennedy was shot by one madman using a worthless rifle when nobody with any sense buys that story except the Warren Commission. … The list of lies by the world’s governments could by themselves, fill a major size book but you already know that.” \n", + "535 — 1991 speech to a Canadian audience \n", + "175 “StoptheSteal 2020 is coming.” – Twitter, Sept. 7, 2020, a full two months before news outlets declared Joe Biden the winner of the 2020 presidential election \n", + "1464 The Barnes Review \n", + "405 “All the heroes of BLM and Antifa are degenerate criminal lowlifes or pedophile rapists. I don’t lose any sleep when they are justly removed from society.” – A Telegram channel associated with the Proud Boys, Sept. 22, 2020 \n", + "846 — “Dealing with the Holocaust \n", + "598 “Pro f—– propaganda is working! AIDS is spreading like wildfire. Dead f—— couldn’t make us happier! Hail AIDS!” – Atomwaffen Division website \n", + "2493 “Around the world awareness is growing that the ‘Holocaust’ campaign is a major weapon in the Jewish-Zionist arsenal, that it is used to justify otherwise unjustifiable Israeli policies, and as a powerful tool for blackmailing enormous sums of money from Americans and Europeans.” \n", + "1052 “The modern trans movement is radicalizing activists into terrorists. […] Trans terrorism is on the rise. […] This sends a message: if you don’t let us mutilate children’s genitals, then we will shoot yours.” – GAG then-Secretary David Leatherwood, “Real America with Dan Ball,” in response to the Covenant School shooting in Nashville, Tennessee, \n", + "2485 “They [the Jews] control the media and so it spews lies against the enemy they fear most. And the Jews now misrule our beloved country of America, bringing it down by huge bribes, media brainwashing and blackmail of our officials, and police, and military officers both high and low. And worst of all, by a national network of organized pedophiles, child molesting and child-murdering adults who once involved in that network, one orgy at a time, are photographed there, captured forever on film. That film is stored away for future blackmail use. Those officials cannot quit even if they want to.” \n", + "203 “Look, I hate women. I think they deserve to be beaten, raped and locked in cages.” – Daily Stomer, July 2018 \n", + "1271 “If we don’t take a stand here, it’s only a matter of time before biological men will be able to share showers and locker rooms with women and girls all across America! … Many on the Left believe that biological men have a right to share showers, locker rooms, and bathrooms with women and little girls as long as they identify as female at that particular point in time — and that anyone who disagrees with that should be punished!” \n", + "297 “Immigration is a kind of proxy war—and maybe a last stand—for White Americans, who are undergoing a painful recognition that, unless dramatic action is taken, their grandchildren will live in a country that is alien and hostile.” \n", + "453 —“How Diversity Should be Nurtured,” \n", + "367 “Imagine how bad she [Hillary Clinton] smells, man? I’m told her and Obama, just stink, stink, stink, stink. You can’t wash that evil off, man. Told there’s a rotten smell around Hillary. I’m not kidding, people say, they say — folks, I’ve been told this by high-up folks. They say listen, Obama and Hillary both smell like sulfur. … I’ve talked to people that are in protective details, they’re scared of her. And they say listen, she’s a frickin’ demon and she stinks and so does Obama. I go, like what? Sulfur. They smell like hell.” — “The Alex Jones Show,” Oct. 10, 2016 \n", + "1055 “Every year, THOUSANDS of children are exposed to displays of sexual depravity at Pride events, and MILLIONS applaud it. This year, Gays Against Groomers will not stand for this destruction of childhood innocence. Be seeing you REAL soon.” – GAG Illinois X (formerly Twitter), \n", + "237 “I believe homosexuality is moral perversion and deserves no special consideration under the law. … I believe the South was right in the War Between the States, and I am not a racist.” \n", + "1553 —Nov. 16, 2013, videotaped rant against a resident while “patrolling” the streets of Leith \n", + "677 “The reality is, homosexuals have entered the Scouts in the past for predatory purposes.” " + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Define function to extract \"In Their Own Words\" sections from markdown files using line-by-line parsing\n", - "def extract_in_their_own_words(md_file):\n", - " try:\n", - " # Extract filename to use as identifier\n", - " filename = Path(md_file).stem\n", - " \n", - " # Read the file line by line\n", - " with open(md_file, 'r', encoding='utf-8', errors='ignore') as f:\n", - " lines = f.readlines()\n", - " \n", - " # Simple pattern to match \"In his/her/their own words\" headers\n", - " own_words_pattern = re.compile(r'^(?:#+\\s*)?[Ii]n (?:[Hh](?:is|er)|[Tt]heir) [Oo]wn [Ww]ords:?')\n", - " background_pattern = re.compile(r'^(?:#+\\s*)?(Background|BACKGROUND):?\\s*$')\n", - " \n", - " # Variables to track state\n", - " in_own_words_section = False\n", - " current_section_lines = []\n", - " all_paragraphs = []\n", - " sections_found = 0\n", - " \n", - " # Process each line\n", - " for i, line in enumerate(lines):\n", - " line = line.strip()\n", - " if not line:\n", - " continue\n", - "\n", - " # Check if this is an \"In their own words\" heading\n", - " starting_own_words_section = own_words_pattern.match(line)\n", - "\n", - " if starting_own_words_section:\n", - " if in_own_words_section:\n", - " # This shouldnot happen. Print useful debug information\n", - " print(f\"Warning: Found another 'In Their Own Words' section in {md_file} at line {i + 1}, but already in one.\")\n", - " print(f\"Previous section lines: {current_section_lines}\")\n", - " current_section_lines = [] # Reset current section lines\n", - " else:\n", - " in_own_words_section = True\n", - " sections_found += 1\n", - " # Skip this line (heading) and start collecting content \n", - " continue\n", - " \n", - " # Check if we've reached a \"Background\" or another header section\n", - " if in_own_words_section and (background_pattern.match(line) or (line.startswith('#') and len(line) > 1)):\n", - " # Process the current section\n", - " paragraphs = process_section(current_section_lines)\n", - " all_paragraphs.extend({'filename': filename, 'paragraph': p} for p in paragraphs)\n", - " current_section_lines = []\n", - " in_own_words_section = False\n", - " continue\n", - " \n", - " # If we're in a section, collect the line\n", - " if in_own_words_section:\n", - " current_section_lines.append(line)\n", - " \n", - " # Don't forget to process the last section if we're still in one at the end of the file\n", - " if current_section_lines:\n", - " if not in_own_words_section:\n", - " print(f\"Warning: Reached end of file in {md_file} without closing 'In Their Own Words' section.\")\n", - " print(f\"Collected lines: {current_section_lines}\")\n", - " paragraphs = process_section(current_section_lines)\n", - " all_paragraphs.extend({'filename': filename, 'paragraph': p} for p in paragraphs)\n", - " \n", - " # if sections_found > 0:\n", - " # print(f\"Processing {md_file}: found {sections_found} 'In Their Own Words' sections\")\n", - " \n", - " return all_paragraphs\n", - " except Exception as e:\n", - " print(f\"Error processing {md_file}: {e}\")\n", - " return []\n", - "\n", - "# Helper function to process collected section lines into paragraphs\n", - "def process_section(lines):\n", - " if not lines:\n", - " return []\n", - " \n", - " # Skip if the first line starts with \"Background\" (table of contents)\n", - " if lines and lines[0].strip().startswith(\"Background\"):\n", - " return []\n", - " \n", - " quotes = []\n", - " \n", - " for line in lines:\n", - " line = line.strip()\n", - " if not line:\n", - " continue\n", - " \n", - " # Citation patterns - typically start with a dash, quote mark, or include a year/reference\n", - " is_citation = False\n", - " \n", - " # Patterns that strongly indicate a citation rather than a quote\n", - " if (\n", - " re.match(r'^(—|â|\\xa0?—|\\xa0?–|\"|â\\x80\\x94)$', line) or\n", - " # Lines ending with a year\n", - " re.search(r' \\d{4}\\.?\\s*$', line)\n", - " ) and len(line) < 100:\n", - " # If it looks like a citation, skip it\n", - " is_citation = True\n", - " \n", - " # If not a citation, add as a quote\n", - " if not is_citation:\n", - " quotes.append(line)\n", - " \n", - " # Filter out very short quotes (likely fragments)\n", - " quotes = [q for q in quotes if len(q) > 10]\n", - " \n", - " return quotes\n", - "\n", - "# Find all markdown files in the SPLC Extremist Files scraped dataset directory\n", - "splc_data_dir = \"data/splc_extremist_files\"\n", - "md_files = []\n", - "if os.path.exists(splc_data_dir):\n", - " md_files = glob.glob(os.path.join(splc_data_dir, \"**\", \"*.md\"), recursive=True)\n", - " print(f\"Found {len(md_files)} markdown files.\")\n", - "else:\n", - " print(\"Dataset directory not found. Please run the download cell first.\")\n", - "\n", - "# If we find markdown files, extract \"In Their Own Words\" sections\n", - "if md_files:\n", - " # Process all markdown files and collect paragraphs\n", - " all_paragraphs = []\n", - " for md_file in tqdm(md_files, desc=\"Processing markdown files\"):\n", - " paragraphs = extract_in_their_own_words(md_file)\n", - " all_paragraphs.extend(paragraphs)\n", - " \n", - " # Create a DataFrame from the extracted paragraphs\n", - " own_words_df = pd.DataFrame(all_paragraphs)\n", - " \n", - " if not own_words_df.empty:\n", - " print(f\"Extracted {len(own_words_df)} paragraphs from 'In Their Own Words' sections.\")\n", - " \n", - " else:\n", - " print(\"No 'In Their Own Words' sections found in the markdown files.\")\n", - "else:\n", - " print(\"No markdown files found.\")" - ], - "execution_count": 3, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Found 249 markdown files.\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6ff47bdd94334db8970505b6b189dc05", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing markdown files: 0%| | 0/249 [00:00 100].index.tolist()\n", - "print(f\"Removing {len(files_with_too_many_quotes)} files with too many quotes: {files_with_too_many_quotes}\")\n", - "own_words_df = own_words_df[~own_words_df['filename'].isin(files_with_too_many_quotes)]" - ], - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Removing 5 files with too many quotes: ['tucker-carlson', 'center-immigration-studies', 'matt-walsh', 'mike-cernovich', 'paul-nehlen']\n" - ] - } - ], - "id": "4b2ac613" - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Max tokens in any paragraph: 322\n", + "Average tokens per paragraph: 49.7\n", + "Number of paragraphs exceeding 512 tokens: 0\n", + "\n", + "Longest paragraph (322 tokens):\n", + "“Only one conclusion is possible… . [T]he broad picture is clear and inescapable: at some point in the foreseeable future the white British people will become a minority in these islands, and whites w...\n" + ] + } + ], + "source": [ + "# Check token counts for some paragraphs\n", + "token_counts = []\n", + "for _, row in own_words_df.iterrows():\n", + " tokens = tokenizer.encode(row['paragraph'], add_special_tokens=False)\n", + " token_counts.append(len(tokens))\n", + "\n", + "print(f\"Max tokens in any paragraph: {max(token_counts)}\")\n", + "print(f\"Average tokens per paragraph: {sum(token_counts)/len(token_counts):.1f}\")\n", + "print(f\"Number of paragraphs exceeding 512 tokens: {sum(1 for count in token_counts if count > 512)}\")\n", + "\n", + "# Check the longest paragraphs\n", + "longest_idx = token_counts.index(max(token_counts))\n", + "print(f\"\\nLongest paragraph ({token_counts[longest_idx]} tokens):\")\n", + "print(own_words_df.iloc[longest_idx]['paragraph'][:200] + \"...\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "609a3723", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "metadata": {}, - "source": [ - "pd.set_option(\"display.max_colwidth\", None)\n", - "pd.set_option(\"display.max_rows\", None)\n", - "\n", - "print(f\"DataFrame shape: {own_words_df.shape}\")\n", - "print(\"Random 30 rows:\")\n", - "display(own_words_df.sample(30, random_state=42))\n", - "\n", - "# Display unique filenames to see how many different extremists were extracted\n", - "print(f\"\\nNumber of unique extremists: {own_words_df['filename'].nunique()}\")\n", - "print(\"Unique extremist quotes:\")\n", - "\n", - "print(own_words_df['filename'].value_counts())\n", - "\n", - "if 8 != len(own_words_df[own_words_df[\"filename\"] == \"daryush-roosh-valizadeh\"]):\n", - " print(\"ERROR: Expected 8 quotes for Daryush Roosh Valizadeh, including one very long citation (117 characters).\")\n", - " display(own_words_df[own_words_df[\"filename\"] == \"daryush-roosh-valizadeh\"])\n", - "\n", - "\n", - "pd.set_option(\"display.max_colwidth\", 500)\n", - "pd.set_option(\"display.max_rows\", 10)\n" - ], - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "text": [ - "DataFrame shape: (2515, 2)\n", - "Random 30 rows:\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/html": [ - "
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617center-immigration-studiesWashington Times
927louis-beam— “New World Order,” 1999 essay by Beam
942bill-whiteIn August 2004, White was convicted of assaulting a woman when she was handing out flyers that identified him as a neo-Nazi landlord. During the court proceedings, White cursed at the woman from the witness stand and was held in contempt of court by the judge. In December 2009, a federal jury found White guilty of threatening several people through intimidating phone calls and internet postings. He was sentenced to 2 ½ years in prison at an April 2010 hearing. In January 2011, White was found guilty of using his website to encourage violence against a jury foreman in the trial of another white supremacist,
973william-pierce— Pierce, quoted in a biography of him called Fame of a Dead Man’s Deeds, on the power of narrative
1967tucker-carlsona reporter later exposed
1116stefan-molyneuxCollective Guilt for Fun and Profit”,
1375malik-zulu-shabazzAudience: “Jews!”
170paul-nehlenNehlen’s 2016 campaign for Congress was ideologically similar to Trump’s and was buoyed by FOX News and other outlets with large, national audiences. Breitbart.com ran several stories on Nehlen, for example. Right-wing pundits such as
2025tucker-carlson“Tucker is ultimately on our side. He can get millions and millions of boomers to nod along with talking points that would have only been seen on VDare or American Renaissance a few years ago,” Greer said on his podcast in the Spring of 2021,
321ronald-doggett“Here in the state of Virginia our Confederate History Month proclamations became so watered down with nods towards the non-Whites they weren’t even worth supporting. We can’t ever have anything just for Whites…I wonder what a true Confederate (White Supremacist) brought to today’s time would think of the cowardly defenders of his cause.”
1000scott-lively“It is not mere coincidence that the emperors of Rome in its horrific final days were homosexual; that Adolf Hitler’s inner circle were mostly homosexual; and that nearly all of the most prolific serial killers in U.S. history were homosexual. It is not mere coincidence that America’s cultural decline parallels the rise of ‘gay rights.’”
2331proud-boys“I am not afraid to speak out about the atrocities that whites and people of European descent face not only here in this country but in Western nations across the world. The war against whites, and Europeans and Western society is very real and it’s time we all started talking about it and stopped worrying about political correctness and optics.” – Kyle Chapman, who formed the Fraternal Order of Alt-Knights, a paramilitary wing of the Proud Boys, Unite America First Peace Rally, Sacramento, California, July 8, 2017
408moms-liberty“Gender dysphoria is a mental health disorder that is being normalized by predators across the USA. California kids are at extreme risk from predatory adults. Now they want to ‘liberate’ children all over the country. Does a double mastectomy on a preteen sound like progress?’
651center-immigration-studiesAmerican Renaissance
1614tomislav-sunicPostmortem Report: Cultural Examinations from Postmodernity
1135stefan-molyneux“If we could just get people to be nice to their babies for five years straight, that would be it for war, drug abuse, addiction, promiscuity, sexually transmitted diseases. Almost all would be completely eliminated, because they all arise from dysfunctional early childhood experiences, which are all run by women.”
582center-immigration-studies(CCC), which Charleston shooter
1961tucker-carlsonserved as a platform
1640michael-flynn“We the people are proud to proclaim that the United States of America is ‘One Nation under God’ – in this public profession of faith in God, we recognize his Lordship over our country, and we proudly stand beneath the banner of Christ and our flag in which millions have sacrificed their very lives for. In scripture through the strength and commitment of Matthew, he said, ‘Whoever is not with Me is against Me.’” – Blog post titled
1073paul-elam“P—- is the only real empowerment women will ever know. Put all the hopelessly wishful thinking of feminist ideology aside and what remains is the fact that it is men, and pretty much men only, who draw power from accomplishment, who invent technology, build nations, cure disease, create empires and generally advance civilization. Women – whether acknowledging it makes us feel warm and fuzzy or not – depend on men for all of that and the only tool they have at their disposal to have any sort of influence on any of it is the power of p—- and p—- is powerful indeed…Sexual robotics may well prove to be the best thing that ever happened to women from the standpoint of their humanity…. what would that do to the vast majority of women who would suddenly have to prove their worth as human beings beyond simply being the owners of said p—-?” – Paul Elam, An Ear for Men, Sex Robots: Part 3 – Disempowering P—-, October 2017
1582mike-cernovichDespite making completely unsubstantiated accusations of pedophilia, Cernovich discussed the allegations that aspiring Alabama senator Roy Moore had sexually abused underage girls cautiously. After tweeting that “If it’s true, string the guy up, man. I got no problem with that,” he later retweeted individuals casting doubt on the validity of the accusers, writing: “When you’re lied about in the news daily, as I am, you pause when 40-year-old accusations surface one month away from an election where WaPo endorsed the other candidate.” Taking it further, however, Cernovich then recorded a podcast giving “scientific” advice to Roy Moore,
907nation-islam“Pedophilia and sexual perversion institutionalized in Hollywood and the entertainment industries can be traced to Talmudic principles and Jewish influence. Now Jewish influence – satanic influence under the name of Jew…The wicked practices that govern their industries are largely justified and influenced by such Talmudic principles. The pervasive rape culture, Hollywood’s casting couch, sex trafficking and prostitution, the age-old buck breaking process that emasculates Black men and corrupts Black women.”
613center-immigration-studiesNo such reprimand occurred when Steinlight,
2039tucker-carlsonthe digital publication.
962edgar-steele“Without a pressure release valve, as open racism once provided, an explosion of epic proportions at some time in the future is guaranteed. There will be a race war, the initial skirmishes of which already are being fought in America’s streets, that will bring an altogether new meaning to the concepts of race war and genocide, courtesy of those who claim to abhor racism.”
2060tucker-carlsonTucker Carlson hosted male supremacist Andrew Tate on August 5, 2022. (Twitter)
1627james-timothy-turner“I am not in jail for violating the law. I am in jail because I stood for righteousness and truth in government. Pray that God will crumble the foundations and break the power and strength of the corporation and restore his righteous government in America.”
2186matt-walshIn addition to spreading disinformation about transgender identity and discredited pseudoscience, Walsh’s film relies on propagandist tactics of narrative manipulation in suggesting legal protections for transgender people will lead to people being prosecuted for using the wrong pronouns. And in making numerous
866barry-black— A 1998 comment to a Virginia newspaper
471david-yerushalmi— “Offensive and Defensive Lawfare: Fighting Civilization Jihad in America’s Courts,” Center for Security Policy press release,
\n", - "
" - ], - "text/plain": [ - " filename \\\n", - "617 center-immigration-studies \n", - "927 louis-beam \n", - "942 bill-white \n", - "973 william-pierce \n", - "1967 tucker-carlson \n", - "1116 stefan-molyneux \n", - "1375 malik-zulu-shabazz \n", - "170 paul-nehlen \n", - "2025 tucker-carlson \n", - "321 ronald-doggett \n", - "1000 scott-lively \n", - "2331 proud-boys \n", - "408 moms-liberty \n", - "651 center-immigration-studies \n", - "1614 tomislav-sunic \n", - "1135 stefan-molyneux \n", - "582 center-immigration-studies \n", - "1961 tucker-carlson \n", - "1640 michael-flynn \n", - "1073 paul-elam \n", - "1582 mike-cernovich \n", - "907 nation-islam \n", - "613 center-immigration-studies \n", - "2039 tucker-carlson \n", - "962 edgar-steele \n", - "2060 tucker-carlson \n", - "1627 james-timothy-turner \n", - "2186 matt-walsh \n", - "866 barry-black \n", - "471 david-yerushalmi \n", - "\n", - " paragraph \n", - "617 Washington Times \n", - "927 — “New World Order,” 1999 essay by Beam \n", - "942 In August 2004, White was convicted of assaulting a woman when she was handing out flyers that identified him as a neo-Nazi landlord. During the court proceedings, White cursed at the woman from the witness stand and was held in contempt of court by the judge. In December 2009, a federal jury found White guilty of threatening several people through intimidating phone calls and internet postings. He was sentenced to 2 ½ years in prison at an April 2010 hearing. In January 2011, White was found guilty of using his website to encourage violence against a jury foreman in the trial of another white supremacist, \n", - "973 — Pierce, quoted in a biography of him called Fame of a Dead Man’s Deeds, on the power of narrative \n", - "1967 a reporter later exposed \n", - "1116 Collective Guilt for Fun and Profit”, \n", - "1375 Audience: “Jews!” \n", - "170 Nehlen’s 2016 campaign for Congress was ideologically similar to Trump’s and was buoyed by FOX News and other outlets with large, national audiences. Breitbart.com ran several stories on Nehlen, for example. Right-wing pundits such as \n", - "2025 “Tucker is ultimately on our side. He can get millions and millions of boomers to nod along with talking points that would have only been seen on VDare or American Renaissance a few years ago,” Greer said on his podcast in the Spring of 2021, \n", - "321 “Here in the state of Virginia our Confederate History Month proclamations became so watered down with nods towards the non-Whites they weren’t even worth supporting. We can’t ever have anything just for Whites…I wonder what a true Confederate (White Supremacist) brought to today’s time would think of the cowardly defenders of his cause.” \n", - "1000 “It is not mere coincidence that the emperors of Rome in its horrific final days were homosexual; that Adolf Hitler’s inner circle were mostly homosexual; and that nearly all of the most prolific serial killers in U.S. history were homosexual. It is not mere coincidence that America’s cultural decline parallels the rise of ‘gay rights.’” \n", - "2331 “I am not afraid to speak out about the atrocities that whites and people of European descent face not only here in this country but in Western nations across the world. The war against whites, and Europeans and Western society is very real and it’s time we all started talking about it and stopped worrying about political correctness and optics.” – Kyle Chapman, who formed the Fraternal Order of Alt-Knights, a paramilitary wing of the Proud Boys, Unite America First Peace Rally, Sacramento, California, July 8, 2017 \n", - "408 “Gender dysphoria is a mental health disorder that is being normalized by predators across the USA. California kids are at extreme risk from predatory adults. Now they want to ‘liberate’ children all over the country. Does a double mastectomy on a preteen sound like progress?’ \n", - "651 American Renaissance \n", - "1614 Postmortem Report: Cultural Examinations from Postmodernity \n", - "1135 “If we could just get people to be nice to their babies for five years straight, that would be it for war, drug abuse, addiction, promiscuity, sexually transmitted diseases. Almost all would be completely eliminated, because they all arise from dysfunctional early childhood experiences, which are all run by women.” \n", - "582 (CCC), which Charleston shooter \n", - "1961 served as a platform \n", - "1640 “We the people are proud to proclaim that the United States of America is ‘One Nation under God’ – in this public profession of faith in God, we recognize his Lordship over our country, and we proudly stand beneath the banner of Christ and our flag in which millions have sacrificed their very lives for. In scripture through the strength and commitment of Matthew, he said, ‘Whoever is not with Me is against Me.’” – Blog post titled \n", - "1073 “P—- is the only real empowerment women will ever know. Put all the hopelessly wishful thinking of feminist ideology aside and what remains is the fact that it is men, and pretty much men only, who draw power from accomplishment, who invent technology, build nations, cure disease, create empires and generally advance civilization. Women – whether acknowledging it makes us feel warm and fuzzy or not – depend on men for all of that and the only tool they have at their disposal to have any sort of influence on any of it is the power of p—- and p—- is powerful indeed…Sexual robotics may well prove to be the best thing that ever happened to women from the standpoint of their humanity…. what would that do to the vast majority of women who would suddenly have to prove their worth as human beings beyond simply being the owners of said p—-?” – Paul Elam, An Ear for Men, Sex Robots: Part 3 – Disempowering P—-, October 2017 \n", - "1582 Despite making completely unsubstantiated accusations of pedophilia, Cernovich discussed the allegations that aspiring Alabama senator Roy Moore had sexually abused underage girls cautiously. After tweeting that “If it’s true, string the guy up, man. I got no problem with that,” he later retweeted individuals casting doubt on the validity of the accusers, writing: “When you’re lied about in the news daily, as I am, you pause when 40-year-old accusations surface one month away from an election where WaPo endorsed the other candidate.” Taking it further, however, Cernovich then recorded a podcast giving “scientific” advice to Roy Moore, \n", - "907 “Pedophilia and sexual perversion institutionalized in Hollywood and the entertainment industries can be traced to Talmudic principles and Jewish influence. Now Jewish influence – satanic influence under the name of Jew…The wicked practices that govern their industries are largely justified and influenced by such Talmudic principles. The pervasive rape culture, Hollywood’s casting couch, sex trafficking and prostitution, the age-old buck breaking process that emasculates Black men and corrupts Black women.” \n", - "613 No such reprimand occurred when Steinlight, \n", - "2039 the digital publication. \n", - "962 “Without a pressure release valve, as open racism once provided, an explosion of epic proportions at some time in the future is guaranteed. There will be a race war, the initial skirmishes of which already are being fought in America’s streets, that will bring an altogether new meaning to the concepts of race war and genocide, courtesy of those who claim to abhor racism.” \n", - "2060 Tucker Carlson hosted male supremacist Andrew Tate on August 5, 2022. (Twitter) \n", - "1627 “I am not in jail for violating the law. I am in jail because I stood for righteousness and truth in government. Pray that God will crumble the foundations and break the power and strength of the corporation and restore his righteous government in America.” \n", - "2186 In addition to spreading disinformation about transgender identity and discredited pseudoscience, Walsh’s film relies on propagandist tactics of narrative manipulation in suggesting legal protections for transgender people will lead to people being prosecuted for using the wrong pronouns. And in making numerous \n", - "866 — A 1998 comment to a Virginia newspaper \n", - "471 — “Offensive and Defensive Lawfare: Fighting Civilization Jihad in America’s Courts,” Center for Security Policy press release, " - ] - } - }, - { - "output_type": "stream", - "text": [ - "\n", - "Number of unique extremists: 159\n", - "Unique extremist quotes:\n", - "tucker-carlson 324\n", - "center-immigration-studies 286\n", - "matt-walsh 152\n", - "mike-cernovich 123\n", - "paul-nehlen 114\n", - "stefan-molyneux 28\n", - "greg-johnson 24\n", - "robert-spencer 24\n", - "gays-against-groomers 24\n", - "jack-posobiec 23\n", - "james-lindsay 23\n", - "veterans-patrol 22\n", - "roy-moore 22\n", - "alex-jones 21\n", - "faith-education-commerce 20\n", - "moms-liberty 19\n", - "act-america 19\n", - "august-kreis 19\n", - "family-research-council 19\n", - "pamela-geller 18\n", - "lieutenant-general-william-g-jerry-boykin-ret 17\n", - "proud-boys 17\n", - "david-yerushalmi 17\n", - "richard-bertrand-spencer 16\n", - "frazier-glenn-miller 15\n", - "malik-zulu-shabazz 15\n", - "pickup-artists-alpha-males-self-help 15\n", - "david-duke 15\n", - "roger-pearson 15\n", - "william-h-regnery-ii 15\n", - "center-security-policy 15\n", - "oath-keepers 14\n", - "joseph-francis-farah 14\n", - "michael-levin 14\n", - "cody-rutledge-wilson 14\n", - "elmer-stewart-rhodes 14\n", - "michael-flynn 14\n", - "david-lane 14\n", - "nation-islam 13\n", - "thomas-rousseau 13\n", - "matt-hale 12\n", - "louis-farrakhan 12\n", - "david-irving 12\n", - "scott-lively 12\n", - "peter-brimelow 12\n", - "base 12\n", - "john-tanton 12\n", - "andrew-anglin 12\n", - "bill-white 11\n", - "andrew-weev-auernheimer 11\n", - "charles-murray 11\n", - "nick-fuentes 11\n", - "male-supremacy 11\n", - "misogynist-incels 11\n", - "garrett-hardin 11\n", - "ernst-zundel 11\n", - "don-black 11\n", - "william-shockley 11\n", - "tom-metzger 10\n", - "matthew-heimbach 10\n", - "larry-klayman 10\n", - "vdare 10\n", - "ray-redfeairn 10\n", - "understanding-threat 10\n", - "james-mason 10\n", - "tomislav-sunic 10\n", - "linda-gottfredson 10\n", - "fred-phelps 10\n", - "michael-hill 10\n", - "raymond-cattell 10\n", - "paul-mullet 10\n", - "lydia-brimelow 10\n", - "asatru-folk-assembly 9\n", - "paul-elam 9\n", - "alt-right 9\n", - "michael-ralph-tubbs 9\n", - "jeff-berry 9\n", - "david-barton 9\n", - "craig-cobb 9\n", - "barnes-reviewfoundation-economic-liberty-inc 9\n", - "kyle-bristow 9\n", - "krisanne-hall 9\n", - "jean-philippe-rushton 9\n", - "louis-beam 9\n", - "edgar-steele 9\n", - "tim-wildmon 8\n", - "richard-butler 8\n", - "sam-francis 8\n", - "bo-gritz 8\n", - "tony-perkins 8\n", - "dan-stein 8\n", - "nathan-benjamin-damigo 8\n", - "daryush-roosh-valizadeh 8\n", - "bradley-dean-griffin 8\n", - "nationalist-social-club-nsc-131 8\n", - "remembrance-project 8\n", - "henry-harpending 8\n", - "kevin-strom 8\n", - "bryan-fischer 8\n", - "barry-black 8\n", - "paul-ray-ramsey 8\n", - "kevin-macdonald 8\n", - "frank-gaffney-jr 8\n", - "hal-turner 8\n", - "richard-lynn 7\n", - "jared-taylor 7\n", - "billy-roper 7\n", - "james-timothy-turner 7\n", - "johnny-monoxide-aka-john-ramondetta 7\n", - "atomwaffen-division 7\n", - "willis-carto 7\n", - "arthur-jensen 7\n", - "chuck-baldwin 7\n", - "aryan-brotherhood 7\n", - "wayne-lutton 6\n", - "american-freedom-party 6\n", - "alex-linder 6\n", - "chaya-raichik 6\n", - "thomas-robb 6\n", - "boyd-cathey 6\n", - "proenglish 6\n", - "gary-demar 6\n", - "gary-gerhard-lauck 6\n", - "ronald-doggett 6\n", - "radical-hebrew-israelites 6\n", - "michael-enoch-peinovich 6\n", - "stephen-miller 6\n", - "committee-open-debate-holocaust 5\n", - "michael-brian-vanderboegh-0 5\n", - "virginia-abernethy 5\n", - "april-gaede 5\n", - "larry-pratt 5\n", - "federation-american-immigration-reform 5\n", - "jeff-schoep 5\n", - "jamie-kelso 5\n", - "james-edwards 5\n", - "william-pierce 5\n", - "paul-cameron 5\n", - "kyle-rogers 5\n", - "james-wickstrom 5\n", - "glenn-spencer 4\n", - "barbara-coe 4\n", - "shaun-walker 4\n", - "william-daniel-johnson 4\n", - "ron-edwards 4\n", - "john-de-nugent 4\n", - "erich-gliebe 4\n", - "mark-weber 4\n", - "aryan-freedom-network 4\n", - "james-orien-allsup 4\n", - "kevin-lamb 4\n", - "paul-fromm 3\n", - "roan-garcia-quintana 3\n", - "cliff-kincaid 3\n", - "tom-deweese 3\n", - "jt-ready 3\n", - "vincent-bertollini 3\n", - "gordon-baum 3\n", - "harry-cooper 2\n", - "Name: filename, dtype: int64\n" - ] - } - ], - "id": "f95affd4" + "name": "stdout", + "output_type": "stream", + "text": [ + "Segmented dataset shape: (1516, 4)\n" + ] }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Check token counts for some paragraphs\n", - "token_counts = []\n", - "for _, row in own_words_df.iterrows():\n", - " tokens = tokenizer.encode(row['paragraph'], add_special_tokens=False)\n", - " token_counts.append(len(tokens))\n", - "\n", - "print(f\"Max tokens in any paragraph: {max(token_counts)}\")\n", - "print(f\"Average tokens per paragraph: {sum(token_counts)/len(token_counts):.1f}\")\n", - "print(f\"Number of paragraphs exceeding 512 tokens: {sum(1 for count in token_counts if count > 512)}\")\n", - "\n", - "# Check the longest paragraphs\n", - "longest_idx = token_counts.index(max(token_counts))\n", - "print(f\"\\nLongest paragraph ({token_counts[longest_idx]} tokens):\")\n", - "print(own_words_df.iloc[longest_idx]['paragraph'][:200] + \"...\")" + "data": { + "text/html": [ + "
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filenameparagraph_segmentsegment_idlabel
51richard-bertrand-spencer“ we ’ re going to be back here, and we ’ re going to humiliate all of these people who opposed us. we ’ ll be back here 1, 000 times if necessary. i always win. because i have the will to win, i keep going until i win. ”01
168tim-wildmona [ islam ] is in fact a religion of war, violence, intolerance, and physical persecution of non - muslims. a01
1469misogynist-incels“ jews heavily pushed feminism. roasties have such high standards because they are ‘ liberated ’. you probably would have gotten your d * * * wet by now if it weren ’ t for feminism and for jews. ”01
926kyle-bristowa quoted in the01
422raymond-cattellasuppose, as may well be the case, that one of these races is naturally courageous, self - sacrificing and enterprising and the other less so. the group will continue to prosper owing to the activities of inventors and explorers of the first race, who, as is generally the rule, will not pass on the usual number of children to the next generation. the nation will be successful in war because the same race has actively responded to the call to arms and to self - sacrifice. throughout these act...01
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1164craig-cobbanov. 16, 2013, videotaped rant against a resident while apatrollinga the streets of leith01
677family-research-council“ the reality is, homosexuals have entered the scouts in the past for predatory purposes. ”01
123base“ most of our members are national socialists and / or fascists, although we also have some run - of - the - mill white nationalists … we have a strong revolutionary and militant current running through the base. most of our members are pretty hardcore in that sense. you ’ re going to be stepping into probably the most extreme group of pro - white people that you can probably come across. ” –01
706scott-lively“ the gay movement is an evil institution [ whose ] goal is to defeat the marriage - based society and replace it with a culture of sexual promiscuity in which thereas no restrictions on sexual conduct except the principle of mutual choice. ”01
602jean-philippe-rushton“ whites have, on average, more neurons and cranial size than blacks … blacks have an advantage in sport because they have narrower hips — but they have narrower hips because they have smaller brains. ”01
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" ], - "execution_count": 5, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Max tokens in any paragraph: 322\n", - "Average tokens per paragraph: 42.7\n", - "Number of paragraphs exceeding 512 tokens: 0\n", - "\n", - "Longest paragraph (322 tokens):\n", - "“Only one conclusion is possible… . [T]he broad picture is clear and inescapable: at some point in the foreseeable future the white British people will become a minority in these islands, and whites w...\n" - ] - } - ], - "id": "81c31a4a" - }, + "text/plain": [ + " filename \\\n", + "51 richard-bertrand-spencer \n", + "168 tim-wildmon \n", + "1469 misogynist-incels \n", + "926 kyle-bristow \n", + "422 raymond-cattell \n", + "... ... \n", + "1164 craig-cobb \n", + "677 family-research-council \n", + "123 base \n", + "706 scott-lively \n", + "602 jean-philippe-rushton \n", + "\n", + " paragraph_segment \\\n", + "51 “ we ’ re going to be back here, and we ’ re going to humiliate all of these people who opposed us. we ’ ll be back here 1, 000 times if necessary. i always win. because i have the will to win, i keep going until i win. ” \n", + "168 a [ islam ] is in fact a religion of war, violence, intolerance, and physical persecution of non - muslims. a \n", + "1469 “ jews heavily pushed feminism. roasties have such high standards because they are ‘ liberated ’. you probably would have gotten your d * * * wet by now if it weren ’ t for feminism and for jews. ” \n", + "926 a quoted in the \n", + "422 asuppose, as may well be the case, that one of these races is naturally courageous, self - sacrificing and enterprising and the other less so. the group will continue to prosper owing to the activities of inventors and explorers of the first race, who, as is generally the rule, will not pass on the usual number of children to the next generation. the nation will be successful in war because the same race has actively responded to the call to arms and to self - sacrifice. throughout these act... \n", + "... ... \n", + "1164 anov. 16, 2013, videotaped rant against a resident while apatrollinga the streets of leith \n", + "677 “ the reality is, homosexuals have entered the scouts in the past for predatory purposes. ” \n", + "123 “ most of our members are national socialists and / or fascists, although we also have some run - of - the - mill white nationalists … we have a strong revolutionary and militant current running through the base. most of our members are pretty hardcore in that sense. you ’ re going to be stepping into probably the most extreme group of pro - white people that you can probably come across. ” – \n", + "706 “ the gay movement is an evil institution [ whose ] goal is to defeat the marriage - based society and replace it with a culture of sexual promiscuity in which thereas no restrictions on sexual conduct except the principle of mutual choice. ” \n", + "602 “ whites have, on average, more neurons and cranial size than blacks … blacks have an advantage in sport because they have narrower hips — but they have narrower hips because they have smaller brains. ” \n", + "\n", + " segment_id label \n", + "51 0 1 \n", + "168 0 1 \n", + "1469 0 1 \n", + "926 0 1 \n", + "422 0 1 \n", + "... ... ... \n", + "1164 0 1 \n", + "677 0 1 \n", + "123 0 1 \n", + "706 0 1 \n", + "602 0 1 \n", + "\n", + "[33 rows x 4 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Apply segmentation to each quote in own_words_df\n", + "segmented_rows = []\n", + "for idx, row in own_words_df.iterrows():\n", + " segments = segment_text(row['paragraph'])\n", + " for i, segment in enumerate(segments):\n", + " segmented_rows.append({\n", + " 'filename': row['filename'],\n", + " 'paragraph_segment': segment,\n", + " 'segment_id': i,\n", + " 'label': 1 # positive example\n", + " })\n", + "\n", + "segmented_df = pd.DataFrame(segmented_rows)\n", + "print(f\"Segmented dataset shape: {segmented_df.shape}\")\n", + "display(segmented_df.sample(33, random_state=42))" + ] + }, + { + "cell_type": "markdown", + "id": "f60af7fa-c933-41f1-86f3-e52034201c06", + "metadata": {}, + "source": [ + "### Download the Neutral example \n", + "Download the neutral dataset from hugging face." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "d5092eff-7e7d-4129-8e01-bbf9b28ee016", + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Apply segmentation to each quote in own_words_df\n", - "segmented_rows = []\n", - "for idx, row in own_words_df.iterrows():\n", - " segments = segment_text(row['paragraph'])\n", - " for i, segment in enumerate(segments):\n", - " segmented_rows.append({\n", - " 'filename': row['filename'],\n", - " 'paragraph_segment': segment,\n", - " 'segment_id': i,\n", - " 'label': 1 # positive example\n", - " })\n", - "\n", - "segmented_df = pd.DataFrame(segmented_rows)\n", - "print(f\"Segmented dataset shape: {segmented_df.shape}\")\n", - "display(segmented_df.sample(33, random_state=42))" - ], - "execution_count": 6, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Segmented dataset shape: (2515, 4)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/html": [ - "
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filenameparagraph_segmentsegment_idlabel
617center-immigration-studieswashington times01
927louis-beama “ new world order, ” 1999 essay by beam01
942bill-whitein august 2004, white was convicted of assaulting a woman when she was handing out flyers that identified him as a neo - nazi landlord. during the court proceedings, white cursed at the woman from the witness stand and was held in contempt of court by the judge. in december 2009, a federal jury found white guilty of threatening several people through intimidating phone calls and internet postings. he was sentenced to 2 a½ years in prison at an april 2010 hearing. in january 2011, white was f...01
973william-piercea pierce, quoted in a biography of him called fame of a dead man ’ s deeds, on the power of narrative01
1967tucker-carlsona reporter later exposed01
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866barry-blacka a 1998 comment to a virginia newspaper01
471david-yerushalmia aoffensive and defensive lawfare : fighting civilization jihad in americaas courts, a center for security policy press release,01
1174kevin-strom“ the aryan race, by dint of its intelligence and creativity and character has managed to drag itself up to a state of civilization and some degree of scientific understanding of the universe about us. but what dr. pierce could clearly see, and what the more jingoistic racialists cannot see, is that that state of civilization is but a few inches above the slime of universal savagery. a¦ the journey has just begun and the danger of falling back is very great. ”01
56david-barton“ and we don ’ t want racism in america. but then as you started watching, that ’ s not what this was about. this was about a hate america movement. ” in reference to cancel culture, the 1619 project, and covid - era anti - racism protests. – the elephant heard podcast, april 27, 202101
1457barnes-reviewfoundation-economic-liberty-incajewish involvement in the communist revolution by john wear, j. d. radical jewish activists, political instigators, and criminals were the main protagonists of the arussiana revolution, which overthrew the tzar, resulting in the brutal murder of the royal family and millions of gentiles. john wear highlights and documents the overwhelming evidence that jews played the leading role in this tragedy, using a variety of sources. a01
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" - ], - "text/plain": [ - " filename \\\n", - "617 center-immigration-studies \n", - "927 louis-beam \n", - "942 bill-white \n", - "973 william-pierce \n", - "1967 tucker-carlson \n", - "... ... \n", - "866 barry-black \n", - "471 david-yerushalmi \n", - "1174 kevin-strom \n", - "56 david-barton \n", - "1457 barnes-reviewfoundation-economic-liberty-inc \n", - "\n", - " paragraph_segment \\\n", - "617 washington times \n", - "927 a “ new world order, ” 1999 essay by beam \n", - "942 in august 2004, white was convicted of assaulting a woman when she was handing out flyers that identified him as a neo - nazi landlord. during the court proceedings, white cursed at the woman from the witness stand and was held in contempt of court by the judge. in december 2009, a federal jury found white guilty of threatening several people through intimidating phone calls and internet postings. he was sentenced to 2 a½ years in prison at an april 2010 hearing. in january 2011, white was f... \n", - "973 a pierce, quoted in a biography of him called fame of a dead man ’ s deeds, on the power of narrative \n", - "1967 a reporter later exposed \n", - "... ... \n", - "866 a a 1998 comment to a virginia newspaper \n", - "471 a aoffensive and defensive lawfare : fighting civilization jihad in americaas courts, a center for security policy press release, \n", - "1174 “ the aryan race, by dint of its intelligence and creativity and character has managed to drag itself up to a state of civilization and some degree of scientific understanding of the universe about us. but what dr. pierce could clearly see, and what the more jingoistic racialists cannot see, is that that state of civilization is but a few inches above the slime of universal savagery. a¦ the journey has just begun and the danger of falling back is very great. ” \n", - "56 “ and we don ’ t want racism in america. but then as you started watching, that ’ s not what this was about. this was about a hate america movement. ” in reference to cancel culture, the 1619 project, and covid - era anti - racism protests. – the elephant heard podcast, april 27, 2021 \n", - "1457 ajewish involvement in the communist revolution by john wear, j. d. radical jewish activists, political instigators, and criminals were the main protagonists of the arussiana revolution, which overthrew the tzar, resulting in the brutal murder of the royal family and millions of gentiles. john wear highlights and documents the overwhelming evidence that jews played the leading role in this tragedy, using a variety of sources. a \n", - "\n", - " segment_id label \n", - "617 0 1 \n", - "927 0 1 \n", - "942 0 1 \n", - "973 0 1 \n", - "1967 0 1 \n", - "... ... ... \n", - "866 0 1 \n", - "471 0 1 \n", - "1174 0 1 \n", - "56 0 1 \n", - "1457 0 1 \n", - "\n", - "[33 rows x 4 columns]" - ] - } - } - ], - "id": "609a3723" + "name": "stdout", + "output_type": "stream", + "text": [ + "Running with: ~/workspace/Sentinel/.venv/bin/python\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Download the Neutral example \n", - "Download the neutral dataset from hugging face." - ], - "id": "f60af7fa-c933-41f1-86f3-e52034201c06" + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading neutral podcast dataset directly from Hugging Face...\n", + "Using cached file at ~/.cache/huggingface/neutral/lex-fridman-podcastUsing cached file.parquet\n", + "Successfully loaded data with 346 rows\n", + "Processed 346 episodes with segments\n", + "Extracting segments: 100%|█████████████████| 316/316 [00:00<00:00, 3308.40it/s]\n", + "Collected 757376 segments before sampling\n", + "Randomly sampled 15000 segments\n", + "Saved 15000 training segments to neutral-segments-training.csv\n", + "Saved 30 evaluation episodes to neutral-episodes-eval.parquet\n" + ] + } + ], + "source": [ + "# ============================================================================\n", + "# ORIGINAL CODE (kept for reference, commented out).\n", + "# It failed with \"poetry: command not found\" because a %%bash cell starts a\n", + "# plain non-login shell that never reads ~/.zshrc, so ~/.local/bin (where\n", + "# poetry is installed) is missing from PATH.\n", + "# ============================================================================\n", + "# %%bash\n", + "# # Change directory into the 'scripts' folder\n", + "# cd scripts\n", + "#\n", + "# # Run your Python script using poetry run\n", + "# # This ensures all your Poetry-managed dependencies are available\n", + "# poetry run python fetch-neutral-examples-data.py\n", + "#\n", + "# # The script will create two files:\n", + "# # 1. `neutral-segments-training.csv`: Contains ~15,000 individual segments (10x our positive examples) for training\n", + "# # 2. `neutral-episodes-eval.parquet`: Contains 30 full episodes for evaluation\n", + "# # You can customize the number of segments and evaluation episodes:\n", + "# # ```bash\n", + "# # python fetch-neutral-examples-data.py -n 20000 -e 50 # 20k segments, 50 eval episodes\n", + "# # ```\n", + "\n", + "# ============================================================================\n", + "# WORKING VERSION\n", + "# ============================================================================\n", + "import sys\n", + "\n", + "# This notebook's kernel already runs inside the Poetry virtualenv\n", + "# (Sentinel/.venv), so sys.executable has every dependency the script needs and\n", + "# the `poetry run` wrapper is unnecessary.\n", + "print(\"Running with:\", sys.executable)\n", + "\n", + "# The script writes two files into examples/data/ :\n", + "# 1. neutral-segments-training.csv -> ~15,000 individual segments for training\n", + "# 2. neutral-episodes-eval.parquet -> 30 full episodes for evaluation\n", + "# To change those amounts, append flags, e.g. `... -n 20000 -e 50`\n", + "!cd scripts && {sys.executable} fetch-neutral-examples-data.py" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "922bed03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of positive examples: 1516\n", + "Number of negative examples to extract: 15160\n", + "Loading neutral podcast dataset directly from Hugging Face...\n", + "Using cached file at ~/.cache/huggingface/lex-fridman-podcast/neutral-episodes-eval.parquet\n", + "Successfully loaded data with 346 rows\n", + "Processed 346 episodes with segments\n", + "Dataset loaded with 346 episodes\n", + "Extracting random segments...\n" + ] }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "%%bash\n", - "# Change directory into the 'scripts' folder\n", - "cd scripts\n", - "\n", - "# Run your Python script using poetry run\n", - "# This ensures all your Poetry-managed dependencies are available\n", - "poetry run python fetch-neutral-examples-data.py\n", - "\n", - "# The script will create two files:\n", - "# 1. `neutral-segments-training.csv`: Contains ~15,000 individual segments (10x our positive examples) for training\n", - "# 2. `neutral-episodes-eval.parquet`: Contains 30 full episodes for evaluation\n", - "# You can customize the number of segments and evaluation episodes:\n", - "# ```bash\n", - "# python fetch-neutral-examples-data.py -n 20000 -e 50 # 20k segments, 50 eval episodes\n", - "# ```" - ], - "execution_count": 7, - "outputs": [ - { - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] - }, - { - "output_type": "stream", - "text": [ - "Loading neutral podcast dataset directly from Hugging Face...\n", - "Using cached file at /Users/evonck/.cache/huggingface/neutral/lex-fridman-podcastUsing cached file.parquet\n", - "Successfully loaded data with 346 rows\n", - "Processed 346 episodes with segments\n" - ] - }, - { - "output_type": "stream", - "text": [ - "Extracting segments: 100%|██████████| 316/316 [00:00<00:00, 3474.10it/s]\n" - ] - }, - { - "output_type": "stream", - "text": [ - "Collected 750250 segments before sampling\n", - "Randomly sampled 15000 segments\n", - "Saved 15000 training segments to neutral-segments-training.csv\n", - "Saved 30 evaluation episodes to neutral-episodes-eval.parquet\n" - ] - } - ], - "id": "d5092eff-7e7d-4129-8e01-bbf9b28ee016" + "name": "stdout", + "output_type": "stream", + "text": [ + "Collected 832839 segments before sampling\n", + "Extracted 15160 negative examples\n", + "\n", + "Sample of negative examples:\n" + ] }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Calculate how many negative examples we need (10x the number of positives)\n", - "\n", - "num_positives = len(segmented_df)\n", - "num_negatives = num_positives * 10\n", - "print(f\"Number of positive examples: {num_positives}\")\n", - "print(f\"Number of negative examples to extract: {num_negatives}\")\n", - "\n", - "# Function to download and read parquet file\n", - "def load_neutral_examplest_dataset():\n", - " try:\n", - " print(\"Loading neutral podcast dataset directly from Hugging Face...\")\n", - " # Direct download of the parquet file\n", - " url = \"https://huggingface.co/datasets/Whispering-GPT/lex-fridman-podcast/resolve/main/data/train-00000-of-00001-25f40520d4548308.parquet\"\n", - " \n", - " # Create cache directory\n", - " cache_dir = os.path.join(os.path.expanduser(\"~\"), \".cache\", \"huggingface\", \"lex-fridman-podcast\")\n", - " os.makedirs(cache_dir, exist_ok=True)\n", - " \n", - " # Download the file if it doesn't exist\n", - " local_path = os.path.join(cache_dir, \"neutral-episodes-eval.parquet\")\n", - " \n", - " if not os.path.exists(local_path):\n", - " print(f\"Downloading parquet file to {local_path}...\")\n", - " response = requests.get(url, stream=True)\n", - " response.raise_for_status()\n", - " \n", - " with open(local_path, 'wb') as f:\n", - " for chunk in response.iter_content(chunk_size=8192):\n", - " f.write(chunk)\n", - " print(\"Download complete!\")\n", - " else:\n", - " print(f\"Using cached file at {local_path}\")\n", - " \n", - " # Read the parquet file with pandas\n", - " df = pd.read_parquet(local_path)\n", - " print(f\"Successfully loaded data with {len(df)} rows\")\n", - " \n", - " # Convert to proper format for our code\n", - " podcast_data = []\n", - " for _, row in df.iterrows():\n", - " if 'segments' in row:\n", - " episode = {\"segments\": []}\n", - " # Process segments\n", - " for segment in row['segments']:\n", - " if isinstance(segment, dict) and 'text' in segment and segment['text']:\n", - " episode[\"segments\"].append({\"text\": segment['text']})\n", - " podcast_data.append(episode)\n", - " \n", - " print(f\"Processed {len(podcast_data)} episodes with segments\")\n", - " return podcast_data\n", - " \n", - " except Exception as e:\n", - " print(f\"Error loading dataset: {e}\")\n", - " return None\n", - "\n", - "# Load the Neutral podcast dataset\n", - "neutral_dataset = load_neutral_examplest_dataset()\n", - "\n", - "print(f\"Dataset loaded with {len(neutral_dataset)} episodes\")\n", - "\n", - "# Function to extract random segments from the dataset\n", - "def extract_random_segments(dataset, num_segments):\n", - " all_segments = []\n", - " \n", - " # Iterate through episodes\n", - " for episode in tqdm(dataset, desc=\"Extracting segments\"):\n", - " if \"segments\" in episode and episode[\"segments\"]:\n", - " # Extract text from random segments in this episode\n", - " episode_segments = episode[\"segments\"]\n", - " # Take some random segments from this episode\n", - " num_to_take = len(episode_segments)\n", - " if num_to_take > 0:\n", - " random_segments = random.sample(episode_segments, num_to_take)\n", - " for segment in random_segments:\n", - " if \"text\" in segment and segment[\"text\"]:\n", - " all_segments.append(segment[\"text\"])\n", - " \n", - " print(f\"Collected {len(all_segments)} segments before sampling\")\n", - " \n", - " # If we have more segments than needed, randomly sample\n", - " if len(all_segments) > num_segments:\n", - " all_segments = random.sample(all_segments, num_segments)\n", - " elif len(all_segments) < num_segments:\n", - " print(f\"Warning: Could only collect {len(all_segments)} segments, fewer than the requested {num_segments}\")\n", - " \n", - " return all_segments\n", - "\n", - "# Extract random segments\n", - "print(\"Extracting random segments...\")\n", - "negative_samples = extract_random_segments(neutral_dataset, num_negatives)\n", - "\n", - "# Create a DataFrame with the negative examples structured like segmented_df\n", - "# The segmented_df has 'filename', 'paragraph_segment', 'segment_id', 'label'\n", - "negative_df = pd.DataFrame({\n", - " 'filename': ['neutral_podcast'] * len(negative_samples),\n", - " 'paragraph_segment': negative_samples, # Using paragraph_segment to match positive df\n", - " 'segment_id': range(len(negative_samples)), # Adding segment_id\n", - " 'label': [0] * len(negative_samples) # 0 for negative examples\n", - "})\n", - "\n", - "# Display some statistics\n", - "print(f\"Extracted {len(negative_samples)} negative examples\")\n", - "print(\"\\nSample of negative examples:\")\n", - "display(negative_df.head(5))\n", - "\n", - "# Create a combined dataset\n", - "combined_df = pd.concat([segmented_df, negative_df], ignore_index=True)\n", - "print(f\"\\nCombined dataset shape: {combined_df.shape}\")\n", - "print(\"Sample of combined dataset:\")\n", - "display(combined_df.sample(50, random_state=42))" - ], - "execution_count": 8, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Number of positive examples: 2515\n", - "Number of negative examples to extract: 25150\n", - "Loading neutral podcast dataset directly from Hugging Face...\n", - "Using cached file at /Users/evonck/.cache/huggingface/lex-fridman-podcast/neutral-episodes-eval.parquet\n", - "Successfully loaded data with 346 rows\n", - "Processed 346 episodes with segments\n", - "Dataset loaded with 346 episodes\n", - "Extracting random segments...\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4968b5e5b3804c72b40b1ab265589e5c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Extracting segments: 0%| | 0/346 [00:00\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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filenameparagraph_segmentsegment_idlabel
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1neutral_podcastOh, that was like the smaller one, like the firefly.10
2neutral_podcastComputers weren't really great at that.20
3neutral_podcastto sort of pretend that there's something that they're not in order to understand what's30
4neutral_podcastto math dot square root.40
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filenameparagraph_segmentsegment_idlabel
22239neutral_podcastIt wasn't because we were evil rhino haters as a whole.197240
11674neutral_podcastYeah, it also probably says that it's quite useful91590
4097neutral_podcastThat's the problem.15820
5219neutral_podcastbecause anybody can always come back27040
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...............
15548neutral_podcastAnd they'll do just fine in those areas as long as pedestrians don't mess with them too130330
17636neutral_podcastSo, so the original one was CASP or critical assessment of of protein structure.151210
8246neutral_podcastAs William James said, death is the warm at the core of the human condition.57310
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filenameparagraph_segmentsegment_idlabel
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" ], - "id": "922bed03" + "text/plain": [ + " filename \\\n", + "0 neutral_podcast \n", + "1 neutral_podcast \n", + "2 neutral_podcast \n", + "3 neutral_podcast \n", + "4 neutral_podcast \n", + "\n", + " paragraph_segment \\\n", + "0 our limited human minds to understand \n", + "1 like Baxter and Sawyer. \n", + "2 Yeah. \n", + "3 is totally off topic and unacceptable in this subreddit and totally on topic and acceptable \n", + "4 that evolutionary process. \n", + "\n", + " segment_id label \n", + "0 0 0 \n", + "1 1 0 \n", + "2 2 0 \n", + "3 3 0 \n", + "4 4 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Build The Indexes\n", - "Using the 2 datasets we have loaded, we will build the negative and positive index used by sentinel" - ], - "id": "fd8e697c-e7a0-453d-8696-2b6ec956feaf" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Combined dataset shape: (16676, 4)\n", + "Sample of combined dataset:\n" + ] }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Create a Sentinel hate speech detection index using our own data\n", - "import torch\n", - "from sentinel.sentinel_local_index import SentinelLocalIndex\n", - "from sentinel.embeddings.sbert import get_sentence_transformer_and_scaling_fn\n", - "\n", - "# Initialize sentence model and get scaling function\n", - "model_name = \"all-MiniLM-L6-v2\"\n", - "model, scale_fn = get_sentence_transformer_and_scaling_fn(model_name)\n", - "\n", - "# Prepare examples\n", - "print(\"Extracting positive and negative examples...\")\n", - "positive_examples = segmented_df['paragraph_segment'].tolist() # From extremists (hate speech)\n", - "negative_examples = negative_df['paragraph_segment'].tolist() # From neutral podcast (neutral speech)\n", - "\n", - "print(f\"Number of positive examples: {len(positive_examples)}\")\n", - "print(f\"Number of negative examples: {len(negative_examples)}\")\n", - "\n", - "# Encode examples\n", - "print(\"\\nEncoding positive examples...\")\n", - "positive_embeddings = model.encode(positive_examples, normalize_embeddings=True)\n", - "positive_embeddings = torch.tensor(positive_embeddings)\n", - "\n", - "print(\"\\nEncoding negative examples...\")\n", - "negative_embeddings = model.encode(negative_examples, normalize_embeddings=True)\n", - "negative_embeddings = torch.tensor(negative_embeddings)\n", - "\n", - "# Create the index\n", - "print(\"\\nCreating Sentinel index...\")\n", - "index = SentinelLocalIndex(\n", - " sentence_model=model,\n", - " positive_embeddings=positive_embeddings,\n", - " negative_embeddings=negative_embeddings,\n", - " scale_fn=scale_fn,\n", - " positive_corpus=positive_examples,\n", - " negative_corpus=negative_examples,\n", - ")\n", - "\n", - "# Save locally\n", - "print(\"\\nSaving index...\")\n", - "save_path = \"./hate_speech_model\"\n", - "saved_config = index.save(path=save_path, encoder_model_name_or_path=model_name)\n", - "print(f\"Saved index with encoder model: {saved_config.encoder_model_name_or_path}\")" - ], - "execution_count": 9, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Extracting positive and negative examples...\n", - "Number of positive examples: 2515\n", - "Number of negative examples: 25150\n", - "\n", - "Encoding positive examples...\n", - "\n", - "Encoding negative examples...\n", - "\n", - "Creating Sentinel index...\n", - "\n", - "Saving index...\n", - "Saved index with encoder model: all-MiniLM-L6-v2\n" - ] - } + "data": { + "text/html": [ + "
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filenameparagraph_segmentsegment_idlabel
4990neutral_podcastYeah.34740
3781neutral_podcastthe observer wishes to be entertained and has some mechanism of enforcing their desire22650
11523neutral_podcastWhat did you take away from that experience?100070
47hal-turneron june 3, 2009, turner was arrested and charged with inciting injury to persons or property. in a blog post, he had asked connecticut catholics to “ take up arms ” against two lawmakers and an employee of the office of state ethics. turner also threatened to release the home addresses of these three men. a jury acquitted turner of the charges in september 2011.01
13262neutral_podcastIf you were to lay out a perfect, productive day,117460
...............
99roy-moore“ we were torn apart in the civil war — brother against brother, north against south, party against party.. what changed? now we have blacks and whites fighting, reds and yellows fighting, democrats and republicans fighting, men and women fighting. ” — speaking at a rally in florence, alabama, september,01
2857neutral_podcastBut we found that a reset switch recently,13410
3115neutral_podcastRight.15990
4972neutral_podcastbecause that's what you're trying to learn.34560
6335neutral_podcastHow suspicious should I be when I'm traveling in Ukraine or different parts of the world when an attractive female walks up to me and shows any kind of attention?48190
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" ], - "id": "e966e3b9" + "text/plain": [ + " filename \\\n", + "4990 neutral_podcast \n", + "3781 neutral_podcast \n", + "11523 neutral_podcast \n", + "47 hal-turner \n", + "13262 neutral_podcast \n", + "... ... \n", + "99 roy-moore \n", + "2857 neutral_podcast \n", + "3115 neutral_podcast \n", + "4972 neutral_podcast \n", + "6335 neutral_podcast \n", + "\n", + " paragraph_segment \\\n", + "4990 Yeah. \n", + "3781 the observer wishes to be entertained and has some mechanism of enforcing their desire \n", + "11523 What did you take away from that experience? \n", + "47 on june 3, 2009, turner was arrested and charged with inciting injury to persons or property. in a blog post, he had asked connecticut catholics to “ take up arms ” against two lawmakers and an employee of the office of state ethics. turner also threatened to release the home addresses of these three men. a jury acquitted turner of the charges in september 2011. \n", + "13262 If you were to lay out a perfect, productive day, \n", + "... ... \n", + "99 “ we were torn apart in the civil war — brother against brother, north against south, party against party.. what changed? now we have blacks and whites fighting, reds and yellows fighting, democrats and republicans fighting, men and women fighting. ” — speaking at a rally in florence, alabama, september, \n", + "2857 But we found that a reset switch recently, \n", + "3115 Right. \n", + "4972 because that's what you're trying to learn. \n", + "6335 How suspicious should I be when I'm traveling in Ukraine or different parts of the world when an attractive female walks up to me and shows any kind of attention? \n", + "\n", + " segment_id label \n", + "4990 3474 0 \n", + "3781 2265 0 \n", + "11523 10007 0 \n", + "47 0 1 \n", + "13262 11746 0 \n", + "... ... ... \n", + "99 0 1 \n", + "2857 1341 0 \n", + "3115 1599 0 \n", + "4972 3456 0 \n", + "6335 4819 0 \n", + "\n", + "[50 rows x 4 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Calculate how many negative examples we need (10x the number of positives)\n", + "\n", + "num_positives = len(segmented_df)\n", + "num_negatives = num_positives * 10\n", + "print(f\"Number of positive examples: {num_positives}\")\n", + "print(f\"Number of negative examples to extract: {num_negatives}\")\n", + "\n", + "# Function to download and read parquet file\n", + "def load_neutral_examplest_dataset():\n", + " try:\n", + " print(\"Loading neutral podcast dataset directly from Hugging Face...\")\n", + " # Direct download of the parquet file\n", + " url = \"https://huggingface.co/datasets/Whispering-GPT/lex-fridman-podcast/resolve/main/data/train-00000-of-00001-25f40520d4548308.parquet\"\n", + " \n", + " # Create cache directory\n", + " cache_dir = os.path.join(os.path.expanduser(\"~\"), \".cache\", \"huggingface\", \"lex-fridman-podcast\")\n", + " os.makedirs(cache_dir, exist_ok=True)\n", + " \n", + " # Download the file if it doesn't exist\n", + " local_path = os.path.join(cache_dir, \"neutral-episodes-eval.parquet\")\n", + " \n", + " if not os.path.exists(local_path):\n", + " print(f\"Downloading parquet file to {local_path}...\")\n", + " response = requests.get(url, stream=True)\n", + " response.raise_for_status()\n", + " \n", + " with open(local_path, 'wb') as f:\n", + " for chunk in response.iter_content(chunk_size=8192):\n", + " f.write(chunk)\n", + " print(\"Download complete!\")\n", + " else:\n", + " print(f\"Using cached file at {local_path}\")\n", + " \n", + " # Read the parquet file with pandas\n", + " df = pd.read_parquet(local_path)\n", + " print(f\"Successfully loaded data with {len(df)} rows\")\n", + " \n", + " # Convert to proper format for our code\n", + " podcast_data = []\n", + " for _, row in df.iterrows():\n", + " if 'segments' in row:\n", + " episode = {\"segments\": []}\n", + " # Process segments\n", + " for segment in row['segments']:\n", + " if isinstance(segment, dict) and 'text' in segment and segment['text']:\n", + " episode[\"segments\"].append({\"text\": segment['text']})\n", + " podcast_data.append(episode)\n", + " \n", + " print(f\"Processed {len(podcast_data)} episodes with segments\")\n", + " return podcast_data\n", + " \n", + " except Exception as e:\n", + " print(f\"Error loading dataset: {e}\")\n", + " return None\n", + "\n", + "# Load the Neutral podcast dataset\n", + "neutral_dataset = load_neutral_examplest_dataset()\n", + "\n", + "print(f\"Dataset loaded with {len(neutral_dataset)} episodes\")\n", + "\n", + "# Function to extract random segments from the dataset\n", + "def extract_random_segments(dataset, num_segments):\n", + " all_segments = []\n", + " \n", + " # Iterate through episodes\n", + " for episode in tqdm(dataset, desc=\"Extracting segments\"):\n", + " if \"segments\" in episode and episode[\"segments\"]:\n", + " # Extract text from random segments in this episode\n", + " episode_segments = episode[\"segments\"]\n", + " # Take some random segments from this episode\n", + " num_to_take = len(episode_segments)\n", + " if num_to_take > 0:\n", + " random_segments = random.sample(episode_segments, num_to_take)\n", + " for segment in random_segments:\n", + " if \"text\" in segment and segment[\"text\"]:\n", + " all_segments.append(segment[\"text\"])\n", + " \n", + " print(f\"Collected {len(all_segments)} segments before sampling\")\n", + " \n", + " # If we have more segments than needed, randomly sample\n", + " if len(all_segments) > num_segments:\n", + " all_segments = random.sample(all_segments, num_segments)\n", + " elif len(all_segments) < num_segments:\n", + " print(f\"Warning: Could only collect {len(all_segments)} segments, fewer than the requested {num_segments}\")\n", + " \n", + " return all_segments\n", + "\n", + "# Extract random segments\n", + "print(\"Extracting random segments...\")\n", + "negative_samples = extract_random_segments(neutral_dataset, num_negatives)\n", + "\n", + "# Create a DataFrame with the negative examples structured like segmented_df\n", + "# The segmented_df has 'filename', 'paragraph_segment', 'segment_id', 'label'\n", + "negative_df = pd.DataFrame({\n", + " 'filename': ['neutral_podcast'] * len(negative_samples),\n", + " 'paragraph_segment': negative_samples, # Using paragraph_segment to match positive df\n", + " 'segment_id': range(len(negative_samples)), # Adding segment_id\n", + " 'label': [0] * len(negative_samples) # 0 for negative examples\n", + "})\n", + "\n", + "# Display some statistics\n", + "print(f\"Extracted {len(negative_samples)} negative examples\")\n", + "print(\"\\nSample of negative examples:\")\n", + "display(negative_df.head(5))\n", + "\n", + "# Create a combined dataset\n", + "combined_df = pd.concat([segmented_df, negative_df], ignore_index=True)\n", + "print(f\"\\nCombined dataset shape: {combined_df.shape}\")\n", + "print(\"Sample of combined dataset:\")\n", + "display(combined_df.sample(50, random_state=42))" + ] + }, + { + "cell_type": "markdown", + "id": "fd8e697c-e7a0-453d-8696-2b6ec956feaf", + "metadata": {}, + "source": [ + "## Build The Indexes\n", + "Using the 2 datasets we have loaded, we will build the negative and positive index used by sentinel" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e966e3b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting positive and negative examples...\n", + "Number of positive examples: 1516\n", + "Number of negative examples: 15160\n", + "\n", + "Encoding positive examples...\n", + "\n", + "Encoding negative examples...\n", + "\n", + "Creating Sentinel index...\n", + "\n", + "Saving index...\n", + "Saved index with encoder model: all-MiniLM-L6-v2\n" + ] + } + ], + "source": [ + "# Create a Sentinel hate speech detection index using our own data\n", + "import torch\n", + "from sentinel.sentinel_local_index import SentinelLocalIndex\n", + "from sentinel.embeddings.sbert import get_sentence_transformer_and_scaling_fn\n", + "\n", + "# Initialize sentence model and get scaling function\n", + "model_name = \"all-MiniLM-L6-v2\"\n", + "model, scale_fn = get_sentence_transformer_and_scaling_fn(model_name)\n", + "\n", + "# Prepare examples\n", + "print(\"Extracting positive and negative examples...\")\n", + "positive_examples = segmented_df['paragraph_segment'].tolist() # From extremists (hate speech)\n", + "negative_examples = negative_df['paragraph_segment'].tolist() # From neutral podcast (neutral speech)\n", + "\n", + "print(f\"Number of positive examples: {len(positive_examples)}\")\n", + "print(f\"Number of negative examples: {len(negative_examples)}\")\n", + "\n", + "# Encode examples\n", + "print(\"\\nEncoding positive examples...\")\n", + "positive_embeddings = model.encode(positive_examples, normalize_embeddings=True)\n", + "positive_embeddings = torch.tensor(positive_embeddings)\n", + "\n", + "print(\"\\nEncoding negative examples...\")\n", + "negative_embeddings = model.encode(negative_examples, normalize_embeddings=True)\n", + "negative_embeddings = torch.tensor(negative_embeddings)\n", + "\n", + "# Create the index\n", + "print(\"\\nCreating Sentinel index...\")\n", + "index = SentinelLocalIndex(\n", + " sentence_model=model,\n", + " positive_embeddings=positive_embeddings,\n", + " negative_embeddings=negative_embeddings,\n", + " scale_fn=scale_fn,\n", + " positive_corpus=positive_examples,\n", + " negative_corpus=negative_examples,\n", + ")\n", + "\n", + "# Save locally\n", + "print(\"\\nSaving index...\")\n", + "save_path = \"./hate_speech_model\"\n", + "saved_config = index.save(path=save_path, encoder_model_name_or_path=model_name)\n", + "print(f\"Saved index with encoder model: {saved_config.encoder_model_name_or_path}\")" + ] + }, + { + "cell_type": "markdown", + "id": "bf1a072e-ef45-4438-bd99-fcfa7d24a8e1", + "metadata": {}, + "source": [ + "# Testing the New Sentinel Index\n", + "Let's use our new sentinel index to detect violation in podcast" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bd2f023f-b51f-4f66-b0ab-296439378c03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The directory 'data/podcast_examples' already exists. Skipping git clone.\n" + ] + } + ], + "source": [ + "%%bash\n", + "TARGET_DIR=\"data/podcast_examples\"\n", + "REPO_URL=\"https://github.com/Fudge/infowars.git\"\n", + "\n", + "if [ -d \"$TARGET_DIR\" ]; then\n", + " echo \"The directory '$TARGET_DIR' already exists. Skipping git clone.\"\n", + "else\n", + " echo \"Cloning '$REPO_URL' into '$TARGET_DIR'...\"\n", + " git clone \"$REPO_URL\" \"$TARGET_DIR\"\n", + " if [ $? -eq 0 ]; then\n", + " echo \"Cloning successful.\"\n", + " else\n", + " echo \"Error: Git clone failed.\"\n", + " fi\n", + "fi" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a374928d-5c31-4f7e-951f-58f171ad90a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 10396 transcript files.\n" + ] + } + ], + "source": [ + "# List transcript files and load a sample for evaluation\n", + "data_dir = \"data/podcast_examples\" # Define data_dir for this cell\n", + "\n", + "transcript_files = glob.glob(os.path.join(data_dir, \"transcripts\", \"**\", \"*.txt\"), recursive=True)\n", + "print(f\"Found {len(transcript_files)} transcript files.\")\n", + "\n", + "# Check if transcript files were found before trying to open one\n", + "if transcript_files:\n", + " # Load the first transcript as an example\n", + " with open(transcript_files[0], \"r\", encoding=\"utf-8\", errors=\"ignore\") as f:\n", + " transcript_text = f.read()\n", + "else:\n", + " print(\"No transcript files found. Please ensure the podcast_examples directory exists and contains transcript files.\")\n", + " # Set a placeholder to avoid errors in subsequent cells\n", + " transcript_text = \"No transcript available.\"" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "455888f7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Loading saved Sentinel index...\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Testing the New Sentinel Index\n", - "Let's use our new sentinel index to detect violation in podcast" - ], - "id": "bf1a072e-ef45-4438-bd99-fcfa7d24a8e1" + "name": "stdout", + "output_type": "stream", + "text": [ + "Index loaded successfully!\n" + ] }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "%%bash\n", - "TARGET_DIR=\"data/podcast_examples\"\n", - "REPO_URL=\"https://github.com/Fudge/infowars.git\"\n", - "\n", - "if [ -d \"$TARGET_DIR\" ]; then\n", - " echo \"The directory '$TARGET_DIR' already exists. Skipping git clone.\"\n", - "else\n", - " echo \"Cloning '$REPO_URL' into '$TARGET_DIR'...\"\n", - " git clone \"$REPO_URL\" \"$TARGET_DIR\"\n", - " if [ $? -eq 0 ]; then\n", - " echo \"Cloning successful.\"\n", - " else\n", - " echo \"Error: Git clone failed.\"\n", - " fi\n", - "fi" - ], - "execution_count": 10, - "outputs": [ - { - "output_type": "stream", - "text": [ - "The directory 'data/podcast_examples' already exists. Skipping git clone.\n" - ] - }, - { - "output_type": "stream", - "text": [ - "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", - "To disable this warning, you can either:\n", - "\t- Avoid using `tokenizers` before the fork if possible\n", - "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" - ] - } - ], - "id": "bd2f023f-b51f-4f66-b0ab-296439378c03" + "name": "stdout", + "output_type": "stream", + "text": [ + "Processed 100 transcripts\n" + ] }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "# List transcript files and load a sample for evaluation\n", - "data_dir = \"data/podcast_examples\" # Define data_dir for this cell\n", - "\n", - "transcript_files = glob.glob(os.path.join(data_dir, \"transcripts\", \"**\", \"*.txt\"), recursive=True)\n", - "print(f\"Found {len(transcript_files)} transcript files.\")\n", - "\n", - "# Check if transcript files were found before trying to open one\n", - "if transcript_files:\n", - " # Load the first transcript as an example\n", - " with open(transcript_files[0], \"r\", encoding=\"utf-8\", errors=\"ignore\") as f:\n", - " transcript_text = f.read()\n", - "else:\n", - " print(\"No transcript files found. Please ensure the podcast_examples directory exists and contains transcript files.\")\n", - " # Set a placeholder to avoid errors in subsequent cells\n", - " transcript_text = \"No transcript available.\"" - ], - "execution_count": 11, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Found 9682 transcript files.\n" - ] - } - ], - "id": "a374928d-5c31-4f7e-951f-58f171ad90a7" + "name": "stdout", + "output_type": "stream", + "text": [ + "Token indices sequence length is longer than the specified maximum sequence length for this model (26877 > 512). Running this sequence through the model will result in indexing errors\n" + ] }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "from sentinel.sentinel_local_index import SentinelLocalIndex\n", - "print(\"Loading saved Sentinel index...\")\n", - "index = SentinelLocalIndex.load(path=\"./hate_speech_model\")\n", - "print(\"Index loaded successfully!\")\n", - "\n", - "# Function to clean transcript text by removing time markers\n", - "def clean_transcript(text):\n", - " # Remove [HH:MM:SS --> HH:MM:SS] time markers\n", - " cleaned = re.sub(r'\\[\\d{2}:\\d{2}:\\d{2}\\s*-->\\s*\\d{2}:\\d{2}:\\d{2}\\]\\s*', '', text)\n", - " # Remove any remaining timestamps in similar formats\n", - " cleaned = re.sub(r'\\d{2}:\\d{2}:\\d{2}\\s*->\\s*\\d{2}:\\d{2}:\\d{2}\\s*', '', cleaned)\n", - " return cleaned.strip()\n", - "\n", - "# Load and clean transcripts\n", - "cleaned_transcripts = []\n", - "for file_path in tqdm(transcript_files[:100], desc=\"Processing transcripts\"): # Sample 100 episodes\n", - " try:\n", - " with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:\n", - " text = f.read()\n", - " cleaned_text = clean_transcript(text)\n", - " cleaned_transcripts.append({\n", - " 'file': file_path,\n", - " 'text': cleaned_text\n", - " })\n", - " except Exception as e:\n", - " print(f\"Error processing {file_path}: {e}\")\n", - "\n", - "print(f\"Processed {len(cleaned_transcripts)} transcripts\")\n", - "\n", - "# Segment the transcripts\n", - "all_segments = []\n", - "for transcript in tqdm(cleaned_transcripts, desc=\"Segmenting transcripts\"):\n", - " segments = segment_text(transcript['text'])\n", - " for segment in segments:\n", - " all_segments.append({\n", - " 'file': transcript['file'],\n", - " 'segment': segment\n", - " })\n", - "\n", - "print(f\"Created {len(all_segments)} segments\")\n", - "\n", - "# Create a DataFrame from the segments\n", - "results_df = pd.DataFrame(all_segments)\n", - "\n", - "# Group by file and calculate affinity scores for each episode\n", - "episode_results = {}\n", - "segment_scores_dict = {}\n", - "\n", - "# Process each file's segments\n", - "for file_path, group in tqdm(results_df.groupby('file'), desc=\"Calculating rare class affinity\"):\n", - " segments = group['segment'].tolist()\n", - " # Calculate hate speech affinity scores\n", - " result = index.calculate_rare_class_affinity(segments)\n", - " \n", - " # Store episode-level score\n", - " episode_results[file_path] = result.rare_class_affinity_score\n", - " \n", - " # Store individual segment scores\n", - " for segment, score in result.observation_scores.items():\n", - " # Create a tuple key to store both file and segment\n", - " segment_scores_dict[(file_path, segment)] = score\n", - "\n", - "# Create DataFrame for episode-level scores\n", - "episode_scores_df = pd.DataFrame({\n", - " 'file': list(episode_results.keys()),\n", - " 'hate_affinity_score': list(episode_results.values())\n", - "}).sort_values('hate_affinity_score', ascending=False)\n", - "\n", - "# Create DataFrame for segment-level scores\n", - "segments_with_scores = []\n", - "for (file_path, segment), score in segment_scores_dict.items():\n", - " segments_with_scores.append({\n", - " 'file': file_path,\n", - " 'segment': segment,\n", - " 'score': score\n", - " })\n", - "segment_scores_df = pd.DataFrame(segments_with_scores)\n" - ], - "execution_count": 12, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Loading saved Sentinel index...\n", - "Index loaded successfully!\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "0bc758d716b0447a9afba4dd7cd4610c", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing transcripts: 0%| | 0/100 [00:00 512). Running this sequence through the model will result in indexing errors\n" - ] - }, - { - "output_type": "stream", - "text": [ - "Created 2731 segments\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "82f14a11b74146348239229229bd63dd", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Calculating rare class affinity: 0%| | 0/100 [00:00 HH:MM:SS] time markers\n", + " cleaned = re.sub(r'\\[\\d{2}:\\d{2}:\\d{2}\\s*-->\\s*\\d{2}:\\d{2}:\\d{2}\\]\\s*', '', text)\n", + " # Remove any remaining timestamps in similar formats\n", + " cleaned = re.sub(r'\\d{2}:\\d{2}:\\d{2}\\s*->\\s*\\d{2}:\\d{2}:\\d{2}\\s*', '', cleaned)\n", + " return cleaned.strip()\n", + "\n", + "# Load and clean transcripts\n", + "cleaned_transcripts = []\n", + "for file_path in tqdm(transcript_files[:100], desc=\"Processing transcripts\"): # Sample 100 episodes\n", + " try:\n", + " with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:\n", + " text = f.read()\n", + " cleaned_text = clean_transcript(text)\n", + " cleaned_transcripts.append({\n", + " 'file': file_path,\n", + " 'text': cleaned_text\n", + " })\n", + " except Exception as e:\n", + " print(f\"Error processing {file_path}: {e}\")\n", + "\n", + "print(f\"Processed {len(cleaned_transcripts)} transcripts\")\n", + "\n", + "# Segment the transcripts\n", + "all_segments = []\n", + "for transcript in tqdm(cleaned_transcripts, desc=\"Segmenting transcripts\"):\n", + " segments = segment_text(transcript['text'])\n", + " for segment in segments:\n", + " all_segments.append({\n", + " 'file': transcript['file'],\n", + " 'segment': segment\n", + " })\n", + "\n", + "print(f\"Created {len(all_segments)} segments\")\n", + "\n", + "# Create a DataFrame from the segments\n", + "results_df = pd.DataFrame(all_segments)\n", + "\n", + "# Group by file and calculate affinity scores for each episode\n", + "episode_results = {}\n", + "segment_scores_dict = {}\n", + "\n", + "# Process each file's segments\n", + "for file_path, group in tqdm(results_df.groupby('file'), desc=\"Calculating rare class affinity\"):\n", + " segments = group['segment'].tolist()\n", + " # Calculate hate speech affinity scores\n", + " result = index.calculate_rare_class_affinity(segments)\n", + " \n", + " # Store episode-level score\n", + " episode_results[file_path] = result.rare_class_affinity_score\n", + " \n", + " # Store individual segment scores\n", + " for segment, score in result.observation_scores.items():\n", + " # Create a tuple key to store both file and segment\n", + " segment_scores_dict[(file_path, segment)] = score\n", + "\n", + "# Create DataFrame for episode-level scores\n", + "episode_scores_df = pd.DataFrame({\n", + " 'file': list(episode_results.keys()),\n", + " 'hate_affinity_score': list(episode_results.values())\n", + "}).sort_values('hate_affinity_score', ascending=False)\n", + "\n", + "# Create DataFrame for segment-level scores\n", + "segments_with_scores = []\n", + "for (file_path, segment), score in segment_scores_dict.items():\n", + " segments_with_scores.append({\n", + " 'file': file_path,\n", + " 'segment': segment,\n", + " 'score': score\n", + " })\n", + "segment_scores_df = pd.DataFrame(segments_with_scores)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8e1c158c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Using the same Sentinel index for Lex Fridman podcasts...\n", + "Selected 30 random Lex Fridman podcast episodes\n" + ] }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Now let's analyze Lex Fridman podcast episodes to compare with the other podcast\n", - "import logging\n", - "import numpy as np\n", - "\n", - "# We'll reuse the same index we loaded earlier\n", - "print(\"Using the same Sentinel index for Lex Fridman podcasts...\")\n", - "\n", - "# Select 30 random episodes from the Lex Fridman dataset\n", - "num_neutral_episodes = min(30, len(neutral_dataset))\n", - "random_neutral_episodes = random.sample(neutral_dataset, num_neutral_episodes)\n", - "print(f\"Selected {num_neutral_episodes} random Lex Fridman podcast episodes\")\n", - "\n", - "# Process the episodes similar to how we processed the transcripts\n", - "lex_cleaned_transcripts = []\n", - "for i, episode in enumerate(tqdm(random_neutral_episodes, desc=\"Processing Neutral episodes\")):\n", - " try:\n", - " if \"segments\" in episode and episode[\"segments\"]:\n", - " # Join all segments to create a full transcript\n", - " segments_text = \" \".join([seg[\"text\"] for seg in episode[\"segments\"] if \"text\" in seg and seg[\"text\"]])\n", - " \n", - " # Create a transcript entry with a unique identifier\n", - " lex_cleaned_transcripts.append({\n", - " 'file': f\"lex_fridman_episode_{i}\", # Create a unique identifier\n", - " 'text': segments_text\n", - " })\n", - " except Exception as e:\n", - " print(f\"Error processing Lex episode {i}: {e}\")\n", - "\n", - "print(f\"Processed {len(lex_cleaned_transcripts)} Lex Fridman transcripts\")\n", - "\n", - "# Segment the transcripts\n", - "lex_all_segments = []\n", - "for transcript in tqdm(lex_cleaned_transcripts, desc=\"Segmenting Neutral transcripts\"):\n", - " segments = segment_text(transcript['text'])\n", - " for segment in segments:\n", - " lex_all_segments.append({\n", - " 'file': transcript['file'],\n", - " 'segment': segment\n", - " })\n", - "\n", - "print(f\"Created {len(lex_all_segments)} segments from Lex Fridman podcasts\")\n", - "\n", - "# Create a DataFrame from the segments\n", - "lex_results_df = pd.DataFrame(lex_all_segments)\n", - "\n", - "# Group by file and calculate affinity scores for each episode\n", - "lex_episode_results = {}\n", - "lex_segment_scores_dict = {}\n", - "\n", - "# Process each file's segments\n", - "for file_path, group in tqdm(lex_results_df.groupby('file'), desc=\"Calculating rare class affinity for Neutral episodes\"):\n", - " segments = group['segment'].tolist()\n", - " # Calculate hate speech affinity scores\n", - " result = index.calculate_rare_class_affinity(segments)\n", - " \n", - " # Store episode-level score\n", - " lex_episode_results[file_path] = result.rare_class_affinity_score\n", - " \n", - " # Store individual segment scores\n", - " for segment, score in result.observation_scores.items():\n", - " # Create a tuple key to store both file and segment\n", - " lex_segment_scores_dict[(file_path, segment)] = score\n", - "\n", - "# Create DataFrame for episode-level scores\n", - "lex_episode_scores_df = pd.DataFrame({\n", - " 'file': list(lex_episode_results.keys()),\n", - " 'hate_affinity_score': list(lex_episode_results.values())\n", - "}).sort_values('hate_affinity_score', ascending=False)\n", - "\n", - "\n", - "# Create DataFrame for segment-level scores\n", - "lex_segments_with_scores = []\n", - "for (file_path, segment), score in lex_segment_scores_dict.items():\n", - " lex_segments_with_scores.append({\n", - " 'file': file_path,\n", - " 'segment': segment,\n", - " 'score': score\n", - " })\n", - "lex_segment_scores_df = pd.DataFrame(lex_segments_with_scores)" - ], - "execution_count": 13, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Using the same Sentinel index for Lex Fridman podcasts...\n", - "Selected 30 random Lex Fridman podcast episodes\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "827df5b146bf496bb89d8188ed756c04", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Processing Neutral episodes: 0%| | 0/30 [00:00" - ] - } - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - } - }, - { - "output_type": "stream", - "text": [ - "\n", - "Statistics for hate affinity scores:\n", - "\n", - "Controversial:\n", - " Mean: 0.0796\n", - " Median: 0.0000\n", - " Std Dev: 0.1947\n", - " Min: -0.5694\n", - " Max: 0.7799\n", - "\n", - "Lex Fridman:\n", - " Mean: 0.0639\n", - " Median: 0.0594\n", - " Std Dev: 0.0754\n", - " Min: 0.0000\n", - " Max: 0.3032\n" - ] - } - ], - "id": "29443f0c" + "name": "stdout", + "output_type": "stream", + "text": [ + "Created 7226 segments from Lex Fridman podcasts\n" + ] + } + ], + "source": [ + "# Now let's analyze Lex Fridman podcast episodes to compare with the other podcast\n", + "import logging\n", + "import numpy as np\n", + "\n", + "# We'll reuse the same index we loaded earlier\n", + "print(\"Using the same Sentinel index for Lex Fridman podcasts...\")\n", + "\n", + "# Select 30 random episodes from the Lex Fridman dataset\n", + "num_neutral_episodes = min(30, len(neutral_dataset))\n", + "random_neutral_episodes = random.sample(neutral_dataset, num_neutral_episodes)\n", + "print(f\"Selected {num_neutral_episodes} random Lex Fridman podcast episodes\")\n", + "\n", + "# Process the episodes similar to how we processed the transcripts\n", + "lex_cleaned_transcripts = []\n", + "for i, episode in enumerate(tqdm(random_neutral_episodes, desc=\"Processing Neutral episodes\")):\n", + " try:\n", + " if \"segments\" in episode and episode[\"segments\"]:\n", + " # Join all segments to create a full transcript\n", + " segments_text = \" \".join([seg[\"text\"] for seg in episode[\"segments\"] if \"text\" in seg and seg[\"text\"]])\n", + " \n", + " # Create a transcript entry with a unique identifier\n", + " lex_cleaned_transcripts.append({\n", + " 'file': f\"lex_fridman_episode_{i}\", # Create a unique identifier\n", + " 'text': segments_text\n", + " })\n", + " except Exception as e:\n", + " print(f\"Error processing Lex episode {i}: {e}\")\n", + "\n", + "print(f\"Processed {len(lex_cleaned_transcripts)} Lex Fridman transcripts\")\n", + "\n", + "# Segment the transcripts\n", + "lex_all_segments = []\n", + "for transcript in tqdm(lex_cleaned_transcripts, desc=\"Segmenting Neutral transcripts\"):\n", + " segments = segment_text(transcript['text'])\n", + " for segment in segments:\n", + " lex_all_segments.append({\n", + " 'file': transcript['file'],\n", + " 'segment': segment\n", + " })\n", + "\n", + "print(f\"Created {len(lex_all_segments)} segments from Lex Fridman podcasts\")\n", + "\n", + "# Create a DataFrame from the segments\n", + "lex_results_df = pd.DataFrame(lex_all_segments)\n", + "\n", + "# Group by file and calculate affinity scores for each episode\n", + "lex_episode_results = {}\n", + "lex_segment_scores_dict = {}\n", + "\n", + "# Process each file's segments\n", + "for file_path, group in tqdm(lex_results_df.groupby('file'), desc=\"Calculating rare class affinity for Neutral episodes\"):\n", + " segments = group['segment'].tolist()\n", + " # Calculate hate speech affinity scores\n", + " result = index.calculate_rare_class_affinity(segments)\n", + " \n", + " # Store episode-level score\n", + " lex_episode_results[file_path] = result.rare_class_affinity_score\n", + " \n", + " # Store individual segment scores\n", + " for segment, score in result.observation_scores.items():\n", + " # Create a tuple key to store both file and segment\n", + " lex_segment_scores_dict[(file_path, segment)] = score\n", + "\n", + "# Create DataFrame for episode-level scores\n", + "lex_episode_scores_df = pd.DataFrame({\n", + " 'file': list(lex_episode_results.keys()),\n", + " 'hate_affinity_score': list(lex_episode_results.values())\n", + "}).sort_values('hate_affinity_score', ascending=False)\n", + "\n", + "\n", + "# Create DataFrame for segment-level scores\n", + "lex_segments_with_scores = []\n", + "for (file_path, segment), score in lex_segment_scores_dict.items():\n", + " lex_segments_with_scores.append({\n", + " 'file': file_path,\n", + " 'segment': segment,\n", + " 'score': score\n", + " })\n", + "lex_segment_scores_df = pd.DataFrame(lex_segments_with_scores)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "29443f0c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved episode scores to CSV files:\n", + "- controversial_podcast.csv\n", + "- lex_fridman_episode_scores.csv\n", + "- all_platform_scores.csv\n" + ] }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Save segment-level scores to CSV files for further analysis\n", - "segment_scores_df.to_csv(\"data/controversial_segment_scores.csv\", index=False)\n", - "lex_segment_scores_df.to_csv(\"data/lex_fridman_segment_scores.csv\", index=False)\n", - "\n", - "print(\"Saved segment-level scores to CSV files:\")\n", - "print(\"- controversial_segment_scores.csv\")\n", - "print(\"- lex_fridman_segment_scores.csv\")\n", - "\n", - "# Count high risk segments in both datasets (score > 0.5)\n", - "controversial_high_risk = len(segment_scores_df[segment_scores_df['score'] > 0.5])\n", - "lex_high_risk = len(lex_segment_scores_df[lex_segment_scores_df['score'] > 0.5])\n", - "\n", - "print(f\"\\nHigh risk segments (score > 0.5):\")\n", - "print(f\"- Controversial: {controversial_high_risk} segments ({controversial_high_risk/len(segment_scores_df)*100:.2f}% of all segments)\")\n", - "print(f\"- Lex Fridman: {lex_high_risk} segments ({lex_high_risk/len(lex_segment_scores_df)*100:.2f}% of all segments)\")\n", - "\n", - "# Count medium risk segments (score between 0.1 and 0.5)\n", - "controversial_medium_risk = len(segment_scores_df[(segment_scores_df['score'] > 0.1) & (segment_scores_df['score'] <= 0.5)])\n", - "lex_medium_risk = len(lex_segment_scores_df[(lex_segment_scores_df['score'] > 0.1) & (lex_segment_scores_df['score'] <= 0.5)])\n", - "\n", - "print(f\"\\nMedium risk segments (0.1 < score <= 0.5):\")\n", - "print(f\"- Controversial: {controversial_medium_risk} segments ({controversial_medium_risk/len(segment_scores_df)*100:.2f}% of all segments)\")\n", - "print(f\"- Lex Fridman: {lex_medium_risk} segments ({lex_medium_risk/len(lex_segment_scores_df)*100:.2f}% of all segments)\")" - ], - "execution_count": 15, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Saved segment-level scores to CSV files:\n", - "- controversial_segment_scores.csv\n", - "- lex_fridman_segment_scores.csv\n", - "\n", - "High risk segments (score > 0.5):\n", - "- Controversial: 0 segments (0.00% of all segments)\n", - "- Lex Fridman: 0 segments (0.00% of all segments)\n", - "\n", - "Medium risk segments (0.1 < score <= 0.5):\n", - "- Controversial: 236 segments (8.64% of all segments)\n", - "- Lex Fridman: 67 segments (0.94% of all segments)\n" - ] - } - ], - "id": "55dbe289" + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Create segment-level visualizations and comparisons\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Save the completed segment dataframes to CSV files\n", - "segment_scores_df.to_csv(\"data/controversial_segment_scores.csv\", index=False)\n", - "lex_segment_scores_df.to_csv(\"data/lex_fridman_segment_scores.csv\", index=False)\n", - "\n", - "# Create dataframes with platform labels for combined analysis\n", - "controversial_segments = pd.DataFrame({\n", - " 'platform': ['Controversial'] * len(segment_scores_df),\n", - " 'score': segment_scores_df['score']\n", - "})\n", - "\n", - "lex_segments = pd.DataFrame({\n", - " 'platform': ['Lex Fridman'] * len(lex_segment_scores_df),\n", - " 'score': lex_segment_scores_df['score']\n", - "})\n", - "\n", - "# Combine segment scores\n", - "all_segment_scores = pd.concat([controversial_segments, lex_segments], ignore_index=True)\n", - "all_segment_scores.to_csv(\"data/all_segment_scores.csv\", index=False)\n", - "print(\"Saved all segment-level data to CSV files\")\n", - "\n", - "# Create a boxplot comparison for segment-level scores\n", - "plt.figure(figsize=(10, 6))\n", - "\n", - "# Extract data for each platform\n", - "platforms = all_segment_scores['platform'].unique()\n", - "segment_data = [all_segment_scores[all_segment_scores['platform'] == platform]['score'] for platform in platforms]\n", - "\n", - "# Create box plot\n", - "box = plt.boxplot(segment_data, patch_artist=True, labels=platforms)\n", - "colors = ['#ff9999', '#66b3ff']\n", - "for patch, color in zip(box['boxes'], colors):\n", - " patch.set_facecolor(color)\n", - "\n", - "plt.title('Segment-Level Hate Speech Score Comparison')\n", - "plt.ylabel('Hate Speech Score')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.show()\n", - "\n", - "# Create histograms to compare segment-level score distributions\n", - "plt.figure(figsize=(12, 6))\n", - "\n", - "# Extract data for each platform\n", - "for i, platform in enumerate(platforms):\n", - " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", - " # Plot histogram with log scale for y-axis to better see the distribution tail\n", - " plt.hist(platform_data, bins=50, alpha=0.6, label=platform)\n", - "\n", - "plt.title('Distribution of Segment-Level Hate Speech Scores')\n", - "plt.xlabel('Hate Speech Score')\n", - "plt.ylabel('Frequency')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "# Create a second histogram with log scale for better comparison of tails\n", - "plt.figure(figsize=(12, 6))\n", - "for i, platform in enumerate(platforms):\n", - " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", - " # Plot histogram with log scale for y-axis\n", - " plt.hist(platform_data, bins=50, alpha=0.6, label=platform)\n", - "\n", - "plt.title('Distribution of Segment-Level Hate Speech Scores (Log Scale)')\n", - "plt.xlabel('Hate Speech Score')\n", - "plt.ylabel('Frequency (log scale)')\n", - "plt.yscale('log')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "# Create cumulative distribution function (CDF) plot\n", - "plt.figure(figsize=(12, 6))\n", - "for i, platform in enumerate(platforms):\n", - " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", - " # Sort the data\n", - " sorted_data = np.sort(platform_data)\n", - " # Get the cumulative probabilities\n", - " p = 1. * np.arange(len(sorted_data)) / (len(sorted_data) - 1)\n", - " # Plot the CDF\n", - " plt.plot(sorted_data, p, label=platform, color=colors[i], linewidth=2)\n", - "\n", - "plt.title('Cumulative Distribution of Segment-Level Hate Speech Scores')\n", - "plt.xlabel('Hate Speech Score')\n", - "plt.ylabel('Cumulative Probability')\n", - "plt.grid(True, alpha=0.3)\n", - "plt.legend()\n", - "plt.show()\n", - "\n", - "# Print detailed statistics for segment-level scores\n", - "print(\"\\nSegment-level statistics for hate speech scores:\")\n", - "for platform in all_segment_scores['platform'].unique():\n", - " platform_scores = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", - " print(f\"\\n{platform}:\")\n", - " print(f\" Count: {len(platform_scores)}\")\n", - " print(f\" Mean: {platform_scores.mean():.4f}\")\n", - " print(f\" Median: {platform_scores.median():.4f}\")\n", - " print(f\" Std Dev: {platform_scores.std():.4f}\")\n", - " print(f\" Min: {platform_scores.min():.4f}\")\n", - " print(f\" Max: {platform_scores.max():.4f}\")\n", - " print(f\" 25th Percentile: {platform_scores.quantile(0.25):.4f}\")\n", - " print(f\" 75th Percentile: {platform_scores.quantile(0.75):.4f}\")\n", - " print(f\" 90th Percentile: {platform_scores.quantile(0.9):.4f}\")\n", - " print(f\" 95th Percentile: {platform_scores.quantile(0.95):.4f}\")\n", - " print(f\" 99th Percentile: {platform_scores.quantile(0.99):.4f}\")\n", - " \n", - " # Count high and medium risk segments\n", - " high_risk = len(platform_scores[platform_scores > 0.5])\n", - " high_risk_pct = high_risk / len(platform_scores) * 100\n", - " medium_risk = len(platform_scores[(platform_scores > 0.1) & (platform_scores <= 0.5)])\n", - " medium_risk_pct = medium_risk / len(platform_scores) * 100\n", - " \n", - " print(f\" High risk segments (> 0.5): {high_risk} ({high_risk_pct:.2f}%)\")\n", - " print(f\" Medium risk segments (0.1-0.5): {medium_risk} ({medium_risk_pct:.2f}%)\")\n" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Saved all segment-level data to CSV files\n" - ] - }, - { - "output_type": "stream", - "text": [ - "/var/folders/ph/f73k11ns3_v8n6g3lrdb792w0000gr/T/ipykernel_89229/3057986402.py:33: MatplotlibDeprecationWarning: The 'labels' parameter of boxplot() has been renamed 'tick_labels' since Matplotlib 3.9; support for the old name will be dropped in 3.11.\n", - " box = plt.boxplot(segment_data, patch_artist=True, labels=platforms)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", 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", 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", 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" - ] - } - }, - { - "output_type": "stream", - "text": [ - "\n", - "Segment-level statistics for hate speech scores:\n", - "\n", - "Controversial:\n", - " Count: 2731\n", - " Mean: 0.0113\n", - " Median: 0.0000\n", - " Std Dev: 0.0376\n", - " Min: 0.0000\n", - " Max: 0.2104\n", - " 25th Percentile: 0.0000\n", - " 75th Percentile: 0.0000\n", - " 90th Percentile: 0.0000\n", - " 95th Percentile: 0.1193\n", - " 99th Percentile: 0.1689\n", - " High risk segments (> 0.5): 0 (0.00%)\n", - " Medium risk segments (0.1-0.5): 236 (8.64%)\n", - "\n", - "Lex Fridman:\n", - " Count: 7153\n", - " Mean: 0.0012\n", - " Median: 0.0000\n", - " Std Dev: 0.0126\n", - " Min: 0.0000\n", - " Max: 0.2409\n", - " 25th Percentile: 0.0000\n", - " 75th Percentile: 0.0000\n", - " 90th Percentile: 0.0000\n", - " 95th Percentile: 0.0000\n", - " 99th Percentile: 0.0000\n", - " High risk segments (> 0.5): 0 (0.00%)\n", - " Medium risk segments (0.1-0.5): 67 (0.94%)\n" - ] - } - ], - "id": "74e25bf5" + "data": { + 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Tuning aggregation strategies with a simulation\n", - "\n", - "So far every episode was scored with the default aggregator (`skewness`). But Sentinel ships several \"summarize metrics\", and which one works best depends on your data. This section runs a small **simulation** (using `sentinel.simulation`) to compare them systematically on our labeled episodes.\n", - "\n", - "Two very different \"metrics\" are involved, and it helps to keep them separate:\n", - "\n", - "- **Summarize metric (the aggregation):** how an episode's many per-segment scores become a single number. Sentinel offers `skewness`, `mean_of_positives`, `top_k_mean`, `percentile_score`, `softmax_weighted_mean`, and `max_score`. This is *not* a ranking.\n", - "- **Evaluation metric:** given one number per episode plus a known label (controversial = 1, Lex Fridman = 0), how well are the two classes separated? Ranking is only one option. The harness reports **three families** so you can tune for whatever matters to you:\n", - " - **Ranking** (`roc_auc`, `recall_at_n`, `rank_ratio`): where do the known-positive episodes land in the leaderboard?\n", - " - **Threshold / classification** (`precision`, `recall`, `f1`): pick a cutoff on the per-episode score and count hits and misses.\n", - " - **Separation / distribution** (`mean_separation`, `cohens_d`, `ks_statistic`): threshold-free distance between the two score distributions.\n", - "\n", - "The expensive part (running the sentence model) is done once by `score_groups`; comparing aggregators and thresholds afterward is cheap." - ], - "id": "fb98a1fd" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Statistics for hate affinity scores:\n", + "\n", + "Controversial:\n", + " Mean: 0.1499\n", + " Median: 0.1429\n", + " Std Dev: 0.0668\n", + " Min: 0.0000\n", + " Max: 0.3100\n", + "\n", + "Lex Fridman:\n", + " Mean: 0.0124\n", + " Median: 0.0000\n", + " Std Dev: 0.0343\n", + " Min: 0.0000\n", + " Max: 0.1436\n" + ] + } + ], + "source": [ + "# Create a comparison visualization of the hate affinity scores between the 2 pdocasts\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Save the dataframes to CSV files\n", + "episode_scores_df.to_csv(\"data/controversial_podcast.csv\", index=False)\n", + "lex_episode_scores_df.to_csv(\"data/lex_fridman_episode_scores.csv\", index=False)\n", + "print(\"Saved episode scores to CSV files:\")\n", + "print(\"- controversial_podcast.csv\")\n", + "print(\"- lex_fridman_episode_scores.csv\")\n", + "\n", + "# Create a combined dataset for comparison\n", + "controversial_scores = pd.DataFrame({\n", + " 'platform': ['Controversial'] * len(episode_scores_df),\n", + " 'hate_affinity_score': episode_scores_df['hate_affinity_score']\n", + "})\n", + "\n", + "lex_scores = pd.DataFrame({\n", + " 'platform': ['Lex Fridman'] * len(lex_episode_scores_df),\n", + " 'hate_affinity_score': lex_episode_scores_df['hate_affinity_score']\n", + "})\n", + "\n", + "all_scores = pd.concat([controversial_scores, lex_scores], ignore_index=True)\n", + "all_scores.to_csv(\"data/all_platform_scores.csv\", index=False)\n", + "print(\"- all_platform_scores.csv\")\n", + "\n", + "# Create a boxplot comparison using matplotlib\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Extract data for each platform\n", + "platforms = all_scores['platform'].unique()\n", + "data = [all_scores[all_scores['platform'] == platform]['hate_affinity_score'] for platform in platforms]\n", + "\n", + "# Create box plot\n", + "box = plt.boxplot(data, patch_artist=True, labels=platforms)\n", + "colors = ['#ff9999', '#66b3ff']\n", + "for patch, color in zip(box['boxes'], colors):\n", + " patch.set_facecolor(color)\n", + "\n", + "plt.title('Hate Speech Affinity Score Comparison')\n", + "plt.ylabel('Hate Affinity Score')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.show()\n", + "\n", + "# Create histograms to compare distributions\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Extract data for each platform\n", + "for i, platform in enumerate(platforms):\n", + " platform_data = all_scores[all_scores['platform'] == platform]['hate_affinity_score']\n", + " plt.hist(platform_data, bins=20, alpha=0.6, label=platform)\n", + " \n", + "\n", + "plt.title('Distribution of Hate Speech Affinity Scores')\n", + "plt.xlabel('Hate Affinity Score')\n", + "plt.ylabel('Frequency')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Print statistics summary\n", + "print(\"\\nStatistics for hate affinity scores:\")\n", + "for platform in all_scores['platform'].unique():\n", + " platform_scores = all_scores[all_scores['platform'] == platform]['hate_affinity_score']\n", + " print(f\"\\n{platform}:\")\n", + " print(f\" Mean: {platform_scores.mean():.4f}\")\n", + " print(f\" Median: {platform_scores.median():.4f}\")\n", + " print(f\" Std Dev: {platform_scores.std():.4f}\")\n", + " print(f\" Min: {platform_scores.min():.4f}\")\n", + " print(f\" Max: {platform_scores.max():.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "55dbe289", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved segment-level scores to CSV files:\n", + "- controversial_segment_scores.csv\n", + "- lex_fridman_segment_scores.csv\n", + "\n", + "High risk segments (score > 0.5):\n", + "- Controversial: 0 segments (0.00% of all segments)\n", + "- Lex Fridman: 0 segments (0.00% of all segments)\n", + "\n", + "Medium risk segments (0.1 < score <= 0.5):\n", + "- Controversial: 1624 segments (2.55% of all segments)\n", + "- Lex Fridman: 10 segments (0.14% of all segments)\n" + ] + } + ], + "source": [ + "# Save segment-level scores to CSV files for further analysis\n", + "segment_scores_df.to_csv(\"data/controversial_segment_scores.csv\", index=False)\n", + "lex_segment_scores_df.to_csv(\"data/lex_fridman_segment_scores.csv\", index=False)\n", + "\n", + "print(\"Saved segment-level scores to CSV files:\")\n", + "print(\"- controversial_segment_scores.csv\")\n", + "print(\"- lex_fridman_segment_scores.csv\")\n", + "\n", + "# Count high risk segments in both datasets (score > 0.5)\n", + "controversial_high_risk = len(segment_scores_df[segment_scores_df['score'] > 0.5])\n", + "lex_high_risk = len(lex_segment_scores_df[lex_segment_scores_df['score'] > 0.5])\n", + "\n", + "print(f\"\\nHigh risk segments (score > 0.5):\")\n", + "print(f\"- Controversial: {controversial_high_risk} segments ({controversial_high_risk/len(segment_scores_df)*100:.2f}% of all segments)\")\n", + "print(f\"- Lex Fridman: {lex_high_risk} segments ({lex_high_risk/len(lex_segment_scores_df)*100:.2f}% of all segments)\")\n", + "\n", + "# Count medium risk segments (score between 0.1 and 0.5)\n", + "controversial_medium_risk = len(segment_scores_df[(segment_scores_df['score'] > 0.1) & (segment_scores_df['score'] <= 0.5)])\n", + "lex_medium_risk = len(lex_segment_scores_df[(lex_segment_scores_df['score'] > 0.1) & (lex_segment_scores_df['score'] <= 0.5)])\n", + "\n", + "print(f\"\\nMedium risk segments (0.1 < score <= 0.5):\")\n", + "print(f\"- Controversial: {controversial_medium_risk} segments ({controversial_medium_risk/len(segment_scores_df)*100:.2f}% of all segments)\")\n", + "print(f\"- Lex Fridman: {lex_medium_risk} segments ({lex_medium_risk/len(lex_segment_scores_df)*100:.2f}% of all segments)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "74e25bf5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved all segment-level data to CSV files\n" + ] }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "import random\n", - "\n", - "import pandas as pd\n", - "\n", - "from sentinel.simulation import (\n", - " LabeledGroup,\n", - " score_groups,\n", - " compare_aggregators,\n", - " run_grid_search,\n", - ")\n", - "\n", - "# Build \"labeled groups\": each podcast episode is one group of text segments.\n", - "# label = 1 -> controversial (rare / positive class)\n", - "# label = 0 -> Lex Fridman (common / negative class)\n", - "controversial_groups = [\n", - " LabeledGroup(name=str(file_path), label=1, observations=group[\"segment\"].tolist())\n", - " for file_path, group in results_df.groupby(\"file\")\n", - "]\n", - "lex_groups = [\n", - " LabeledGroup(name=str(file_path), label=0, observations=group[\"segment\"].tolist())\n", - " for file_path, group in lex_results_df.groupby(\"file\")\n", - "]\n", - "\n", - "# Subsample the controversial episodes so the demo runs quickly (the model has to\n", - "# embed every segment of every group). Feel free to raise these caps.\n", - "random.seed(0)\n", - "controversial_sample = random.sample(\n", - " controversial_groups, min(30, len(controversial_groups))\n", - ")\n", - "groups = controversial_sample + lex_groups\n", - "\n", - "n_pos = sum(g.label == 1 for g in groups)\n", - "n_neg = sum(g.label == 0 for g in groups)\n", - "print(f\"Built {len(groups)} labeled groups: {n_pos} controversial (label=1), {n_neg} Lex Fridman (label=0)\")\n", - "\n", - "# EXPENSIVE STEP (runs the sentence model): score every segment once. The result\n", - "# is reused by every aggregator below, so we don't re-encode anything.\n", - "scored = score_groups(index, groups, top_k=5, show_progress_bar=True)\n", - "\n", - "# CHEAP STEP: compare all six summarize metrics on the same scores.\n", - "comparison = pd.DataFrame(compare_aggregators(scored))\n", - "comparison_view = (\n", - " comparison[\n", - " [\n", - " \"aggregator\",\n", - " \"roc_auc\",\n", - " \"recall_at_n\",\n", - " \"precision_at_n\",\n", - " \"rank_ratio\",\n", - " \"f1\",\n", - " \"precision\",\n", - " \"recall\",\n", - " \"mean_separation\",\n", - " \"cohens_d\",\n", - " \"ks_statistic\",\n", - " ]\n", - " ]\n", - " .sort_values(\"roc_auc\", ascending=False)\n", - " .reset_index(drop=True)\n", - ")\n", - "comparison_view" - ], - "execution_count": null, - "outputs": [], - "id": "a9f35cf0" + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "# Show one representative metric from each evaluation family, per aggregator.\n", - "# We use separate subplots because the families live on different scales\n", - "# (roc_auc and f1 are in [0, 1], while Cohen's d can exceed 1).\n", - "families = [\"roc_auc\", \"f1\", \"cohens_d\"]\n", - "family_titles = {\n", - " \"roc_auc\": \"Ranking (ROC-AUC)\",\n", - " \"f1\": \"Threshold (best F1)\",\n", - " \"cohens_d\": \"Separation (Cohen's d)\",\n", - "}\n", - "\n", - "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", - "for ax, family in zip(axes, families):\n", - " values = comparison.set_index(\"aggregator\")[family]\n", - " values.plot(kind=\"bar\", ax=ax, color=\"steelblue\", alpha=0.85, edgecolor=\"white\")\n", - " ax.set_title(family_titles[family])\n", - " ax.set_xlabel(\"\")\n", - " ax.tick_params(axis=\"x\", rotation=30)\n", - " ax.grid(True, axis=\"y\", alpha=0.3)\n", - " ax.set_axisbelow(True)\n", - "\n", - "fig.suptitle(\"Aggregator comparison across the three evaluation families\", fontsize=14)\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "execution_count": null, - "outputs": [], - "id": "da17416d" + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Grid search: sweep top_k (each value re-scores the segments) and the\n", - "# per-observation threshold min_score_to_consider (applied cheaply), for every\n", - "# aggregator. Returns one row per (top_k, min_score, aggregator) combination.\n", - "grid = pd.DataFrame(\n", - " run_grid_search(\n", - " index,\n", - " groups,\n", - " top_k_values=[3, 5, 10],\n", - " min_score_values=[0.0, 0.1, 0.25],\n", - " )\n", - ")\n", - "\n", - "# Which single configuration separates the two classes best (by ROC-AUC)?\n", - "best = grid.sort_values(\"roc_auc\", ascending=False).reset_index(drop=True)\n", - "print(\"Top configurations by ROC-AUC:\")\n", - "best[[\"aggregator\", \"top_k\", \"min_score_to_consider\", \"roc_auc\", \"f1\", \"cohens_d\"]].head(10)" - ], - "execution_count": null, - "outputs": [], - "id": "b196a11d" + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "metadata": {}, - "source": [ - "# Heatmap of ROC-AUC over (top_k x min_score_to_consider) for the best aggregator,\n", - "# so we can pick a robust configuration rather than a single lucky point.\n", - "best_aggregator = best.iloc[0][\"aggregator\"]\n", - "sub = grid[grid[\"aggregator\"] == best_aggregator]\n", - "pivot = sub.pivot(index=\"top_k\", columns=\"min_score_to_consider\", values=\"roc_auc\")\n", - "\n", - "fig, ax = plt.subplots(figsize=(8, 5))\n", - "im = ax.imshow(pivot.values, cmap=\"viridis\", aspect=\"auto\")\n", - "ax.set_xticks(range(len(pivot.columns)))\n", - "ax.set_xticklabels(pivot.columns)\n", - "ax.set_yticks(range(len(pivot.index)))\n", - "ax.set_yticklabels(pivot.index)\n", - "ax.set_xlabel(\"min_score_to_consider\")\n", - "ax.set_ylabel(\"top_k\")\n", - "ax.set_title(f\"ROC-AUC heatmap for '{best_aggregator}'\")\n", - "for i in range(len(pivot.index)):\n", - " for j in range(len(pivot.columns)):\n", - " ax.text(j, i, f\"{pivot.values[i, j]:.3f}\", ha=\"center\", va=\"center\", color=\"white\")\n", - "fig.colorbar(im, ax=ax, label=\"ROC-AUC\")\n", - "plt.tight_layout()\n", - "plt.show()" - ], - "execution_count": null, - "outputs": [], - "id": "20e57208" + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Reading the results\n", - "\n", - "- The **aggregator comparison** shows how the choice of summarize metric changes how cleanly controversial and Lex Fridman episodes separate. If `skewness` is not the top row, another aggregator may fit this dataset better.\n", - "- The three families can disagree: an aggregator can rank well (high `roc_auc`) yet have a modest best-`f1`, or vice versa. Choose the family that matches how you will use the score - triage/ranking (ranking family), a hard yes/no cutoff (threshold family), or distribution monitoring (separation family).\n", - "- The **grid search** reports the best `(top_k, min_score_to_consider)` per aggregator, and the heatmap helps you pick a setting that is good across neighboring values (robust) rather than a single lucky cell.\n", - "\n", - "This ties directly into the \"wolf in sheep's clothing\" appendix below: aggregators such as `skewness`, `top_k_mean`, and `percentile_score` are built to catch a few high-scoring segments hidden inside otherwise benign content - exactly the signal that simple averaging would wash out." - ], - "id": "8054d065" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Segment-level statistics for hate speech scores:\n", + "\n", + "Controversial:\n", + " Count: 63780\n", + " Mean: 0.0032\n", + " Median: 0.0000\n", + " Std Dev: 0.0199\n", + " Min: 0.0000\n", + " Max: 0.2883\n", + " 25th Percentile: 0.0000\n", + " 75th Percentile: 0.0000\n", + " 90th Percentile: 0.0000\n", + " 95th Percentile: 0.0000\n", + " 99th Percentile: 0.1236\n", + " High risk segments (> 0.5): 0 (0.00%)\n", + " Medium risk segments (0.1-0.5): 1624 (2.55%)\n", + "\n", + "Lex Fridman:\n", + " Count: 7226\n", + " Mean: 0.0002\n", + " Median: 0.0000\n", + " Std Dev: 0.0047\n", + " Min: 0.0000\n", + " Max: 0.1622\n", + " 25th Percentile: 0.0000\n", + " 75th Percentile: 0.0000\n", + " 90th Percentile: 0.0000\n", + " 95th Percentile: 0.0000\n", + " 99th Percentile: 0.0000\n", + " High risk segments (> 0.5): 0 (0.00%)\n", + " Medium risk segments (0.1-0.5): 10 (0.14%)\n" + ] + } + ], + "source": [ + "# Create segment-level visualizations and comparisons\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# Save the completed segment dataframes to CSV files\n", + "segment_scores_df.to_csv(\"data/controversial_segment_scores.csv\", index=False)\n", + "lex_segment_scores_df.to_csv(\"data/lex_fridman_segment_scores.csv\", index=False)\n", + "\n", + "# Create dataframes with platform labels for combined analysis\n", + "controversial_segments = pd.DataFrame({\n", + " 'platform': ['Controversial'] * len(segment_scores_df),\n", + " 'score': segment_scores_df['score']\n", + "})\n", + "\n", + "lex_segments = pd.DataFrame({\n", + " 'platform': ['Lex Fridman'] * len(lex_segment_scores_df),\n", + " 'score': lex_segment_scores_df['score']\n", + "})\n", + "\n", + "# Combine segment scores\n", + "all_segment_scores = pd.concat([controversial_segments, lex_segments], ignore_index=True)\n", + "all_segment_scores.to_csv(\"data/all_segment_scores.csv\", index=False)\n", + "print(\"Saved all segment-level data to CSV files\")\n", + "\n", + "# Create a boxplot comparison for segment-level scores\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Extract data for each platform\n", + "platforms = all_segment_scores['platform'].unique()\n", + "segment_data = [all_segment_scores[all_segment_scores['platform'] == platform]['score'] for platform in platforms]\n", + "\n", + "# Create box plot\n", + "box = plt.boxplot(segment_data, patch_artist=True, labels=platforms)\n", + "colors = ['#ff9999', '#66b3ff']\n", + "for patch, color in zip(box['boxes'], colors):\n", + " patch.set_facecolor(color)\n", + "\n", + "plt.title('Segment-Level Hate Speech Score Comparison')\n", + "plt.ylabel('Hate Speech Score')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.show()\n", + "\n", + "# Create histograms to compare segment-level score distributions\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# Extract data for each platform\n", + "for i, platform in enumerate(platforms):\n", + " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", + " # Plot histogram with log scale for y-axis to better see the distribution tail\n", + " plt.hist(platform_data, bins=50, alpha=0.6, label=platform)\n", + "\n", + "plt.title('Distribution of Segment-Level Hate Speech Scores')\n", + "plt.xlabel('Hate Speech Score')\n", + "plt.ylabel('Frequency')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Create a second histogram with log scale for better comparison of tails\n", + "plt.figure(figsize=(12, 6))\n", + "for i, platform in enumerate(platforms):\n", + " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", + " # Plot histogram with log scale for y-axis\n", + " plt.hist(platform_data, bins=50, alpha=0.6, label=platform)\n", + "\n", + "plt.title('Distribution of Segment-Level Hate Speech Scores (Log Scale)')\n", + "plt.xlabel('Hate Speech Score')\n", + "plt.ylabel('Frequency (log scale)')\n", + "plt.yscale('log')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Create cumulative distribution function (CDF) plot\n", + "plt.figure(figsize=(12, 6))\n", + "for i, platform in enumerate(platforms):\n", + " platform_data = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", + " # Sort the data\n", + " sorted_data = np.sort(platform_data)\n", + " # Get the cumulative probabilities\n", + " p = 1. * np.arange(len(sorted_data)) / (len(sorted_data) - 1)\n", + " # Plot the CDF\n", + " plt.plot(sorted_data, p, label=platform, color=colors[i], linewidth=2)\n", + "\n", + "plt.title('Cumulative Distribution of Segment-Level Hate Speech Scores')\n", + "plt.xlabel('Hate Speech Score')\n", + "plt.ylabel('Cumulative Probability')\n", + "plt.grid(True, alpha=0.3)\n", + "plt.legend()\n", + "plt.show()\n", + "\n", + "# Print detailed statistics for segment-level scores\n", + "print(\"\\nSegment-level statistics for hate speech scores:\")\n", + "for platform in all_segment_scores['platform'].unique():\n", + " platform_scores = all_segment_scores[all_segment_scores['platform'] == platform]['score']\n", + " print(f\"\\n{platform}:\")\n", + " print(f\" Count: {len(platform_scores)}\")\n", + " print(f\" Mean: {platform_scores.mean():.4f}\")\n", + " print(f\" Median: {platform_scores.median():.4f}\")\n", + " print(f\" Std Dev: {platform_scores.std():.4f}\")\n", + " print(f\" Min: {platform_scores.min():.4f}\")\n", + " print(f\" Max: {platform_scores.max():.4f}\")\n", + " print(f\" 25th Percentile: {platform_scores.quantile(0.25):.4f}\")\n", + " print(f\" 75th Percentile: {platform_scores.quantile(0.75):.4f}\")\n", + " print(f\" 90th Percentile: {platform_scores.quantile(0.9):.4f}\")\n", + " print(f\" 95th Percentile: {platform_scores.quantile(0.95):.4f}\")\n", + " print(f\" 99th Percentile: {platform_scores.quantile(0.99):.4f}\")\n", + " \n", + " # Count high and medium risk segments\n", + " high_risk = len(platform_scores[platform_scores > 0.5])\n", + " high_risk_pct = high_risk / len(platform_scores) * 100\n", + " medium_risk = len(platform_scores[(platform_scores > 0.1) & (platform_scores <= 0.5)])\n", + " medium_risk_pct = medium_risk / len(platform_scores) * 100\n", + " \n", + " print(f\" High risk segments (> 0.5): {high_risk} ({high_risk_pct:.2f}%)\")\n", + " print(f\" Medium risk segments (0.1-0.5): {medium_risk} ({medium_risk_pct:.2f}%)\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "fb98a1fd", + "metadata": {}, + "source": [ + "## Tuning aggregation strategies with a simulation\n", + "\n", + "So far every episode was scored with the default aggregator (`skewness`). But Sentinel ships several \"summarize metrics\", and which one works best depends on your data. This section runs a small **simulation** (using `sentinel.simulation`) to compare them systematically on our labeled episodes.\n", + "\n", + "Two very different \"metrics\" are involved, and it helps to keep them separate:\n", + "\n", + "- **Summarize metric (the aggregation):** how an episode's many per-segment scores become a single number. Sentinel offers `skewness`, `mean_of_positives`, `top_k_mean`, `percentile_score`, `softmax_weighted_mean`, and `max_score`. This is *not* a ranking.\n", + "- **Evaluation metric:** given one number per episode plus a known label (controversial = 1, Lex Fridman = 0), how well are the two classes separated? Ranking is only one option. The harness reports **three families** so you can tune for whatever matters to you:\n", + " - **Ranking** (`roc_auc`, `recall_at_n`, `rank_ratio`): where do the known-positive episodes land in the leaderboard?\n", + " - **Threshold / classification** (`precision`, `recall`, `f1`): pick a cutoff on the per-episode score and count hits and misses.\n", + " - **Separation / distribution** (`mean_separation`, `cohens_d`, `ks_statistic`): threshold-free distance between the two score distributions.\n", + "\n", + "The expensive part (running the sentence model) is done once by `score_groups`; comparing aggregators and thresholds afterward is cheap." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a9f35cf0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Built 60 labeled groups: 30 controversial (label=1), 30 Lex Fridman (label=0)\n" + ] }, { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Conclusion and Key Findings\n", - "\n", - "In this notebook, we've built and evaluated a hate speech detection system using Sentinel with minimal training data. The key aspects of our approach include:\n", - "\n", - "1. **Efficient Model Creation**: We constructed a hate speech detection model using only ~1,500 extremist quotes from SPLC as positive examples, balanced against ~15,000 neutral Lex Fridman podcast segments. This demonstrates Sentinel's ability to work effectively with limited training data.\n", - "\n", - "2. **Cross-Dataset Evaluation**: By testing on a controversial podcast content (not seen during training) and comparing against Lex Fridman episodes, we validated the model's generalization capabilities and resistance to domain-specific biases.\n", - "\n", - "3. **Multi-level Analysis**: We analyzed content at both segment level (individual text chunks) and episode level (aggregated scores using skewness), showing how patterns of concerning language emerge even when individual segments might not cross thresholds.\n", - "\n", - "4. **Statistical Validation**: The distributions and percentile analysis demonstrate clear differentiation between the two content sources, with the other podcast showing significantly higher percentages of high-risk segments compared to Lex Fridman's podcast.\n", - "\n", - "5. **Practical Application**: The CSV exports and visualization tools enable further analysis and potential integration into content moderation workflows.\n", - "\n", - "This methodology showcases Sentinel's capability to detect rare class patterns in text with minimal examples, highlighting its potential for practical applications in online safety, content moderation, and harmful content detection across diverse platforms." + "data": { + "text/html": [ + "
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aggregatorroc_aucrecall_at_nprecision_at_nrank_ratiof1precisionrecallmean_separationcohens_dks_statistic
0max_score0.9644440.9333330.9333330.3728430.9508200.9354840.9666670.1425863.4272070.900000
1skewness0.9600000.9000000.9000000.3769750.9354840.9062500.9666670.1406062.7529960.866667
2top_k_mean0.9600000.9333330.9333330.3769750.9508200.9354840.9666670.1307003.3227320.900000
3percentile_score0.9455560.9333330.9333330.3905780.9508200.9354840.9666670.1230393.2725350.900000
4mean_of_positives0.9222220.9333330.9333330.4131270.9508200.9354840.9666670.1019053.0627120.900000
5softmax_weighted_mean0.9222220.9333330.9333330.4131270.9508200.9354840.9666670.1022323.0656140.900000
\n", + "
" ], - "id": "9c91aa37" + "text/plain": [ + " aggregator roc_auc recall_at_n precision_at_n rank_ratio \\\n", + "0 max_score 0.964444 0.933333 0.933333 0.372843 \n", + "1 skewness 0.960000 0.900000 0.900000 0.376975 \n", + "2 top_k_mean 0.960000 0.933333 0.933333 0.376975 \n", + "3 percentile_score 0.945556 0.933333 0.933333 0.390578 \n", + "4 mean_of_positives 0.922222 0.933333 0.933333 0.413127 \n", + "5 softmax_weighted_mean 0.922222 0.933333 0.933333 0.413127 \n", + "\n", + " f1 precision recall mean_separation cohens_d ks_statistic \n", + "0 0.950820 0.935484 0.966667 0.142586 3.427207 0.900000 \n", + "1 0.935484 0.906250 0.966667 0.140606 2.752996 0.866667 \n", + "2 0.950820 0.935484 0.966667 0.130700 3.322732 0.900000 \n", + "3 0.950820 0.935484 0.966667 0.123039 3.272535 0.900000 \n", + "4 0.950820 0.935484 0.966667 0.101905 3.062712 0.900000 \n", + "5 0.950820 0.935484 0.966667 0.102232 3.065614 0.900000 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import random\n", + "\n", + "import pandas as pd\n", + "\n", + "from sentinel.simulation import (\n", + " LabeledGroup,\n", + " score_groups,\n", + " compare_aggregators,\n", + " run_grid_search,\n", + ")\n", + "\n", + "# Build \"labeled groups\": each podcast episode is one group of text segments.\n", + "# label = 1 -> controversial (rare / positive class)\n", + "# label = 0 -> Lex Fridman (common / negative class)\n", + "controversial_groups = [\n", + " LabeledGroup(name=str(file_path), label=1, observations=group[\"segment\"].tolist())\n", + " for file_path, group in results_df.groupby(\"file\")\n", + "]\n", + "lex_groups = [\n", + " LabeledGroup(name=str(file_path), label=0, observations=group[\"segment\"].tolist())\n", + " for file_path, group in lex_results_df.groupby(\"file\")\n", + "]\n", + "\n", + "# Why 30? It matches the 30 Lex Fridman episodes sampled earlier, so the\n", + "# evaluation set is a balanced 30-vs-30 rather than 100-vs-30. Balance changes\n", + "# what the metrics mean: top_n defaults to the number of positives, and\n", + "# precision/f1 move with the class ratio (roc_auc and recall do not).\n", + "# It also caps the runtime, which scales with the number of *segments* rather\n", + "# than episodes. Raise both sides together to keep the comparison balanced.\n", + "random.seed(0)\n", + "controversial_sample = random.sample(\n", + " controversial_groups, min(30, len(controversial_groups))\n", + ")\n", + "groups = controversial_sample + lex_groups\n", + "\n", + "n_pos = sum(g.label == 1 for g in groups)\n", + "n_neg = sum(g.label == 0 for g in groups)\n", + "print(f\"Built {len(groups)} labeled groups: {n_pos} controversial (label=1), {n_neg} Lex Fridman (label=0)\")\n", + "\n", + "# EXPENSIVE STEP (runs the sentence model): score every segment once. The result\n", + "# is reused by every aggregator below, so we don't re-encode anything.\n", + "scored = score_groups(index, groups, top_k=5, show_progress_bar=True)\n", + "\n", + "# CHEAP STEP: compare all six summarize metrics on the same scores.\n", + "comparison = pd.DataFrame(compare_aggregators(scored))\n", + "comparison_view = (\n", + " comparison[\n", + " [\n", + " \"aggregator\",\n", + " \"roc_auc\",\n", + " \"recall_at_n\",\n", + " \"precision_at_n\",\n", + " \"rank_ratio\",\n", + " \"f1\",\n", + " \"precision\",\n", + " \"recall\",\n", + " \"mean_separation\",\n", + " \"cohens_d\",\n", + " \"ks_statistic\",\n", + " ]\n", + " ]\n", + " .sort_values(\"roc_auc\", ascending=False)\n", + " .reset_index(drop=True)\n", + ")\n", + "comparison_view" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "da17416d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# Show one representative metric from each evaluation family, per aggregator.\n", + "# We use separate subplots because the families live on different scales\n", + "# (roc_auc and f1 are in [0, 1], while Cohen's d can exceed 1).\n", + "families = [\"roc_auc\", \"f1\", \"cohens_d\"]\n", + "family_titles = {\n", + " \"roc_auc\": \"Ranking (ROC-AUC)\",\n", + " \"f1\": \"Threshold (best F1)\",\n", + " \"cohens_d\": \"Separation (Cohen's d)\",\n", + "}\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "for ax, family in zip(axes, families):\n", + " values = comparison.set_index(\"aggregator\")[family]\n", + " values.plot(kind=\"bar\", ax=ax, color=\"steelblue\", alpha=0.85, edgecolor=\"white\")\n", + " ax.set_title(family_titles[family])\n", + " ax.set_xlabel(\"\")\n", + " ax.tick_params(axis=\"x\", rotation=30)\n", + " ax.grid(True, axis=\"y\", alpha=0.3)\n", + " ax.set_axisbelow(True)\n", + "\n", + "fig.suptitle(\"Aggregator comparison across the three evaluation families\", fontsize=14)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "b196a11d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top configurations by ROC-AUC:\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "vscode": { - "languageId": "markdown" - } - }, - "source": [ - "## Appendix: Detecting the \"Wolf in Sheep's Clothes\" - The Power of Skewness\n", - "\n", - "Our analysis reveals an important pattern in how harmful content manifests across different sources, demonstrating Sentinel's ability to detect what we might call the \"wolf in sheep's clothes\" phenomenon.\n", - "\n", - "### Understanding the Distribution Patterns\n", - "\n", - "Looking at the segment-level histograms and statistics, we observe:\n", - "\n", - "1. **Low Central Tendency in Both Sources**: Both Lex Fridman podcasts and the other podcast have segment score distributions with relatively low central tendency (mean/median) - the majority of content segments from both sources receive low hate speech scores.\n", - "\n", - "2. **Critical Difference in Distribution Shape**: While both distributions have similar centers, the other podcast' distribution is notably **right-skewed with a heavy tail** - showing a relatively small number of highly toxic segments that score much higher than the average content.\n", - "\n", - "3. **The \"Hidden Toxicity\" Problem**: This pattern represents a common challenge in content moderation - harmful content often appears as occasional \"spikes\" within otherwise benign material. Simple averaging methods would dilute these spikes, potentially missing concerning content.\n", - "\n", - "### How Sentinel's Episode-Level Scoring Captures This Pattern\n", - "\n", - "Sentinel's methodology effectively addresses this challenge through:\n", - "\n", - "1. **Statistical Sensitivity to Skewness**: Rather than simple averaging, the episode-level scores account for distribution shape, giving higher weight to right-skewed distributions where even a few high-scoring segments exist.\n", - "\n", - "2. **Capturing Rare but Significant Signals**: This approach successfully identifies content sources that occasionally \"show their teeth\" with harmful rhetoric, even when such rhetoric represents a small percentage of the total content.\n", - "\n", - "3. **Practical Moderation Value**: The resulting episode-level scores place the other podcast episodes significantly higher than Lex Fridman episodes, creating a clear prioritization signal for content moderation efforts.\n", - "\n", - "This approach demonstrates why robust hate speech detection requires attention not just to average content characteristics, but to distribution patterns that might reveal occasional but significant harmful content within otherwise unremarkable material." + "data": { + "text/html": [ + "
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aggregatortop_kmin_score_to_considerroc_aucf1cohens_d
0skewness50.00.9955560.9666674.111937
1skewness100.00.9933330.9677424.091533
2skewness30.10.9911110.9523813.518106
3skewness30.00.9900000.9523813.699453
4max_score50.00.9788890.9508203.084493
5top_k_mean30.10.9777780.9677423.555129
6top_k_mean30.00.9777780.9677423.419085
7top_k_mean100.00.9766670.9333332.928998
8top_k_mean50.00.9766670.9523813.349176
9max_score30.10.9755560.9677423.656795
\n", + "
" ], - "id": "8ac615b3" + "text/plain": [ + " aggregator top_k min_score_to_consider roc_auc f1 cohens_d\n", + "0 skewness 5 0.0 0.995556 0.966667 4.111937\n", + "1 skewness 10 0.0 0.993333 0.967742 4.091533\n", + "2 skewness 3 0.1 0.991111 0.952381 3.518106\n", + "3 skewness 3 0.0 0.990000 0.952381 3.699453\n", + "4 max_score 5 0.0 0.978889 0.950820 3.084493\n", + "5 top_k_mean 3 0.1 0.977778 0.967742 3.555129\n", + "6 top_k_mean 3 0.0 0.977778 0.967742 3.419085\n", + "7 top_k_mean 10 0.0 0.976667 0.933333 2.928998\n", + "8 top_k_mean 5 0.0 0.976667 0.952381 3.349176\n", + "9 max_score 3 0.1 0.975556 0.967742 3.656795" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" + ], + "source": [ + "# Grid search: sweep top_k (each value re-scores the segments) and the\n", + "# per-observation threshold min_score_to_consider (applied cheaply), for every\n", + "# aggregator. Returns one row per (top_k, min_score, aggregator) combination.\n", + "grid = pd.DataFrame(\n", + " run_grid_search(\n", + " index,\n", + " groups,\n", + " top_k_values=[3, 5, 10],\n", + " min_score_values=[0.0, 0.1, 0.25],\n", + " )\n", + ")\n", + "\n", + "# Which single configuration separates the two classes best (by ROC-AUC)?\n", + "best = grid.sort_values(\"roc_auc\", ascending=False).reset_index(drop=True)\n", + "print(\"Top configurations by ROC-AUC:\")\n", + "best[[\"aggregator\", \"top_k\", \"min_score_to_consider\", \"roc_auc\", \"f1\", \"cohens_d\"]].head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "20e57208", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Heatmap of ROC-AUC over (top_k x min_score_to_consider) for the best aggregator,\n", + "# so we can pick a robust configuration rather than a single lucky point.\n", + "best_aggregator = best.iloc[0][\"aggregator\"]\n", + "sub = grid[grid[\"aggregator\"] == best_aggregator]\n", + "pivot = sub.pivot(index=\"top_k\", columns=\"min_score_to_consider\", values=\"roc_auc\")\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "im = ax.imshow(pivot.values, cmap=\"viridis\", aspect=\"auto\")\n", + "ax.set_xticks(range(len(pivot.columns)))\n", + "ax.set_xticklabels(pivot.columns)\n", + "ax.set_yticks(range(len(pivot.index)))\n", + "ax.set_yticklabels(pivot.index)\n", + "ax.set_xlabel(\"min_score_to_consider\")\n", + "ax.set_ylabel(\"top_k\")\n", + "ax.set_title(f\"ROC-AUC heatmap for '{best_aggregator}'\")\n", + "for i in range(len(pivot.index)):\n", + " for j in range(len(pivot.columns)):\n", + " ax.text(j, i, f\"{pivot.values[i, j]:.3f}\", ha=\"center\", va=\"center\", color=\"white\")\n", + "fig.colorbar(im, ax=ax, label=\"ROC-AUC\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8054d065", + "metadata": {}, + "source": [ + "### Reading the results\n", + "\n", + "- The **aggregator comparison** shows how the choice of summarize metric changes how cleanly controversial and Lex Fridman episodes separate. If `skewness` is not the top row, another aggregator may fit this dataset better.\n", + "- The three families can disagree: an aggregator can rank well (high `roc_auc`) yet have a modest best-`f1`, or vice versa. Choose the family that matches how you will use the score - triage/ranking (ranking family), a hard yes/no cutoff (threshold family), or distribution monitoring (separation family).\n", + "- The **grid search** reports the best `(top_k, min_score_to_consider)` per aggregator, and the heatmap helps you pick a setting that is good across neighboring values (robust) rather than a single lucky cell.\n", + "\n", + "This ties directly into the \"wolf in sheep's clothing\" appendix below: aggregators such as `skewness`, `top_k_mean`, and `percentile_score` are built to catch a few high-scoring segments hidden inside otherwise benign content - exactly the signal that simple averaging would wash out." + ] + }, + { + "cell_type": "markdown", + "id": "9c91aa37", + "metadata": {}, + "source": [ + "## Conclusion and Key Findings\n", + "\n", + "In this notebook, we've built and evaluated a hate speech detection system using Sentinel with minimal training data. The key aspects of our approach include:\n", + "\n", + "1. **Efficient Model Creation**: We constructed a hate speech detection model using only ~1,500 extremist quotes from SPLC as positive examples, balanced against ~15,000 neutral Lex Fridman podcast segments. This demonstrates Sentinel's ability to work effectively with limited training data.\n", + "\n", + "2. **Cross-Dataset Evaluation**: By testing on a controversial podcast content (not seen during training) and comparing against Lex Fridman episodes, we validated the model's generalization capabilities and resistance to domain-specific biases.\n", + "\n", + "3. **Multi-level Analysis**: We analyzed content at both segment level (individual text chunks) and episode level (aggregated scores using skewness), showing how patterns of concerning language emerge even when individual segments might not cross thresholds.\n", + "\n", + "4. **Statistical Validation**: The distributions and percentile analysis demonstrate clear differentiation between the two content sources, with the other podcast showing significantly higher percentages of high-risk segments compared to Lex Fridman's podcast.\n", + "\n", + "5. **Practical Application**: The CSV exports and visualization tools enable further analysis and potential integration into content moderation workflows.\n", + "\n", + "This methodology showcases Sentinel's capability to detect rare class patterns in text with minimal examples, highlighting its potential for practical applications in online safety, content moderation, and harmful content detection across diverse platforms." + ] + }, + { + "cell_type": "markdown", + "id": "8ac615b3", + "metadata": { + "vscode": { + "languageId": "markdown" } + }, + "source": [ + "## Appendix: Detecting the \"Wolf in Sheep's Clothes\" - The Power of Skewness\n", + "\n", + "Our analysis reveals an important pattern in how harmful content manifests across different sources, demonstrating Sentinel's ability to detect what we might call the \"wolf in sheep's clothes\" phenomenon.\n", + "\n", + "### Understanding the Distribution Patterns\n", + "\n", + "Looking at the segment-level histograms and statistics, we observe:\n", + "\n", + "1. **Low Central Tendency in Both Sources**: Both Lex Fridman podcasts and the other podcast have segment score distributions with relatively low central tendency (mean/median) - the majority of content segments from both sources receive low hate speech scores.\n", + "\n", + "2. **Critical Difference in Distribution Shape**: While both distributions have similar centers, the other podcast' distribution is notably **right-skewed with a heavy tail** - showing a relatively small number of highly toxic segments that score much higher than the average content.\n", + "\n", + "3. **The \"Hidden Toxicity\" Problem**: This pattern represents a common challenge in content moderation - harmful content often appears as occasional \"spikes\" within otherwise benign material. Simple averaging methods would dilute these spikes, potentially missing concerning content.\n", + "\n", + "### How Sentinel's Episode-Level Scoring Captures This Pattern\n", + "\n", + "Sentinel's methodology effectively addresses this challenge through:\n", + "\n", + "1. **Statistical Sensitivity to Skewness**: Rather than simple averaging, the episode-level scores account for distribution shape, giving higher weight to right-skewed distributions where even a few high-scoring segments exist.\n", + "\n", + "2. **Capturing Rare but Significant Signals**: This approach successfully identifies content sources that occasionally \"show their teeth\" with harmful rhetoric, even when such rhetoric represents a small percentage of the total content.\n", + "\n", + "3. **Practical Moderation Value**: The resulting episode-level scores place the other podcast episodes significantly higher than Lex Fridman episodes, creating a clear prioritization signal for content moderation efforts.\n", + "\n", + "This approach demonstrates why robust hate speech detection requires attention not just to average content characteristics, but to distribution patterns that might reveal occasional but significant harmful content within otherwise unremarkable material." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (Sentinel)", + "language": "python", + "name": "sentinel" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.20" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 66f86fc9234ccd68737cddc1726ab1d8accf4804 Mon Sep 17 00:00:00 2001 From: Wei Xiao <11197323+wxiao0421@users.noreply.github.com> Date: Mon, 27 Jul 2026 15:57:39 -0700 Subject: [PATCH 3/4] Make the notebook's episode sampling reproducible and interpret the results Two sampling steps were unseeded and the transcript list came back in filesystem order, so every run scored a different set of episodes and produced different numbers. Seed both samples and sort the glob so a re-run is comparable to the one recorded here. Add a "What we found" section interpreting the tuning results. It is written qualitatively on purpose: with 30 episodes per class, differences of a few thousandths of ROC-AUC are inside the margin of error, and separate runs did reorder the leading aggregators. The claims that survive are that the top configurations are all skewness, and that min_score_to_consider matters more for skewness than for the others because skewness is computed over the whole score array while the other five filter to scores > 0. Note: the saved outputs predate the seed, so re-running will produce slightly different figures until the notebook is executed again. Co-authored-by: Cursor --- examples/sentinel_against_hate.ipynb | 28 ++++++++++++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/examples/sentinel_against_hate.ipynb b/examples/sentinel_against_hate.ipynb index e1bbcf7..9418ebb 100644 --- a/examples/sentinel_against_hate.ipynb +++ b/examples/sentinel_against_hate.ipynb @@ -1326,6 +1326,10 @@ "source": [ "# Calculate how many negative examples we need (10x the number of positives)\n", "\n", + "# Seeded so the negative segments sampled below are the same on every run and\n", + "# the numbers reported later in the notebook stay comparable.\n", + "random.seed(42)\n", + "\n", "num_positives = len(segmented_df)\n", "num_negatives = num_positives * 10\n", "print(f\"Number of positive examples: {num_positives}\")\n", @@ -1574,7 +1578,9 @@ "# List transcript files and load a sample for evaluation\n", "data_dir = \"data/podcast_examples\" # Define data_dir for this cell\n", "\n", - "transcript_files = glob.glob(os.path.join(data_dir, \"transcripts\", \"**\", \"*.txt\"), recursive=True)\n", + "# sorted() because glob returns filesystem order, which differs between machines.\n", + "# Without it the \"first 100\" episodes used below are not the same set twice.\n", + "transcript_files = sorted(glob.glob(os.path.join(data_dir, \"transcripts\", \"**\", \"*.txt\"), recursive=True))\n", "print(f\"Found {len(transcript_files)} transcript files.\")\n", "\n", "# Check if transcript files were found before trying to open one\n", @@ -1747,7 +1753,9 @@ "# We'll reuse the same index we loaded earlier\n", "print(\"Using the same Sentinel index for Lex Fridman podcasts...\")\n", "\n", - "# Select 30 random episodes from the Lex Fridman dataset\n", + "# Select 30 random episodes from the Lex Fridman dataset. Seeded so the same\n", + "# episodes are picked on every run.\n", + "random.seed(42)\n", "num_neutral_episodes = min(30, len(neutral_dataset))\n", "random_neutral_episodes = random.sample(neutral_dataset, num_neutral_episodes)\n", "print(f\"Selected {num_neutral_episodes} random Lex Fridman podcast episodes\")\n", @@ -2736,6 +2744,22 @@ "This ties directly into the \"wolf in sheep's clothing\" appendix below: aggregators such as `skewness`, `top_k_mean`, and `percentile_score` are built to catch a few high-scoring segments hidden inside otherwise benign content - exactly the signal that simple averaging would wash out." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### What we found\n", + "\n", + "Three conclusions held up on this dataset, and one trap is worth avoiding.\n", + "\n", + "**Skewness is the right default.** After the grid search, the highest-scoring configurations are all `skewness`, and tuning lifts it clearly above what any aggregator reaches at the out-of-the-box settings. Sentinel's default choice is a good one for content shaped like this - long, mostly benign transcripts with occasional concentrated spikes.\n", + "\n", + "**The noise floor deserves a sweep, not an assumption.** `min_score_to_consider` affects `skewness` far more than the other aggregators, and there is a concrete reason. `skewness` computes `(mean - median) / std` over the *entire* array of segment scores, zeros included, while the other five first filter with `scores[scores > 0]`. Raising the floor therefore reshapes the distribution that `skewness` sees, whereas for the others it simply removes inputs. Its best value also interacts with `top_k`, so sweep the two together rather than tuning either on its own.\n", + "\n", + "**Do not over-read small gaps.** With 30 episodes per class, differences of a few thousandths of ROC-AUC sit inside the margin of error: re-running against a different sample can reorder the leading aggregators. Read the table as \"this group is clearly ahead of that group\" rather than as an exact ranking, and prefer a configuration surrounded by other strong cells in the heatmap over the single best cell, which may just be lucky. Raising the episode counts is the way to tighten this up." + ], + "id": "c7c28001" + }, { "cell_type": "markdown", "id": "9c91aa37", From a6435c0ca6ee9feb80b9837be8c5e0a9becd04e3 Mon Sep 17 00:00:00 2001 From: Wei Xiao <11197323+wxiao0421@users.noreply.github.com> Date: Tue, 28 Jul 2026 14:06:17 -0700 Subject: [PATCH 4/4] Sample evaluation episodes randomly instead of taking the alphabetical head Sorting the transcript glob made the selection reproducible but biased it badly. The corpus holds three shows, so transcript_files[:100] took all 16 ajn-live episodes plus the first 84 alex-jones-show ones and never reached special-reports at all. Those episodes are also far shorter, which starves the aggregators of the extreme segments they rely on: the controversial episodes scored above zero on 1.4% of segments versus 1.2% for Lex Fridman, leaving almost nothing to separate, and ROC-AUC fell to 0.70. Draw a seeded random sample over the sorted pool instead, which is both deterministic and representative (86 alex-jones-show / 14 special-reports, matching the corpus). Segment-level separation returns to 1.9% versus 1.0%. Re-ran the notebook on that sample. Untuned, the aggregators cluster between 0.80 and 0.89 ROC-AUC with skewness ahead at 0.894; the grid search lifts skewness with top_k=5 and min_score_to_consider=0.0 to 0.977, and the top three configurations are all skewness. Cross-checked that compare_aggregators and run_grid_search agree exactly where they overlap. Co-authored-by: Cursor --- examples/sentinel_against_hate.ipynb | 448 +++++++++++++-------------- 1 file changed, 224 insertions(+), 224 deletions(-) diff --git a/examples/sentinel_against_hate.ipynb b/examples/sentinel_against_hate.ipynb index 9418ebb..5936edc 100644 --- a/examples/sentinel_against_hate.ipynb +++ b/examples/sentinel_against_hate.ipynb @@ -44,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "d3aa300e", "metadata": {}, "outputs": [], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "64a69da0", "metadata": {}, "outputs": [], @@ -102,7 +102,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "f0401ec2", "metadata": {}, "outputs": [ @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "4b2ac613", "metadata": {}, "outputs": [ @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "f95affd4", "metadata": {}, "outputs": [ @@ -734,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "81c31a4a", "metadata": {}, "outputs": [ @@ -770,7 +770,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "609a3723", "metadata": {}, "outputs": [ @@ -967,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "d5092eff-7e7d-4129-8e01-bbf9b28ee016", "metadata": {}, "outputs": [ @@ -986,8 +986,8 @@ "Using cached file at ~/.cache/huggingface/neutral/lex-fridman-podcastUsing cached file.parquet\n", "Successfully loaded data with 346 rows\n", "Processed 346 episodes with segments\n", - "Extracting segments: 100%|█████████████████| 316/316 [00:00<00:00, 3308.40it/s]\n", - "Collected 757376 segments before sampling\n", + "Extracting segments: 100%|█████████████████| 316/316 [00:00<00:00, 3296.62it/s]\n", + "Collected 761946 segments before sampling\n", "Randomly sampled 15000 segments\n", "Saved 15000 training segments to neutral-segments-training.csv\n", "Saved 30 evaluation episodes to neutral-episodes-eval.parquet\n" @@ -1036,7 +1036,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "922bed03", "metadata": {}, "outputs": [ @@ -1095,35 +1095,35 @@ " \n", " 0\n", " neutral_podcast\n", - " our limited human minds to understand\n", + " and gave people a big massive lesson in civics.\n", " 0\n", " 0\n", " \n", " \n", " 1\n", " neutral_podcast\n", - " like Baxter and Sawyer.\n", + " I'm humiliated about that kind of stuff\n", " 1\n", " 0\n", " \n", " \n", " 2\n", " neutral_podcast\n", - " Yeah.\n", + " I don't know.\n", " 2\n", " 0\n", " \n", " \n", " 3\n", " neutral_podcast\n", - " is totally off topic and unacceptable in this subreddit and totally on topic and acceptable\n", + " Yeah, like it made me imagine\n", " 3\n", " 0\n", " \n", " \n", " 4\n", " neutral_podcast\n", - " that evolutionary process.\n", + " but people love it when I ask it,\n", " 4\n", " 0\n", " \n", @@ -1132,19 +1132,12 @@ "" ], "text/plain": [ - " filename \\\n", - "0 neutral_podcast \n", - "1 neutral_podcast \n", - "2 neutral_podcast \n", - "3 neutral_podcast \n", - "4 neutral_podcast \n", - "\n", - " paragraph_segment \\\n", - "0 our limited human minds to understand \n", - "1 like Baxter and Sawyer. \n", - "2 Yeah. \n", - "3 is totally off topic and unacceptable in this subreddit and totally on topic and acceptable \n", - "4 that evolutionary process. \n", + " filename paragraph_segment \\\n", + "0 neutral_podcast and gave people a big massive lesson in civics. \n", + "1 neutral_podcast I'm humiliated about that kind of stuff \n", + "2 neutral_podcast I don't know. \n", + "3 neutral_podcast Yeah, like it made me imagine \n", + "4 neutral_podcast but people love it when I ask it, \n", "\n", " segment_id label \n", "0 0 0 \n", @@ -1197,21 +1190,21 @@ " \n", " 4990\n", " neutral_podcast\n", - " Yeah.\n", + " Really?\n", " 3474\n", " 0\n", " \n", " \n", " 3781\n", " neutral_podcast\n", - " the observer wishes to be entertained and has some mechanism of enforcing their desire\n", + " If land wars turn into an inescapable quagmire each time\n", " 2265\n", " 0\n", " \n", " \n", " 11523\n", " neutral_podcast\n", - " What did you take away from that experience?\n", + " And for the military, in some sense,\n", " 10007\n", " 0\n", " \n", @@ -1225,7 +1218,7 @@ " \n", " 13262\n", " neutral_podcast\n", - " If you were to lay out a perfect, productive day,\n", + " through that decision tree that Ryan was presenting,\n", " 11746\n", " 0\n", " \n", @@ -1246,28 +1239,28 @@ " \n", " 2857\n", " neutral_podcast\n", - " But we found that a reset switch recently,\n", + " Right.\n", " 1341\n", " 0\n", " \n", " \n", " 3115\n", " neutral_podcast\n", - " Right.\n", + " Yeah, so if you dig in on the idea of this reference frame,\n", " 1599\n", " 0\n", " \n", " \n", " 4972\n", " neutral_podcast\n", - " because that's what you're trying to learn.\n", + " an important moment in human history.\n", " 3456\n", " 0\n", " \n", " \n", " 6335\n", " neutral_podcast\n", - " How suspicious should I be when I'm traveling in Ukraine or different parts of the world when an attractive female walks up to me and shows any kind of attention?\n", + " I guess I shit the bed because he looks at me and he just goes, okay. All right. Okay. Well,\n", " 4819\n", " 0\n", " \n", @@ -1291,17 +1284,17 @@ "6335 neutral_podcast \n", "\n", " paragraph_segment \\\n", - "4990 Yeah. \n", - "3781 the observer wishes to be entertained and has some mechanism of enforcing their desire \n", - "11523 What did you take away from that experience? \n", + "4990 Really? \n", + "3781 If land wars turn into an inescapable quagmire each time \n", + "11523 And for the military, in some sense, \n", "47 on june 3, 2009, turner was arrested and charged with inciting injury to persons or property. in a blog post, he had asked connecticut catholics to “ take up arms ” against two lawmakers and an employee of the office of state ethics. turner also threatened to release the home addresses of these three men. a jury acquitted turner of the charges in september 2011. \n", - "13262 If you were to lay out a perfect, productive day, \n", + "13262 through that decision tree that Ryan was presenting, \n", "... ... \n", "99 “ we were torn apart in the civil war — brother against brother, north against south, party against party.. what changed? now we have blacks and whites fighting, reds and yellows fighting, democrats and republicans fighting, men and women fighting. ” — speaking at a rally in florence, alabama, september, \n", - "2857 But we found that a reset switch recently, \n", - "3115 Right. \n", - "4972 because that's what you're trying to learn. \n", - "6335 How suspicious should I be when I'm traveling in Ukraine or different parts of the world when an attractive female walks up to me and shows any kind of attention? \n", + "2857 Right. \n", + "3115 Yeah, so if you dig in on the idea of this reference frame, \n", + "4972 an important moment in human history. \n", + "6335 I guess I shit the bed because he looks at me and he just goes, okay. All right. Okay. Well, \n", "\n", " segment_id label \n", "4990 3474 0 \n", @@ -1451,7 +1444,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "e966e3b9", "metadata": {}, "outputs": [ @@ -1530,7 +1523,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "bd2f023f-b51f-4f66-b0ab-296439378c03", "metadata": {}, "outputs": [ @@ -1562,7 +1555,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "id": "a374928d-5c31-4f7e-951f-58f171ad90a7", "metadata": {}, "outputs": [ @@ -1596,7 +1589,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "id": "455888f7", "metadata": {}, "outputs": [ @@ -1622,17 +1615,17 @@ ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Token indices sequence length is longer than the specified maximum sequence length for this model (26877 > 512). Running this sequence through the model will result in indexing errors\n" + "Token indices sequence length is longer than the specified maximum sequence length for this model (77886 > 512). Running this sequence through the model will result in indexing errors\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Created 63780 segments\n" + "Created 52185 segments\n" ] } ], @@ -1650,9 +1643,16 @@ " cleaned = re.sub(r'\\d{2}:\\d{2}:\\d{2}\\s*->\\s*\\d{2}:\\d{2}:\\d{2}\\s*', '', cleaned)\n", " return cleaned.strip()\n", "\n", + "# Take a random sample of 100 episodes rather than the first 100. The corpus\n", + "# is grouped by show, so an alphabetical slice would cover only the shows that\n", + "# sort first and would miss the rest entirely. Seeded so the same 100 episodes\n", + "# are drawn on every run.\n", + "random.seed(42)\n", + "selected_transcripts = random.sample(transcript_files, min(100, len(transcript_files)))\n", + "\n", "# Load and clean transcripts\n", "cleaned_transcripts = []\n", - "for file_path in tqdm(transcript_files[:100], desc=\"Processing transcripts\"): # Sample 100 episodes\n", + "for file_path in tqdm(selected_transcripts, desc=\"Processing transcripts\"):\n", " try:\n", " with open(file_path, 'r', encoding='utf-8', errors='ignore') as f:\n", " text = f.read()\n", @@ -1718,7 +1718,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "8e1c158c", "metadata": {}, "outputs": [ @@ -1741,7 +1741,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Created 7226 segments from Lex Fridman podcasts\n" + "Created 7008 segments from Lex Fridman podcasts\n" ] } ], @@ -1831,7 +1831,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "id": "29443f0c", "metadata": {}, "outputs": [ @@ -1847,7 +1847,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", "text/plain": [ "
" ] @@ -1873,18 +1873,18 @@ "Statistics for hate affinity scores:\n", "\n", "Controversial:\n", - " Mean: 0.1499\n", - " Median: 0.1429\n", - " Std Dev: 0.0668\n", + " Mean: 0.1185\n", + " Median: 0.1118\n", + " Std Dev: 0.0722\n", " Min: 0.0000\n", - " Max: 0.3100\n", + " Max: 0.3922\n", "\n", "Lex Fridman:\n", - " Mean: 0.0124\n", + " Mean: 0.0355\n", " Median: 0.0000\n", - " Std Dev: 0.0343\n", + " Std Dev: 0.0967\n", " Min: 0.0000\n", - " Max: 0.1436\n" + " Max: 0.5095\n" ] } ], @@ -1963,7 +1963,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "55dbe289", "metadata": {}, "outputs": [ @@ -1980,8 +1980,8 @@ "- Lex Fridman: 0 segments (0.00% of all segments)\n", "\n", "Medium risk segments (0.1 < score <= 0.5):\n", - "- Controversial: 1624 segments (2.55% of all segments)\n", - "- Lex Fridman: 10 segments (0.14% of all segments)\n" + "- Controversial: 976 segments (1.87% of all segments)\n", + "- Lex Fridman: 72 segments (1.03% of all segments)\n" ] } ], @@ -2013,7 +2013,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "id": "74e25bf5", "metadata": {}, "outputs": [ @@ -2026,7 +2026,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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Z4/tw5coVo27duoYkIygoyBgwYIDx3//+10hISMjUt127doa3t3emz/etsXTv3t1wc3MzDh06ZGk7deqU4e3tbbRr187SlvF7ok2bNsaNGzcs7ZcvXzb8/PyMIUOGWK3jzJkzhq+vr6X977//NiQZ06ZNy3H7stKvXz+jXLlymdrj4+NzHTOv+9Hvv//ekGRERUVZ+qWnpxv33Xdftr8bbrV7926jVKlShiQjNDTUGD58uLF48WIjJSXFqt/FixcNb29vo2XLlsbVq1etnrv155LV523y5MmGyWSy+nn+83fx7XzO6tSpY3Tu3DnH7QNQ8DjlHkCB8vLyynG2+4y/+P/444+3PYGcu7t7lqcfZ+epp56yHLmVpMcee0wVK1bUL7/8clvrz6tffvlFrq6uevHFF63aR44cKcMw9Ouvv1q1h4WFqWbNmpblRo0aycfHR4cPH851PYGBgerbt6+lrWTJknrxxReVnJysNWvW2By7yWTSsmXL9Pbbb6tMmTL65ptvFBkZqaCgIPXu3dtyDb1hGPr+++/VpUsXGYahc+fOWR7h4eG6dOmStm7dKklaunSpKleurK5du1rW4+Hhke3ZHF5eXpYjz5JUt25d+fn56a677rI6Gpjx/4z3yZaYMgwcONDqyHfG2Re5vff5tXDhQrVt21ZlypSxijMsLEzp6emWU7/vu+8+lS9fXvPnz7e89u+//9aKFSvUu3dvq/Huuusu1atXz2q8jCOB2Z0NkZtu3bppxYoVmR4Zp83f6tazXjKO7N9zzz2SlOl9z8qiRYtkNpvVq1cvq20IDAxU7dq1c92G+++/Xxs2bFDXrl21Y8cOTZ06VeHh4apcubJ++umnTP1btWqlpk2bWparVaumbt26admyZUpPT7cpn/L6/i9evFhms1njxo2Ti4v1V7N/HkkPCQmx5KN08yyGunXr5pqbpUqV0saNGy0/ozlz5mjw4MGqWLGiXnjhBcslAGfPntXatWs1aNAgVatWLctY0tPTtXz5cnXv3l01atSwPF+xYkU98cQT+uOPP5SUlGT12iFDhsjV1dWyvGLFCl28eFF9+/a1em9cXV3VsmVLy3tTqlQpubm5afXq1fr7779z3MZ/On/+vMqUKWPTazLkdT+6dOlSlSxZ0mq/5eLiosjIyDytp379+tq+fbv69eunI0eOaPr06erevbsCAgL02WefWfqtWLFCly9f1ujRo+Xh4WE1xq05cuvnLSUlRefOndO9994rwzC0bdu2bOO4nc9Zxn4KgGNxyj2AApWcnJzldYAZevfurVmzZunpp5/W6NGj1alTJ/Xs2VOPPfZYpi+22alcubJNE+DVrl3batlkMqlWrVq5Xo+ZX0ePHlWlSpWs/pgg3Tx1P+P5W/3zy7R08wtUbl9qjx49qtq1a2d6/7JbT165u7tr7NixGjt2rE6fPq01a9Zo+vTpWrBggUqWLKmvvvpKZ8+e1cWLF/Xpp5/q008/zXKcjImejh49qpo1a2YqWGrVqpXl66pUqZKpr6+vr6pWrZqpTZLlfbIlpgz/fO8zioK8FBRnzpzJFE9eL+U4cOCAdu7cme2lDxlxlihRQo8++qi+/vprpaamyt3dXYsWLdL169etCvoDBw7or7/+ynU8W1WpUiXL65JPnDiRqe3ChQuaMGGCvv3220zru3TpUq7rOnDggAzDyPS5zZCXySebN2+uRYsWKS0tTTt27NAPP/ygDz/8UI899pi2b99uNTFkVuupU6eOrly5orNnz8rFxSXP+ZTX9//QoUNycXHJ0wSVt7tfkG7m4tSpUzV16lQdPXpUMTExeu+99/Txxx/L19dXb7/9tuUPAzldnnD27FlduXJFdevWzfTcXXfdJbPZrOPHj1tdAlC9enWrfgcOHJCkbE8zz7im393dXe+++65GjhypgIAA3XPPPXrkkUf01FNPKTAwMNdtNv4xP0le5XU/evToUVWsWFGenp5W/bLbj2WlTp06+vLLL5Wenq64uDj9/PPPmjp1qoYOHarq1asrLCzMMgdCbpeNHDt2TOPGjdNPP/2UKSdy+rzdzufMMIzbvpc9APuhoAdQYE6cOKFLly7l+MWmVKlSWrt2rVatWqUlS5Zo6dKlmj9/vu677z4tX77c6ohOTmPYW3ZfUtLT0/MUkz1kt57b/YJqTxUrVlSfPn306KOPqn79+lqwYIHmzJljOcuiX79+2V5DeuttDG2R3fuR2/t0OzHl572vWLGi1fLnn3+e5QSLWTGbzbr//vv16quvZvl8nTp1LP/v06ePPvnkE/3666/q3r27FixYoHr16qlx48ZW4zVs2FAffPBBluP9848hBaFXr15av369XnnlFYWGhsrLy0tms1kPPvhgns7KMZvNMplM+vXXX7P8udgyd4Gbm5uaN2+u5s2bq06dOho4cKAWLlxo0/2+bcmngnj/7bVfCAoK0qBBg9SjRw/VqFFD8+bN09tvv21zPHn1z/10xvv45ZdfZlmY3zpPx0svvaQuXbpo8eLFWrZsmd544w1NnjxZK1euzPH6+HLlytl8VN+RXF1d1bBhQzVs2FCtWrVSx44dNW/evDxP6peenq77779fFy5c0GuvvaZ69eqpdOnSOnnypAYMGJDj5+12Pmd///13tn8AAFB4KOgBFJgvv/xSkhQeHp5jPxcXF3Xq1EmdOnXSBx98oHfeeUdjx47VqlWrFBYWZvcjABlHhjIYhqGDBw9aFXVlypTJ8lZsR48etTrF1JbYMiY/unz5stVR+r1791qet4egoCDt3LlTZrPZ6uiSvdcj3Txq06hRIx04cEDnzp2Tv7+/vL29lZ6enuuX0KCgIMXFxWU6ynPrbPL2YEtMtsjuZ//PWZ9zmqjsn2rWrKnk5OQ8xdmuXTtVrFhR8+fPV5s2bbRy5cpMs5DXrFlTO3bsUKdOnRxyJO3vv/9WTEyMJkyYoHHjxlna//kZlLJ/P2vWrCnDMFS9enWrP2jkV8atNk+fPm3VnlVs+/fvl6enp+VIe17zKa/vf82aNWU2mxUXF6fQ0FAbtyR/ypQpo5o1a2r37t2SZNm/ZSxnxd/fX56entq3b1+m5/bu3SsXF5dc/1iRcTlRhQoV8pTvNWvW1MiRIzVy5EgdOHBAoaGhev/9963uovJP9erV07x583Tp0iXLmTt5ldf9aFBQkFatWmW5XWqG/O7H/pmfGe/X7t27s/0j+a5du7R//37NnTtXTz31lKU9LzPR2/o5u3Hjho4fP251yRQAx+AaegAFYuXKlZo4caKqV6+uJ598Mtt+Fy5cyNSW8YU245rOjPsV2+te51988YXVdf3fffedTp8+rc6dO1vaatasqT///NPq3sw///xzptv32BLbQw89pPT0dH388cdW7R9++KFMJpPV+vPjoYce0pkzZ6yur75x44Y++ugjeXl5qX379jaPeeDAAR07dixT+8WLF7VhwwaVKVNG/v7+cnV11aOPPqrvv/8+y4Lg7Nmzlv+Hh4fr5MmTVtcxX7t2zeq6UXuwJSZblC5dOstTWMPCwqwe/zxin5NevXppw4YNWrZsWabnLl68qBs3bliWXVxc9Nhjj+l///ufvvzyS924ccPqdPuM8U6ePJnle3r16lXLbOMFJeNI3z+PHkdFRWXqm91nqWfPnnJ1ddWECRMyjWMYhtXt77KyatWqLI9eZ8yZ8c/Txjds2GB1bf/x48f1448/6oEHHpCrq6tN+ZTX97979+5ycXHRW2+9lekoqr3OyNmxY0eW1zsfPXpUcXFxlvfB399f7dq10+zZszN95jNicXV11QMPPKAff/zR6lKlhIQEff3112rTpk2m2+D9U3h4uHx8fPTOO+/o+vXrmZ7PeB+vXLmia9euWT1Xs2ZNeXt753rrxVatWskwDG3ZsiXHflnJ6340PDxc169ft/oZm81mRUdH52k9v//+e5bb/8/8fOCBB+Tt7a3Jkydnej9u/bncupzx/+nTp+cah62fs7i4OF27di3bu7oAKDwcoQeQb7/++qv27t2rGzduKCEhQStXrtSKFSsUFBSkn376KdMEPrd66623tHbtWj388MMKCgpSYmKi/vOf/6hKlSpq06aNpJtf3vz8/DRz5kx5e3urdOnSatmyZaZrMvOqbNmyatOmjQYOHKiEhARFRUWpVq1aVpMaPf300/ruu+/04IMPqlevXjp06JC++uorq0nqbI2tS5cu6tixo8aOHasjR46ocePGWr58uX788Ue99NJLmca+XUOHDtUnn3yiAQMGaMuWLQoODtZ3332ndevWKSoqKtM1/HmxY8cOPfHEE+rcubPatm2rsmXL6uTJk5o7d65OnTqlqKgoy5fJKVOmaNWqVWrZsqWGDBmikJAQXbhwQVu3btVvv/1m+SPOM888o48//lh9+/bV8OHDVbFiRc2bN8+SL/Y8opzXmGzRtGlTzZ8/XyNGjFDz5s3l5eWlLl265Pq677//3nKU71YRERF65ZVX9NNPP+mRRx6x3IosJSVFu3bt0nfffacjR45Ybukm3ZyD4qOPPtL48ePVsGFDy/W9Gfr3768FCxbo2Wef1apVq9S6dWulp6dr7969WrBggZYtW2Y5ElgQfHx81K5dO02dOlXXr19X5cqVtXz5csXHx2fqmzER3dixY9WnTx+VLFlSXbp0Uc2aNfX2229rzJgxOnLkiLp37y5vb2/Fx8frhx9+0NChQzVq1KhsY3jhhRd05coV9ejRQ/Xq1VNaWprWr1+v+fPnKzg4ONOEmg0aNFB4eLjVbeukm7eVy5DXfMrr+1+rVi2NHTtWEydOVNu2bdWzZ0+5u7srNjZWlSpV0uTJk/P9s1ixYoXGjx+vrl276p577pGXl5cOHz6s2bNnKzU1VW+++aal77///W+1adNGd999t+U67iNHjmjJkiXavn27JOntt9/WihUr1KZNGz3//PMqUaKEPvnkE6Wmpmrq1Km5xuPj46MZM2aof//+uvvuu9WnTx/5+/vr2LFjWrJkiVq3bq2PP/5Y+/fvV6dOndSrVy+FhISoRIkS+uGHH5SQkGA1SWZW2rRpo3Llyum3337L8lr9mJiYTMWxdPMPLHndj3bv3l0tWrTQyJEjdfDgQdWrV08//fSTJQdy24+9++672rJli3r27Gk5S2zr1q364osvVLZsWcstEH18fPThhx/q6aefVvPmzfXEE0+oTJky2rFjh65cuaK5c+eqXr16qlmzpkaNGqWTJ0/Kx8dH33//fZ4uO7D1c7ZixQp5enrq/vvvz3VsAAWs4CfSB3CnyrgdUcbDzc3NCAwMNO6//35j+vTpVrdHy/DPW+XExMQY3bp1MypVqmS4ubkZlSpVMvr27Zvptl0//vijERISYpQoUcLqVkDt27c36tevn2V82d227ptvvjHGjBljVKhQwShVqpTx8MMPZ3n7tffff9+oXLmy4e7ubrRu3drYvHlzpjFziu2ft60zjJu3anr55ZeNSpUqGSVLljRq165tTJs2zeq2Q4Zx8/ZZkZGRmWLK663FEhISjIEDBxrly5c33NzcjIYNG2Z5+6S83rYuISHBmDJlitG+fXujYsWKRokSJYwyZcoY9913n/Hdd99l2T8yMtKoWrWqUbJkSSMwMNDo1KmT8emnn1r1O3z4sPHwww8bpUqVMvz9/Y2RI0dabgN1663osvs5Zxd/Vu9fXmLK7tZyGbe5uvU9TE5ONp544gnDz8/PciuwnGSMnd0j49Zmly9fNsaMGWPUqlXLcHNzM8qXL2/ce++9xnvvvZfpVmtms9moWrVqlrdDzJCWlma8++67Rv369Q13d3ejTJkyRtOmTY0JEyYYly5dsnov83rbuqxy0zCyvpXliRMnjB49ehh+fn6Gr6+v8fjjjxunTp0yJBnjx4+3ev3EiRONypUrGy4uLpluYff9998bbdq0MUqXLm2ULl3aqFevnhEZGWns27cvx3h//fVXY9CgQUa9evUMLy8vw83NzahVq5bxwgsvZLplW8a2ffXVV0bt2rUNd3d3o0mTJla3UMyQ1xzP6/tvGIYxe/Zso0mTJpZ+7du3N1asWGF5Prt8z2q/9E+HDx82xo0bZ9xzzz1GhQoVjBIlShj+/v7Gww8/bKxcuTJT/927d1t+bh4eHkbdunWNN954w6rP1q1bjfDwcMPLy8vw9PQ0OnbsaKxfv96qT263N121apURHh5u+Pr6Gh4eHkbNmjWNAQMGWG4deO7cOSMyMtKoV6+eUbp0acPX19do2bKlsWDBghy3N8OLL75o1KpVy6ot4/Oc3ePLL780DCPv+9GzZ88aTzzxhOHt7W34+voaAwYMMNatW2dIMr799tsc41u3bp0RGRlpNGjQwPD19TVKlixpVKtWzRgwYIDVLQEz/PTTT8a9995rlCpVyvDx8TFatGhhfPPNN5bn4+LijLCwMMPLy8soX768MWTIEMstT2+N/Z+/izPk9XPWsmVLo1+/fjluG4DCYTKMIjC7EgAA/19UVJRefvllnThxQpUrV3Z0OChGTCaTIiMjM10WA+d1+PBh1atXT7/++qs6depUaOtdvHixevTooT/++EOtW7cutPUWhu3bt+vuu+/W1q1bC33OBwCZcQ09AMBhrl69arV87do1ffLJJ6pduzbFPIB8q1GjhgYPHqwpU6YU2Dr+uR9LT0/XRx99JB8fH919990Ftl5HmTJlih577DGKeaCI4Bp6AIDD9OzZU9WqVVNoaKguXbqkr776Snv37tW8efMcHRqAO8SMGTMKdPwXXnhBV69eVatWrZSamqpFixZp/fr1eueddwrktqqO9u233zo6BAC3oKAHADhMeHi4Zs2apXnz5ik9PV0hISH69ttvM83WDgBF1X333af3339fP//8s65du6ZatWrpo48+0rBhwxwdGoBigGvoAQAAAABwQnfEEfrg4GD5+PjIxcVFZcqU0apVqxwdEgAAAAAABeqOKOglaf369fLy8nJ0GAAAAAAAFIo7pqC/XWazWadOnZK3t7dMJpOjwwEAAAAA3OEMw9Dly5dVqVIlubjk4+ZzhX3j+39as2aN8cgjjxgVK1Y0JBk//PBDpj4ff/yxERQUZLi7uxstWrQwNm7caPV8cHCwcffddxvNmjUzvvrqK5vWf/z4cUMSDx48ePDgwYMHDx48ePDgUaiP48eP56ecNhx+hD4lJUWNGzfWoEGD1LNnz0zPz58/XyNGjNDMmTPVsmVLRUVFKTw8XPv27VOFChUkSX/88YcqV66s06dPKywsTA0bNlSjRo3ytH5vb29J0vHjx+Xj42O/DbMzs9mss2fPyt/fP39/wUGxRy7BHsgj2Au5BHshl2AP5BHsJbdcSkpKUtWqVS316O1yeEHfuXNnde7cOdvnP/jgAw0ZMkQDBw6UJM2cOVNLlizR7NmzNXr0aElS5cqVJUkVK1bUQw89pK1bt2Zb0Kempio1NdWyfPnyZUmSl5dXkb4G32w26+rVq/Ly8mLngnwhl2AP5BHshVyCvZBLsAfyCPaSWy6ZzWZJyvdl3w4v6HOSlpamLVu2aMyYMZY2FxcXhYWFacOGDZJuHuE3m83y9vZWcnKyVq5cqV69emU75uTJkzVhwoRM7WfPntW1a9fsvxF2YjabdenSJRmGwc4F+UIuwR7II9gLuQR7IZdgD+QR7CW3XMo4sJxfRbqgP3funNLT0xUQEGDVHhAQoL1790qSEhIS1KNHD0lSenq6hgwZoubNm2c75pgxYzRixAjLcsapDv7+/kX+lHuTycTpP8g3cgn2QB7BXsgl2Au5BHsgj2AvueWSh4eHXdZTpAv6vKhRo4Z27NiR5/7u7u5yd3fP1O7i4lLkP7Qmk8kp4kTRRy7BHsgj2Au5BHshl2AP5BHsJadcsld+FemCvnz58nJ1dVVCQoJVe0JCggIDAx0UFQAAAIDixjAM3bhxQ+np6Y4OBU7AZDLJMIwCX0+RLujd3NzUtGlTxcTEqHv37pJunroQExOjYcOG5Wvs6OhoRUdH84EEAAAAkKO0tDSdPn1aV65ccXQocBKGYchkMqlMmTJ2O70+Kw4v6JOTk3Xw4EHLcnx8vLZv366yZcuqWrVqGjFihCIiItSsWTO1aNFCUVFRSklJscx6f7siIyMVGRmppKQk+fr65nczAAAAANyBzGaz4uPj5erqqkqVKsnNzS3fM5PjzmYYhtLS0pSQkKAjR46oTp06BXYJh8ML+s2bN6tjx46W5YwJ6yIiIjRnzhz17t1bZ8+e1bhx43TmzBmFhoZq6dKlmSbKAwAAAAB7S0tLk9lsVtWqVeXp6enocOAkPDw8ZDKZdOLECaWlpRXYUXqHF/QdOnTI9dqCYcOG5fsUewAAAAC4XUySB1sVxpkcxTYro6OjFRISkuMt7gAAAAAAKKqKbUEfGRmpuLg4xcbGOjoUAAAAAABs5vBT7gEAAADAGY1ZtKtQ1ze5Z8NCXV9xMmDAAF28eFGLFy/OU/8jR46oevXq2rZtm0JDQws0tpwU2yP0AAAAAHCnO3PmjF544QXVqFFD7u7uqlq1qrp06aKYmBi7raNDhw566aWX7DaeI0yfPl1z5sxxdBg24wg9AAAAANyBjhw5otatW8vPz0/Tpk1Tw4YNdf36dS1btkyRkZHau3dvocViGIbS09NVokThl6BpaWlyc3PLsY+z3sq82B6hZ1I8AAAAAHey559/XiaTSZs2bdKjjz6qOnXqqH79+hoxYoT+/PNPSdKxY8fUrVs3eXl5ycfHR7169VJCQoJljDfffFOhoaH68ssvFRwcLF9fX/Xp00eXL1+WdPNU9TVr1mj69OkymUwymUw6cuSIVq9eLZPJpF9//VVNmzaVu7u7/vjjD6WmpurFF19UhQoV5OHhoTZt2ljmNTObzapSpYpmzJhhtR3btm2Ti4uLjh49Kkm6ePGinn76afn7+8vHx0f33XefduzYkSnmWbNmqXr16pZbxn333Xdq2LChSpUqpXLlyiksLEwpKSmW7ejevbtljKVLl6pNmzby8/NTuXLl9Mgjj+jQoUN2/gnlX7Et6JkUDwAAAMCd6sKFC1q6dKkiIyNVunTpTM/7+fnJbDarW7duunDhgtasWaMVK1bo8OHD6t27t1XfQ4cOafHixfr555/1888/a82aNZoyZYqkm6eqt2rVSkOGDNHp06d1+vRpVa1a1fLa0aNHa8qUKfrrr7/UqFEjvfrqq/r+++81d+5cbd26VbVq1VJ4eLguXLggFxcX9e3bV19//bXV+ufNm6fWrVsrKChIkvT4448rMTFRv/76q7Zs2aK7775bnTp10oULFyyvOXjwoL7//nstWrRI27dv1+nTp9W3b18NGjRIf/31l1avXq2ePXtmewv1lJQUjRgxQps3b1ZMTIxcXFzUo0cPmc3m2/uBFBBOuQcAAACAO8zBgwdlGIbq1auXbZ+YmBjt2rVL8fHxliL8iy++UP369RUbG2s5m9lsNmvOnDny9vaWJPXv318xMTGaNGmSfH195ebmJk9PTwUGBmZax1tvvaX7779f0s0iecaMGZozZ446d+4sSfrss8+0YsUK/fe//9Urr7yiJ598Uu+//76OHTumatWqyWw269tvv9Xrr78uSfrjjz+0adMmJSYmyt3dXZL03nvvafHixfruu+80dOhQSTdPs//iiy/k7+8vSdq6datu3Lihnj17Wv4w0LBh9pMMPvroo1bLs2fPlr+/v+Li4tSgQYOc3vpCVWyP0AMAAADAnSq7I8+3+uuvv1S1alWrI+ohISHy8/PTX3/9ZWkLDg62FPOSVLFiRSUmJuYpjmbNmln+f+jQIV2/fl2tW7e2tJUsWVItWrSwrC80NFR33XWX5Sj9mjVrlJiYqMcff1yStGPHDiUnJ6tcuXLy8vKyPOLj461OiQ8KCrIU85LUuHFjderUSQ0bNtTjjz+uzz77TH///Xe2cR84cEB9+/ZVjRo15OPjo+DgYEk3L1EoSjhCDwAAAAB3mNq1a8tkMtll4ruSJUtaLZtMpjyfep7V6f65efLJJ/X1119r9OjR+vrrr/Xggw+qXLlykqTk5GRVrFhRq1evzvQ6Pz+/bNfr6uqqFStWaP369Vq+fLk++ugjjR07Vhs3blT16tUzjdWlSxcFBQXps88+U6VKlWQ2m9WgQQOlpaXZvD0FiYLeicxdf0QXlSjJdNtjcO9KAAAA4M5XtmxZhYeHKzo6Wi+++GKmAvfixYu66667dPz4cR0/ftxylD4uLk4XL15USEhIntfl5uam9PT0XPvVrFlTbm5uWrduneW09+vXrys2NtbqtndPPPGEXn/9dW3ZskXfffedZs6caXnu7rvv1pkzZ1SiRAnLUfO8MplMat26tVq3bq1x48YpKChIP/zwg0aMGGHV7/z589q3b58+++wztW3bVtLNU/2LomJb0EdHRys6OjpPiQcAAAAAziY6OlqtW7dWixYt9NZbb6lRo0a6ceOGVqxYoRkzZiguLk4NGzbUk08+qaioKN24cUPPP/+82rdvb3WqfG6Cg4O1ceNGHTlyRF5eXipbtmyW/UqXLq3nnntOr7zyisqWLatq1app6tSpunLligYPHmw13r333qvBgwcrPT1dXbt2tTwXFhamVq1aqXv37po6darq1KmjU6dOacmSJerRo0e2cW/cuFExMTF64IEHVKFCBW3cuFFnz57VXXfdlalvmTJlVK5cOX366aeqWLGijh07ptGjR+f5/ShMxbagj4yMVGRkpJKSkpz2noMAAAAAHKeon/1ao0YNbd26VZMmTdLIkSN1+vRp+fv7q2nTppoxY4ZMJpN+/PFHvfDCC2rXrp1cXFz04IMP6qOPPrJpPaNGjVJERIRCQkJ09epVxcfHZ9t3ypQpMpvN6t+/vy5fvqxmzZpp2bJlKlOmjFW/J598Us8//7yeeuoplSpVytJuMpn0yy+/aOzYsRo4cKDOnj2rwMBAtWvXTgEBAdmu18fHR2vXrlVUVJSSkpIUFBSk999/3zI5361cXFz07bff6sUXX1SDBg1Ut25d/fvf/1aHDh1sel8Kg8nIy2wJd7CMgv7SpUvy8fFxdDjZMpvNmrZ4ky7KU5xyj/wwm81KTExUhQoV5OLCvJi4PeQR7IVcgr2QS7CHrPLo2rVrio+Pt7qfOZAbwzCUnJys48ePq0aNGplyx151KHs7AAAAAACcEAU9AAAAAABOiIIeAAAAAAAnREEPAAAAAIATKrYFfXR0tEJCQtS8eXNHhwIAAAAAgM2KbUEfGRmpuLg4xcbGOjoUAAAAAABsVmwLegAAAAAAnBkFPQAAAAAAToiCHgAAAAAAJ1TC0QEAAAAAgFP63/DCXV+X6YW7vkIwYMAAXbx4UYsXL862T4cOHRQaGqqoqKhCi8tZcIQeAAAAAO5AAwYMUPfu3QttfatXr5bJZMr0eP3117N9zfTp0zVnzpxCi/FOwxF6AAAAAIDd7Nu3Tz4+PpZlLy+vTH3S09NlMpnk6+tbmKHdcYrtEXruQw8AAACgONu9e7c6d+4sLy8vBQQEqH///jp37pykm0fb3dzc9Pvvv1v6T506VRUqVFBCQkKO41aoUEGBgYGWh5eXl+bMmSM/Pz/99NNPCgkJkbu7u44dO5bpLIKUlBQ99dRT8vLyUsWKFfX+++9nGj84OFhvv/22pV9QUJB++uknnT17Vt26dZOXl5caNWqkzZs3W15z/vx59e3bV5UrV5anp6caNmyob775xmrcDh066MUXX9Srr76qsmXLKjAwUG+++eZtvLOFp9gW9NyHHgAAAEBxdfHiRd13331q0qSJNm/erKVLlyohIUG9evWSdLO4femll9S/f39dunRJ27Zt0xtvvKFZs2YpICDgttZ55coVvfvuu5o1a5b27NmjChUqZOrzyiuvaM2aNfrxxx+1fPlyrV69Wlu3bs3U78MPP1Tr1q21bds2Pfzww+rfv7+eeuop9evXT1u3blXNmjX11FNPyTAMSdK1a9fUtGlTLVmyRLt379bQoUPVv39/bdq0yWrcuXPnqnTp0tq4caOmTp2qt956SytWrLit7S0MnHIPAAAAAMXMxx9/rCZNmuidd96xtM2ePVtVq1bV/v37VadOHb399ttasWKFhg4dqt27dysiIkJdu3bNdewqVapYLR89elSSdP36df3nP/9R48aNs3xdcnKy/vvf/+qrr75Sp06dJN0ssP85niQ99NBDeuaZZyRJ48aN04wZM9S8eXM9/vjjkqTXXntNrVq1UkJCggIDA1W5cmWNGjXK8voXXnhBy5Yt04IFC9SiRQtLe6NGjTR+/HhJUu3atfXxxx8rJiZG999/f67b7QgU9AAAAABQzOzYsUOrVq3K8vr2Q4cOqU6dOnJzc9O8efPUqFEjBQUF6cMPP8zT2L///ru8vb0ty2XKlJEkubm5qVGjRtm+7tChQ0pLS1PLli0tbWXLllXdunUz9b11nIwzBho2bJipLTExUYGBgUpPT9c777yjBQsW6OTJk0pLS1Nqaqo8PT2zHVeSKlasqMTExFy32VEo6AEAAACgmElOTlaXLl307rvvZnquYsWKlv+vX79eknThwgVduHBBpUuXznXs6tWry8/PL1N7qVKlZDKZbj/oW5QsWdLy/4wxs2ozm82SpGnTpmn69OmKiopSw4YNVbp0ab300ktKS0vLdtyMcTLGKIqK7TX0AAAAAFBc3X333dqzZ4+Cg4NVq1Ytq0dG0X7o0CG9/PLL+uyzz9SyZUtFREQUaHFbs2ZNlSxZUhs3brS0/f3339q/f3++x163bp26deumfv36qXHjxqpRo4ZdxnU0CnoAAAAAuENdunRJ27dvt3ocP35ckZGRunDhgvr27avY2FgdOnRIy5Yt08CBA5Wenq709HT169dP4eHhGjhwoD7//HPt3Lkzy1nn7cXLy0uDBw/WK6+8opUrV2r37t0aMGCAXFzyX7bWrl1bK1as0Pr16/XXX3/pmWeeyXW2fmfAKfcAAAAAcDu6THd0BLlavXq1mjRpYtU2ePBgzZo1S+vWrdNrr72mBx54QKmpqQoKCtKDDz4oFxcXTZw4UUePHtXPP/8s6eZp+J9++qn69u2rBx54INuJ7fJr2rRplssBvL29NXLkSF26dCnf477++us6fPiwwsPD5enpqaFDh6p79+52GduRTEbGPP7FVFJSknx9fXXp0iX5+Pg4Opxsmc1mTVu8SRflKen2rzuZ3LNh7p1wRzObzUpMTFSFChXs8tdOFE/kEeyFXIK9kEuwh6zy6Nq1a4qPj1f16tXl4eHh4AjhLAzDUHJyso4fP64aNWpkyh171aHs7QAAAAAAcEIU9AAAAAAAOKFiW9BHR0crJCREzZs3d3QoAAAAAADYrNgW9JGRkYqLi1NsbKyjQwEAAAAAwGbFtqAHAAAAgLwq5nOJ4zYURs5Q0AMAAABANkqWLClJunLlioMjgbO5du2apP/LoYLAfegBAAAAIBuurq7y8/NTYmKiJMnT01Mm0+3fRhp3PsMwlJKSosTERJUtW1aurq4Fti4KegAAAADIQWBgoCRZinogN4ZhyN3dXQEBAQW6Hgp6AAAAAMiByWRSxYoVVaFCBV2/ft3R4cAJuLq66vz58wV+NgcFPQAAAADkgaura4GePo07h9lsLpT1MCkeAAAAAABOiIIeAAAAAAAnREEPAAAAAIAToqAHAAAAAMAJUdADAAAAAOCEKOgBAAAAAHBCFPQAAAAAADghCnoAAAAAAJxQsS3oo6OjFRISoubNmzs6FAAAAAAAbFZsC/rIyEjFxcUpNjbW0aEAAAAAAGCzYlvQAwAAAADgzCjoAQAAAABwQhT0AAAAAAA4IQp6AAAAAACcEAU9AAAAAABOiIIeAAAAAAAnREEPAAAAAIAToqAHAAAAAMAJUdADAAAAAOCEKOgBAAAAAHBCFPQAAAAAADghCnoAAAAAAJwQBT0AAAAAAE6Igh4AAAAAACdEQQ8AAAAAgBOioAcAAAAAwAlR0AMAAAAA4IQo6AEAAAAAcEIU9AAAAAAAOKE7pqC/cuWKgoKCNGrUKEeHAgAAAABAgbtjCvpJkybpnnvucXQYAAAAAAAUijuioD9w4ID27t2rzp07OzoUAAAAAAAKhcML+rVr16pLly6qVKmSTCaTFi9enKlPdHS0goOD5eHhoZYtW2rTpk1Wz48aNUqTJ08upIgBAAAAAHC8Eo4OICUlRY0bN9agQYPUs2fPTM/Pnz9fI0aM0MyZM9WyZUtFRUUpPDxc+/btU4UKFfTjjz+qTp06qlOnjtavX5/r+lJTU5WammpZTkpKkiSZzWaZzWb7bZid3YzN+P+P/I6D4sxsNsswDHIB+UIewV7IJdgLuQR7II9gL7nlkr1yzOEFfefOnXM8Vf6DDz7QkCFDNHDgQEnSzJkztWTJEs2ePVujR4/Wn3/+qW+//VYLFy5UcnKyrl+/Lh8fH40bNy7L8SZPnqwJEyZkaj979qyuXbtmn40qAGazWV5Kk2TK1ziJiYn2CQhOy2w269KlSzIMQy4uDj9JB06KPIK9kEuwF3IJ9kAewV5yy6XLly/bZT0OL+hzkpaWpi1btmjMmDGWNhcXF4WFhWnDhg2SbhboGafbz5kzR7t37862mJekMWPGaMSIEZblpKQkVa1aVf7+/vLx8SmgLck/s9msZB3VRZVSfor6ChUq2C8oOCWz2SyTySR/f39+UeG2kUewF3IJ9kIuwR7II9hLbrnk4eFhl/UU6YL+3LlzSk9PV0BAgFV7QECA9u7de1tjuru7y93dPVO7i4uLE3xoTbc8bk/R30YUBpPJ5CQ5j6KMPIK9kEuwF3IJ9kAewV5yyiV75VeRLuhtNWDAAEeHAAAAAABAoSjSf3YqX768XF1dlZCQYNWekJCgwMDAfI0dHR2tkJAQNW/ePF/jAAAAAADgCEW6oHdzc1PTpk0VExNjaTObzYqJiVGrVq3yNXZkZKTi4uIUGxub3zABAAAAACh0Dj/lPjk5WQcPHrQsx8fHa/v27SpbtqyqVaumESNGKCIiQs2aNVOLFi0UFRWllJQUy6z3AAAAAAAURw4v6Ddv3qyOHTtaljNmoI+IiNCcOXPUu3dvnT17VuPGjdOZM2cUGhqqpUuXZpooDwAAAACA4sThBX2HDh1kGEaOfYYNG6Zhw4YVUkQAAAAAABR9Rfoa+oLEpHgAAAAAAGdWbAt6JsUDAAAAADizYlvQAwAAAADgzCjoAQAAAABwQg6fFA95d8/5RXK/ekYmZT+J4OIqrxZiRAAAAAAARym2R+iZFA8AAAAA4MyKbUHPpHgAAAAAAGdWbAt6AAAAAACcGQU9AAAAAABOiIIeAAAAAAAnREEPAAAAAIATKrYFPbPcAwAAAACcWbEt6JnlHgAAAADgzIptQQ8AAAAAgDOjoAcAAAAAwAlR0AMAAAAA4IQo6AEAAAAAcEIU9AAAAAAAOKFiW9Bz2zoAAAAAgDMrtgU9t60DAAAAADizYlvQAwAAAADgzCjoAQAAAABwQhT0AAAAAAA4IQp6AAAAAACcEAU9AAAAAABOiIIeAAAAAAAnVGwLeu5DDwAAAABwZsW2oOc+9AAAAAAAZ1ZsC3oAAAAAAJwZBT0AAAAAAE6Igh4AAAAAACdEQQ8AAAAAgBOioAcAAAAAwAlR0AMAAAAA4IQo6AEAAAAAcEIU9AAAAAAAOCEKegAAAAAAnFCxLeijo6MVEhKi5s2bOzoUAAAAAABsVmwL+sjISMXFxSk2NtbRoQAAAAAAYLNiW9ADAAAAAODMKOgBAAAAAHBCFPQAAAAAADghCnoAAAAAAJwQBT0AAAAAAE6Igh4AAAAAACdEQQ8AAAAAgBOioAcAAAAAwAlR0AMAAAAA4IQo6AEAAAAAcEIU9AAAAAAAOCEKegAAAAAAnBAFPQAAAAAATqjYFvTR0dEKCQlR8+bNHR0KAAAAAAA2K7YFfWRkpOLi4hQbG+voUAAAAAAAsFmxLegBAAAAAHBmFPQAAAAAADghCnoAAAAAAJwQBT0AAAAAAE6Igh4AAAAAACdEQQ8AAAAAgBMqcTsvun79us6cOaMrV67I399fZcuWtXdcAAAAAAAgB3k+Qn/58mXNmDFD7du3l4+Pj4KDg3XXXXfJ399fQUFBGjJkCPd0BwAAAACgkOSpoP/ggw8UHByszz//XGFhYVq8eLG2b9+u/fv3a8OGDRo/frxu3LihBx54QA8++KAOHDhQ0HEDAAAAAFCs5emU+9jYWK1du1b169fP8vkWLVpo0KBBmjlzpj7//HP9/vvvql27tl0DBQAAAAAA/ydPBf0333yTp8Hc3d317LPP5isgAAAAAACQu9ue5f7gwYNatmyZrl69KkkyDMNuQQEAAAAAgJzZXNCfP39eYWFhqlOnjh566CGdPn1akjR48GCNHDnS7gECAAAAAIDMbC7oX375ZZUoUULHjh2Tp6enpb13795aunSpXYMDAAAAAABZs/k+9MuXL9eyZctUpUoVq/batWvr6NGjdgsMAAAAAABkz+Yj9CkpKVZH5jNcuHBB7u7udgkKAAAAAADkzOaCvm3btvriiy8syyaTSWazWVOnTlXHjh3tGhwAAAAAAMiazafcT506VZ06ddLmzZuVlpamV199VXv27NGFCxe0bt26gogRAAAAAAD8g81H6Bs0aKD9+/erTZs26tatm1JSUtSzZ09t27ZNNWvWLIgYAQAAAADAP9h8hF6SfH19NXbsWHvHclsuXryosLAw3bhxQzdu3NDw4cM1ZMgQR4cFAAAAAECBylNBv3PnzjwP2KhRo9sO5nZ4e3tr7dq18vT0VEpKiho0aKCePXuqXLlyhRoHAAAAAACFKU8FfWhoqEwmkwzDyLGfyWRSenq6XQLLK1dXV8us+6mpqTIMI9c4AQAAAABwdnm6hj4+Pl6HDx9WfHx8jo/Dhw/bHMDatWvVpUsXVapUSSaTSYsXL87UJzo6WsHBwfLw8FDLli21adMmq+cvXryoxo0bq0qVKnrllVdUvnx5m+MAAAAAAMCZ5OkIfVBQUIEFkJKSosaNG2vQoEHq2bNnpufnz5+vESNGaObMmWrZsqWioqIUHh6uffv2qUKFCpIkPz8/7dixQwkJCerZs6cee+wxBQQEFFjMAAAAAAA42m1NiidJcXFxOnbsmNLS0qzau3btatM4nTt3VufOnbN9/oMPPtCQIUM0cOBASdLMmTO1ZMkSzZ49W6NHj7bqGxAQoMaNG+v333/XY489luV4qampSk1NtSwnJSVJksxms8xms02xFyaz2SxDkiFTLj1zvtygKG8jCofZbJZhGOQC8oU8gr2QS7AXcgn2QB7BXnLLJXvlmM0F/eHDh9WjRw/t2rXL6rp6k+lmoWnPa+jT0tK0ZcsWjRkzxtLm4uKisLAwbdiwQZKUkJAgT09PeXt769KlS1q7dq2ee+65bMecPHmyJkyYkKn97Nmzunbtmt1itzez2awbbmVkkkk5Fe1+upLjOImJiXaODM7GbDbr0qVLMgxDLi4237kSkEQewX7IJdgLuQR7II9gL7nl0uXLl+2yHpsL+uHDh6t69eqKiYlR9erVtWnTJp0/f14jR47Ue++9Z5egMpw7d07p6emZTp8PCAjQ3r17JUlHjx7V0KFDLZPhvfDCC2rYsGG2Y44ZM0YjRoywLCclJalq1ary9/eXj4+PXeO3J7PZrP1pf8vtaoJMORT0F+WZ4zgZlymg+DKbzTKZTPL39+cXFW4beQR7IZdgL+QS7IE8gr3klkseHh52WY/NBf2GDRu0cuVKlS9fXi4uLnJxcVGbNm00efJkvfjii9q2bZtdAsurFi1aaPv27Xnu7+7uLnd390ztGdtSlJkkmWTkWNArl1Pyi/o2onCYTCanyHkUbeQR7IVcgr2QS7AH8gj2klMu2Su/bB4lPT1d3t7ekqTy5cvr1KlTkm5OnLdv3z67BJWhfPnycnV1VUJCglV7QkKCAgMD7bouAAAAAACcic0FfYMGDbRjxw5JUsuWLTV16lStW7dOb731lmrUqGHX4Nzc3NS0aVPFxMRY2sxms2JiYtSqVat8jR0dHa2QkBA1b948v2ECAAAAAFDobD7l/vXXX1dKSook6a233tIjjzyitm3bqly5cpo/f77NASQnJ+vgwYOW5fj4eG3fvl1ly5ZVtWrVNGLECEVERKhZs2Zq0aKFoqKilJKSYpn1/nZFRkYqMjJSSUlJ8vX1zddYAAAAAAAUNpsL+vDwcMv/a9Wqpb179+rChQsqU6aMZaZ7W2zevFkdO3a0LGdMWBcREaE5c+aod+/eOnv2rMaNG6czZ84oNDRUS5cu5T7zAAAAAIBizeaC/tKlS0pPT1fZsmUtbWXLltWFCxdUokQJm2eK79Chg+XWd9kZNmyYhg0bZmuoAAAAAADcsWy+hr5Pnz769ttvM7UvWLBAffr0sUtQhYFr6AEAAAAAzszmgn7jxo1Wp8hn6NChgzZu3GiXoApDZGSk4uLiFBsb6+hQAAAAAACwmc0FfWpqqm7cuJGp/fr167p69apdggIAAAAAADmzuaBv0aKFPv3000ztM2fOVNOmTe0SFAAAAAAAyJnNk+K9/fbbCgsL044dO9SpUydJUkxMjGJjY7V8+XK7BwgAAAAAADKz+Qh969attWHDBlWtWlULFizQ//73P9WqVUs7d+5U27ZtCyLGAsGkeAAAAAAAZ2bzEXpJCg0N1bx58+wdS6GKjIxUZGSkkpKS5Ovr6+hwAAAAAACwic1H6Ldu3apdu3ZZln/88Ud1795d//rXv5SWlmbX4AAAAAAAQNZsLuifeeYZ7d+/X5J0+PBh9e7dW56enlq4cKFeffVVuwcIAAAAAAAys7mg379/v0JDQyVJCxcuVPv27fX1119rzpw5+v777+0dHwAAAAAAyILNBb1hGDKbzZKk3377TQ899JAkqWrVqjp37px9owMAAAAAAFmyuaBv1qyZ3n77bX355Zdas2aNHn74YUlSfHy8AgIC7B5gQWGWewAAAACAM7O5oI+KitLWrVs1bNgwjR07VrVq1ZIkfffdd7r33nvtHmBBiYyMVFxcnGJjYx0dCgAAAAAANrP5tnWNGjWymuU+w7Rp0+Tq6mqXoAAAAAAAQM5u6z70WfHw8LDXUAAAAAAAIBc2n3IPAAAAAAAcj4IeAAAAAAAnREEPAAAAAIATKrYFPbetAwAAAAA4M5snxRsxYkSW7SaTSR4eHqpVq5a6deumsmXL5ju4ghQZGanIyEglJSXJ19fX0eEAAAAAAGATmwv6bdu2aevWrUpPT1fdunUlSfv375erq6vq1aun//znPxo5cqT++OMPhYSE2D1gAAAAAABwG6fcd+vWTWFhYTp16pS2bNmiLVu26MSJE7r//vvVt29fnTx5Uu3atdPLL79cEPECAAAAAADdRkE/bdo0TZw4UT4+PpY2X19fvfnmm5o6dao8PT01btw4bdmyxa6BAgAAAACA/2NzQX/p0iUlJiZmaj979qySkpIkSX5+fkpLS8t/dAAAAAAAIEu3dcr9oEGD9MMPP+jEiRM6ceKEfvjhBw0ePFjdu3eXJG3atEl16tSxd6wAAAAAAOD/s3lSvE8++UQvv/yy+vTpoxs3btwcpEQJRURE6MMPP5Qk1atXT7NmzbJvpAAAAAAAwMLmgt7Ly0ufffaZPvzwQx0+fFiSVKNGDXl5eVn6hIaG2i1AAAAAAACQmc0FfQYvLy/LveZvLeadRXR0tKKjo5Wenu7oUAAAAAAAsJnN19CbzWa99dZb8vX1VVBQkIKCguTn56eJEyfKbDYXRIwFIjIyUnFxcYqNjXV0KAAAAAAA2MzmI/Rjx47Vf//7X02ZMkWtW7eWJP3xxx968803de3aNU2aNMnuQQIAAAAAAGs2F/Rz587VrFmz1LVrV0tbo0aNVLlyZT3//PMU9AAAAAAAFAKbT7m/cOGC6tWrl6m9Xr16unDhgl2CAgAAAAAAObO5oG/cuLE+/vjjTO0ff/yxGjdubJegAAAAAABAzmw+5X7q1Kl6+OGH9dtvv6lVq1aSpA0bNuj48eP65Zdf7B4gAAAAAADIzOYj9O3bt9f+/fvVo0cPXbx4URcvXlTPnj21b98+tW3btiBiBAAAAAAA/3Bb96GvVKkSk98BAAAAAOBAeSrod+7cmecBGzVqdNvBAAAAAACAvMlTQR8aGiqTySTDMHLsZzKZlJ6ebpfAAAAAAABA9vJU0MfHxxd0HIUuOjpa0dHR/AECAAAAAOCU8lTQBwUFFXQchS4yMlKRkZFKSkqSr6+vo8MBgDvamEW78j3G5J4N7RAJAADAnSNPs9z/+eefeR7wypUr2rNnz20HBAAAAAAAcpengr5///4KDw/XwoULlZKSkmWfuLg4/etf/1LNmjW1ZcsWuwYJAAAAAACs5emU+7i4OM2YMUOvv/66nnjiCdWpU0eVKlWSh4eH/v77b+3du1fJycnq0aOHli9froYNOS0SAAAAAICClKeCvmTJknrxxRf14osvavPmzfrjjz909OhRXb16VY0bN9bLL7+sjh07qmzZsgUdLwAAAAAAUB4L+ls1a9ZMzZo1K4hYAAAAAABAHuXpGnoAAAAAAFC0UNADAAAAAOCEKOgBAAAAAHBCFPQAAAAAADghmyfFO3z4sGrUqFEQsQAAkK0xi3ble4zJPbmtKgAAuHPYfIS+Vq1a6tixo7766itdu3atIGICAAAAAAC5sLmg37p1qxo1aqQRI0YoMDBQzzzzjDZt2lQQsQEAAAAAgGzYfMp9aGiopk+frvfff18//fST5syZozZt2qhOnToaNGiQ+vfvL39//4KIFQDgAFmf6m7IT1d0UYmSTIUdEgAAAJSPSfFKlCihnj17auHChXr33Xd18OBBjRo1SlWrVtVTTz2l06dP2zNOu4uOjlZISIiaN2/u6FAAAAAAALCZzUfoM2zevFmzZ8/Wt99+q9KlS2vUqFEaPHiwTpw4oQkTJqhbt25F+lT8yMhIRUZGKikpSb6+vo4OBwBQCJhYDwAA3ElsLug/+OADff7559q3b58eeughffHFF3rooYfk4nLzYH/16tU1Z84cBQcH2ztWAAAAAADw/9lc0M+YMUODBg3SgAEDVLFixSz7VKhQQf/973/zHRwAAAAAAMiazQX9gQMHcu3j5uamiIiI2woIAAAAAADkzuZJ8T7//HMtXLgwU/vChQs1d+5cuwQFAAAAAAByZnNBP3nyZJUvXz5Te4UKFfTOO+/YJSgAAAAAAJAzmwv6Y8eOqXr16pnag4KCdOzYMbsEBQAAAAAAcmZzQV+hQgXt3LkzU/uOHTtUrlw5uwQFAAAAAAByZvOkeH379tWLL74ob29vtWvXTpK0Zs0aDR8+XH369LF7gAAAFCXcyx4AABQVNhf0EydO1JEjR9SpUyeVKHHz5WazWU899RTX0AMAAAAAUEhsLujd3Nw0f/58TZw4UTt27FCpUqXUsGFDBQUFFUR8AAAAAAAgCzYX9Bnq1KmjOnXq2DMWAAAAAACQRzYX9Onp6ZozZ45iYmKUmJgos9ls9fzKlSvtFhwAAAAAAMiazQX98OHDNWfOHD388MNq0KCBTCZTQcQFAAAAAAByYHNB/+2332rBggV66KGHCiIeAAAAAACQBzbfh97NzU21atUqiFgAAAAAAEAe2VzQjxw5UtOnT5dhGAURDwAAAAAAyAObT7n/448/tGrVKv3666+qX7++SpYsafX8okWL7BYcAAB3ojGLduV7jMk9G9ohEgAA4MxsLuj9/PzUo0ePgogFAAAAAADkkc0F/eeff14QcQAAAAAAABvYfA29JN24cUO//fabPvnkE12+fFmSdOrUKSUnJ9s1uLw4fvy4OnTooJCQEDVq1EgLFy4s9BgAAAAAAChsNh+hP3r0qB588EEdO3ZMqampuv/+++Xt7a13331XqampmjlzZkHEma0SJUooKipKoaGhOnPmjJo2baqHHnpIpUuXLtQ4AAAAAAAoTDYfoR8+fLiaNWumv//+W6VKlbK09+jRQzExMXYNLi8qVqyo0NBQSVJgYKDKly+vCxcuFHocAAAAAAAUJpsL+t9//12vv/663NzcrNqDg4N18uRJmwNYu3atunTpokqVKslkMmnx4sWZ+kRHRys4OFgeHh5q2bKlNm3alOVYW7ZsUXp6uqpWrWpzHAAAAAAAOBObT7k3m81KT0/P1H7ixAl5e3vbHEBKSooaN26sQYMGqWfPnpmenz9/vkaMGKGZM2eqZcuWioqKUnh4uPbt26cKFSpY+l24cEFPPfWUPvvssxzXl5qaqtTUVMtyUlKSZbvMZrPN8RcWs9ksQ5IhUy49jVzHQfFmNptlGAa5ABtktV8xbnnAEe6UzzD7JNgLuQR7II9gL7nlkr1yzOaC/oEHHlBUVJQ+/fRTSZLJZFJycrLGjx+vhx56yOYAOnfurM6dO2f7/AcffKAhQ4Zo4MCBkqSZM2dqyZIlmj17tkaPHi3pZpHevXt3jR49Wvfee2+O65s8ebImTJiQqf3s2bO6du2azfEXFrPZrBtuZWSSSTl9gfbTlRzHSUxMtHNkcDZms1mXLl2SYRhycbmteTFRzGS3X/FSmpTrHxlRUO6U/Tn7JNgLuQR7II9gL7nlUsbk8vllc0H//vvvKzw8XCEhIbp27ZqeeOIJHThwQOXLl9c333xjl6AypKWlacuWLRozZoylzcXFRWFhYdqwYYMkyTAMDRgwQPfdd5/69++f65hjxozRiBEjLMtJSUmqWrWq/P395ePjY9f47clsNmt/2t9yu5ogUw4F/UV55jjOrWc1oHgym80ymUzy9/fnFxXy5KKyKhxvHp2/qFKiqHeMO2V/zj4J9kIuwR7II9hLbrnk4eFhl/XYXNBXqVJFO3bs0LfffqudO3cqOTlZgwcP1pNPPmk1SZ49nDt3Tunp6QoICLBqDwgI0N69eyVJ69at0/z589WoUSPL9fdffvmlGjZsmOWY7u7ucnd3z9Tu4uJS5D+0JkkmGTkW9Ll9sS7q24jCYTKZnCLnUVRkt18x3fJAYbuTPr/sk2Av5BLsgTyCveSUS/bKL5sLeunmreL69etnlwDyq02bNlzjAgAAAAAodmwu6L/44oscn3/qqaduO5h/Kl++vFxdXZWQkGDVnpCQoMDAwHyNHR0drejo6Cwn+AMAoKgbs2hXvseY3DPrs9kAAIBzsLmgHz58uNXy9evXdeXKFbm5ucnT09OuBb2bm5uaNm2qmJgYde/eXdLNaxFiYmI0bNiwfI0dGRmpyMhIJSUlydfX1w7RAgAAAABQeGwu6P/+++9MbQcOHNBzzz2nV155xeYAkpOTdfDgQctyfHy8tm/frrJly6patWoaMWKEIiIi1KxZM7Vo0UJRUVFKSUmxzHoPAAAAAEBxdFvX0P9T7dq1NWXKFPXr188yWV1ebd68WR07drQsZ8xAHxERoTlz5qh37946e/asxo0bpzNnzig0NFRLly7NNFEeAAAAAADFiV0KeunmRHmnTp2y+XUdOnSQYeQ0a7s0bNiwfJ9iDwAAAADAncTmgv6nn36yWjYMQ6dPn9bHH3+s1q1b2y2wgsakeACA4o6J9QAAcG42F/QZk9NlMJlM8vf313333af333/fXnEVOCbFAwAAAAA4M5sLeu75DgAAAACA47k4OgAAAAAAAGA7m4/QZ8xCnxcffPCBrcMDAAAAAIA8sLmg37Ztm7Zt26br16+rbt26kqT9+/fL1dVVd999t6WfyWSyX5QFgEnxAAAAAADOzOaCvkuXLvL29tbcuXNVpkwZSdLff/+tgQMHqm3btho5cqTdgywITIoHAAAAAHBmNl9D//7772vy5MmWYl6SypQpo7ffftupZrkHAAAAAMCZ2VzQJyUl6ezZs5naz549q8uXL9slKAAAAAAAkDObC/oePXpo4MCBWrRokU6cOKETJ07o+++/1+DBg9WzZ8+CiBEAAAAAAPyDzdfQz5w5U6NGjdITTzyh69ev3xykRAkNHjxY06ZNs3uAAAAAAAAgM5sLek9PT/3nP//RtGnTdOjQIUlSzZo1Vbp0absHV5CY5R4AAAAA4MxsLugznD59WqdPn1a7du1UqlQpGYZR5G9VdytmuQcAIP/GLNqVzxEMDb+3gl1iAQCguLH5Gvrz58+rU6dOqlOnjh566CGdPn1akjR48GCnuWUdAAAAAADOzuaC/uWXX1bJkiV17NgxeXp6Wtp79+6tpUuX2jU4AAAAAACQNZtPuV++fLmWLVumKlWqWLXXrl1bR48etVtgAAAAAAAgezYfoU9JSbE6Mp/hwoULcnd3t0tQAAAAAAAgZzYfoW/btq2++OILTZw4UZJkMplkNps1depUdezY0e4BAgCAO9vc9Ud0UYmSbm9y3ck9G9o3IAAAnITNBf3UqVPVqVMnbd68WWlpaXr11Ve1Z88eXbhwQevWrSuIGAsEt60DAAAAADgzm0+5b9Cggfbv3682bdqoW7duSklJUc+ePbVt2zbVrFmzIGIsEJGRkYqLi1NsbKyjQwEAAAAAwGY2HaG/fv26HnzwQc2cOVNjx44tqJgAAAAAAEAubDpCX7JkSe3cubOgYgEAAAAAAHlk8yn3/fr103//+9+CiAUAAAAAAOSRzZPi3bhxQ7Nnz9Zvv/2mpk2bqnTp0lbPf/DBB3YLDgAAAAAAZM3mgn737t26++67JUn79++3es5kur3bzQAAANyuMYt25XsMbn0HAHBGeS7oDx8+rOrVq2vVqlUFGQ8AAAAAAMiDPF9DX7t2bZ09e9ay3Lt3byUkJBRIUIUhOjpaISEhat68uaNDAQAAAADAZnku6A3DsFr+5ZdflJKSYveACgv3oQcAAAAAODObZ7kHAAAAAACOl+eC3mQyZZr0jknwAAAAAABwjDxPimcYhgYMGCB3d3dJ0rVr1/Tss89mum3dokWL7BshAAAAAADIJM8FfUREhNVyv3797B4MAAAAAADImzwX9J9//nlBxgEAAAAAAGzApHgAAAAAADghCnoAAAAAAJwQBT0AAAAAAE6Igh4AAAAAACdUbAv66OhohYSEqHnz5o4OBQAAAAAAmxXbgj4yMlJxcXGKjY11dCgAAAAAANis2Bb0AAAAAAA4Mwp6AAAAAACcEAU9AAAAAABOiIIeAAAAAAAnREEPAAAAAIATKuHoAAAAABxtzKJd+R5jcs+GdogEAIC84wg9AAAAAABOiIIeAAAAAAAnxCn3AAAAdsBp+wCAwsYRegAAAAAAnBAFPQAAAAAAToiCHgAAAAAAJ0RBDwAAAACAE6KgBwAAAADACRXbgj46OlohISFq3ry5o0MBAAAAAMBmxbagj4yMVFxcnGJjYx0dCgAAAAAANiu2BT0AAAAAAM6Mgh4AAAAAACdEQQ8AAAAAgBOioAcAAAAAwAlR0AMAAAAA4IQo6AEAAAAAcEIlHB0AAAAAbhqzaFe+x5jcs6EdIgEAOAOO0AMAAAAA4IQo6AEAAAAAcEIU9AAAAAAAOCEKegAAAAAAnBCT4gEAANxBmFgPAIoPjtADAAAAAOCEKOgBAAAAAHBCFPQAAAAAADghCnoAAAAAAJwQBT0AAAAAAE6Igh4AAAAAACdEQQ8AAAAAgBO6Iwr6Hj16qEyZMnrsscccHQoAAAAAAIXijijohw8fri+++MLRYQAAAAAAUGjuiIK+Q4cO8vb2dnQYAAAAAAAUGocX9GvXrlWXLl1UqVIlmUwmLV68OFOf6OhoBQcHy8PDQy1bttSmTZsKP1AAAAAAAIqQEo4OICUlRY0bN9agQYPUs2fPTM/Pnz9fI0aM0MyZM9WyZUtFRUUpPDxc+/btU4UKFWxeX2pqqlJTUy3LSUlJkiSz2Syz2Xz7G1LAzGazDEmGTLn0NHIdB8Wb2WyWYRjkAmyQ1X7FuOUB5Ae5VBQ54+8Ifr/BHsgj2EtuuWSvHHN4Qd+5c2d17tw52+c/+OADDRkyRAMHDpQkzZw5U0uWLNHs2bM1evRom9c3efJkTZgwIVP72bNnde3aNZvHKyxms1k33MrIJJNy+tLjpys5jpOYmGjnyOBszGazLl26JMMw5OLi8JN04ASy2694KU3K9Y+MQO7IpaLHGb8v8PsN9kAewV5yy6XLly/bZT0OL+hzkpaWpi1btmjMmDGWNhcXF4WFhWnDhg23NeaYMWM0YsQIy3JSUpKqVq0qf39/+fj45DvmgmI2m7U/7W+5XU2QKYeC/qI8cxznds5qwJ3FbDbLZDLJ39+fX1TIk4vK6ov9zSOqF1VKFGLIH3KpKHLG7wv8foM9kEewl9xyycPDwy7rKdIF/blz55Senq6AgACr9oCAAO3du9eyHBYWph07diglJUVVqlTRwoUL1apVqyzHdHd3l7u7e6Z2FxeXIv+hNUkyycixoM/ty1BR30YUDpPJ5BQ5j6Iiu/2K6ZYHkB/kUlHjrL8f+P0GeyCPYC855ZK98qtIF/R59dtvvzk6BAAAAAAAClWR/rNT+fLl5erqqoSEBKv2hIQEBQYG5mvs6OhohYSEqHnz5vkaBwAAAAAARyjSBb2bm5uaNm2qmJgYS5vZbFZMTEy2p9TnVWRkpOLi4hQbG5vfMAEAAAAAKHQOP+U+OTlZBw8etCzHx8dr+/btKlu2rKpVq6YRI0YoIiJCzZo1U4sWLRQVFaWUlBTLrPcAAAAAABRHDi/oN2/erI4dO1qWM2agj4iI0Jw5c9S7d2+dPXtW48aN05kzZxQaGqqlS5dmmigPAAAAAIDixOEFfYcOHWQYOc3aLg0bNkzDhg0rpIgAAAAAACj6ivQ19AWJSfEAAAAAAM6s2Bb0TIoHAAAAAHBmxbagBwAAAADAmVHQAwAAAADghCjoAQAAAABwQg6f5d5RoqOjFR0drfT0dEeHAgAAUKSMWbQr32NM7tnQDpEAAHJSbI/QMykeAAAAAMCZFduCHgAAAAAAZ0ZBDwAAAACAE6KgBwAAAADACVHQAwAAAADghIptQR8dHa2QkBA1b97c0aEAAAAAAGCzYlvQM8s9AAAAAMCZFduCHgAAAAAAZ0ZBDwAAAACAE6KgBwAAAADACVHQAwAAAADghEo4OgBHiY6OVnR0tNLT0x0dCgAAwB1nzKJd+R5jcs+GdogEAO5cxfYIPbPcAwAAAACcWbEt6AEAAAAAcGYU9AAAAAAAOCEKegAAAAAAnBAFPQAAAAAAToiCHgAAAAAAJ0RBDwAAAACAE6KgBwAAAADACZVwdACOEh0drejoaKWnpzs6FAAAABSQMYt25XuMyT0b2iESALC/YnuEPjIyUnFxcYqNjXV0KAAAAAAA2KzYFvQAAAAAADgzCnoAAAAAAJwQBT0AAAAAAE6Igh4AAAAAACdEQQ8AAAAAgBOioAcAAAAAwAlR0AMAAAAA4IQo6AEAAAAAcEIU9AAAAAAAOKESjg7AUaKjoxUdHa309HRHhwIAAIAsjFm0y4behvx0RReVKMnkwDiyNrlnQ6ePAUDRU2yP0EdGRiouLk6xsbGODgUAAAAAAJsV24IeAAAAAABnRkEPAAAAAIAToqAHAAAAAMAJUdADAAAAAOCEKOgBAAAAAHBCFPQAAAAAADghCnoAAAAAAJwQBT0AAAAAAE6Igh4AAAAAACdEQQ8AAAAAgBOioAcAAAAAwAlR0AMAAAAA4IQo6AEAAAAAcEIlHB2Ao0RHRys6Olrp6emODgUAnFr3E1PtNtbiKq/abSwAKErGLNrl6BAA3IGK7RH6yMhIxcXFKTY21tGhAAAAAABgs2Jb0AMAAAAA4Mwo6AEAAAAAcEIU9AAAAAAAOCEKegAAAAAAnBAFPQAAAAAAToiCHgAAAAAAJ0RBDwAAAACAE6KgBwAAAADACVHQAwAAAADghCjoAQAAAABwQhT0AAAAAAA4IQp6AAAAAACcEAU9AAAAAABOiIIeAAAAAAAnREEPAAAAAIAToqAHAAAAAMAJUdADAAAAAOCEKOgBAAAAAHBCFPQAAAAAADihO6Kg//nnn1W3bl3Vrl1bs2bNcnQ4AAAAAAAUuBKODiC/bty4oREjRmjVqlXy9fVV06ZN1aNHD5UrV87RoQEAAAAAUGCc/gj9pk2bVL9+fVWuXFleXl7q3Lmzli9f7uiwAAAAAAAoUA4v6NeuXasuXbqoUqVKMplMWrx4caY+0dHRCg4OloeHh1q2bKlNmzZZnjt16pQqV65sWa5cubJOnjxZGKEDAAAAAOAwDi/oU1JS1LhxY0VHR2f5/Pz58zVixAiNHz9eW7duVePGjRUeHq7ExMRCjhQAAAAAgKLD4dfQd+7cWZ07d872+Q8++EBDhgzRwIEDJUkzZ87UkiVLNHv2bI0ePVqVKlWyOiJ/8uRJtWjRItvxUlNTlZqaallOSkqSJJnNZpnN5vxuToExm80yJBky5dLTyHUcFG9ms1mGYZALsEFW+xXD8sh9v5TfdeHOZtzyAPKDXMoNv/tzx/ck2EtuuWSvHHN4QZ+TtLQ0bdmyRWPGjLG0ubi4KCwsTBs2bJAktWjRQrt379bJkyfl6+urX3/9VW+88Ua2Y06ePFkTJkzI1H727Fldu3bN/hthJ2azWTfcysgkk3L6ReWnKzmOw5kNMJvNunTpkgzDkIuLw0/SgRPIbr/ipTRJJqWWCizwdd3qnvOL8jTWn+V6FupYRVlettMR23gzLpOuu/mpZNpF5acQ4+cN6f/2S8jatMWbcu9UCCLuDXZ0CNnie5JjzV1/JN9jFJX8yi2XLl++bJf1FOmC/ty5c0pPT1dAQIBVe0BAgPbu3StJKlGihN5//3117NhRZrNZr776ao4z3I8ZM0YjRoywLCclJalq1ary9/eXj49PwWyIHZjNZu1P+1tuVxNkyuELz0V55jhOhQoV7B0anIzZbJbJZJK/vz+/qJAnF5XVHwJvHgW7qFJyv3rGjuvKeR8mKc/rK+yxirK8bKcjttH96hkZMsmQkevvt9zw88at+yWK+qKtKH8f5XuSY2X9ncM2RSW/csslDw8Pu6ynSBf0edW1a1d17do1T33d3d3l7u6eqd3FxaXIf2hNkkz//6tPzr2yV9S3EYXDZDI5Rc6jqMhuv3Jzr5SfIizv67q1R17XV7hjFWV5287C38aMuPL2+y330fK6PnuMhaLKdMsDRVVR//7B9yRHyv9ntyj93HLKJXvFWXS2Ngvly5eXq6urEhISrNoTEhIUGGi/UzwBAAAAAHA2Rbqgd3NzU9OmTRUTE2NpM5vNiomJUatWrfI1dnR0tEJCQtS8efP8hgkAAAAAQKFz+Cn3ycnJOnjwoGU5Pj5e27dvV9myZVWtWjWNGDFCERERatasmVq0aKGoqCilpKRYZr2/XZGRkYqMjFRSUpJ8fX3zuxkAAAAAABQqhxf0mzdvVseOHS3LGRPWRUREaM6cOerdu7fOnj2rcePG6cyZMwoNDdXSpUszTZQHAAAAAEBx4vCCvkOHDjKMnCepGTZsmIYNG1ZIEQEAAAAAUPQV6WvoCxLX0AMAAAAAnFmxLegjIyMVFxen2NhYR4cCAAAAAIDNim1BDwAAAACAM6OgBwAAAADACVHQAwAAAADghIptQc+keAAAAAAAZ1ZsC3omxQMAAAAAOLNiW9ADAAAAAODMKOgBAAAAAHBCFPQAAAAAADghCnoAAAAAAJxQsS3omeUeAAAAAODMSjg6AEeJjIxUZGSkLl26JD8/PyUlJTk6pByZzWalXE3VjWtpMsnItl/qleQcxynq24mCZzabdfnyZXl4eMjFpdj+TQ82yHq/YuiaripVZqVcSyvgdVnL6/oKe6yiLC/b6YhtTLmWJkMmpSr332+54eeNW/dLksnRwSAHRfn7KN+THMse+9+ikl+55VJGnIZx+7/7JMlk5HcEJ3fixAlVrVrV0WEAAAAAAIqZ48ePq0qVKrf9+mJf0JvNZp06dUre3t4ymYruX3OTkpJUtWpVHT9+XD4+Po4OB06MXII9kEewF3IJ9kIuwR7II9hLbrlkGIYuX76sSpUq5etskGJ7yn0GFxeXfP1FpLD5+Piwc4FdkEuwB/II9kIuwV7IJdgDeQR7ySmXfH198z0+F4YAAAAAAOCEKOgBAAAAAHBCFPROwt3dXePHj5e7u7ujQ4GTI5dgD+QR7IVcgr2QS7AH8gj2Uli5VOwnxQMAAAAAwBlxhB4AAAAAACdEQQ8AAAAAgBOioAcAAAAAwAlR0AMAAAAA4IQo6B0kOjpawcHB8vDwUMuWLbVp06Yc+y9cuFD16tWTh4eHGjZsqF9++cXqecMwNG7cOFWsWFGlSpVSWFiYDhw4UJCbgCLC3rk0YMAAmUwmq8eDDz5YkJuAIsKWXNqzZ48effRRBQcHy2QyKSoqKt9j4s5g7zx68803M+2T6tWrV4BbgKLCllz67LPP1LZtW5UpU0ZlypRRWFhYpv58Vyq+7J1LfFcqnmzJo0WLFqlZs2by8/NT6dKlFRoaqi+//NKqj732SRT0DjB//nyNGDFC48eP19atW9W4cWOFh4crMTExy/7r169X3759NXjwYG3btk3du3dX9+7dtXv3bkufqVOn6t///rdmzpypjRs3qnTp0goPD9e1a9cKa7PgAAWRS5L04IMP6vTp05bHN998UxibAweyNZeuXLmiGjVqaMqUKQoMDLTLmHB+BZFHklS/fn2rfdIff/xRUJuAIsLWXFq9erX69u2rVatWacOGDapataoeeOABnTx50tKH70rFU0HkksR3peLG1jwqW7asxo4dqw0bNmjnzp0aOHCgBg4cqGXLlln62G2fZKDQtWjRwoiMjLQsp6enG5UqVTImT56cZf9evXoZDz/8sFVby5YtjWeeecYwDMMwm81GYGCgMW3aNMvzFy9eNNzd3Y1vvvmmALYARYW9c8kwDCMiIsLo1q1bgcSLosvWXLpVUFCQ8eGHH9p1TDingsij8ePHG40bN7ZjlHAG+d1/3Lhxw/D29jbmzp1rGAbflYoze+eSYfBdqTiyx3eaJk2aGK+//rphGPbdJ3GEvpClpaVpy5YtCgsLs7S5uLgoLCxMGzZsyPI1GzZssOovSeHh4Zb+8fHxOnPmjFUfX19ftWzZMtsx4fwKIpcyrF69WhUqVFDdunX13HPP6fz58/bfABQZt5NLjhgTRVtB/swPHDigSpUqqUaNGnryySd17Nix/IaLIsweuXTlyhVdv35dZcuWlcR3peKqIHIpA9+Vio/85pFhGIqJidG+ffvUrl07SfbdJ1HQF7Jz584pPT1dAQEBVu0BAQE6c+ZMlq85c+ZMjv0z/rVlTDi/gsgl6eYpZF988YViYmL07rvvas2aNercubPS09PtvxEoEm4nlxwxJoq2gvqZt2zZUnPmzNHSpUs1Y8YMxcfHq23btrp8+XJ+Q0YRZY9ceu2111SpUiXLl2W+KxVPBZFLEt+VipvbzaNLly7Jy8tLbm5uevjhh/XRRx/p/vvvl2TffVIJm3oDuOP16dPH8v+GDRuqUaNGqlmzplavXq1OnTo5MDIAxVHnzp0t/2/UqJFatmypoKAgLViwQIMHD3ZgZCiqpkyZom+//VarV6+Wh4eHo8OBE8sul/iuhLzw9vbW9u3blZycrJiYGI0YMUI1atRQhw4d7LoejtAXsvLly8vV1VUJCQlW7QkJCdlOCBQYGJhj/4x/bRkTzq8gcikrNWrUUPny5XXw4MH8B40i6XZyyRFjomgrrJ+5n5+f6tSpwz7pDpafXHrvvfc0ZcoULV++XI0aNbK0812peCqIXMoK35XubLebRy4uLqpVq5ZCQ0M1cuRIPfbYY5o8ebIk++6TKOgLmZubm5o2baqYmBhLm9lsVkxMjFq1apXla1q1amXVX5JWrFhh6V+9enUFBgZa9UlKStLGjRuzHRPOryByKSsnTpzQ+fPnVbFiRfsEjiLndnLJEWOiaCusn3lycrIOHTrEPukOdru5NHXqVE2cOFFLly5Vs2bNrJ7ju1LxVBC5lBW+K93Z7PX7zWw2KzU1VZKd90k2TaEHu/j2228Nd3d3Y86cOUZcXJwxdOhQw8/Pzzhz5oxhGIbRv39/Y/To0Zb+69atM0qUKGG89957xl9//WWMHz/eKFmypLFr1y5LnylTphh+fn7Gjz/+aOzcudPo1q2bUb16dePq1auFvn0oPPbOpcuXLxujRo0yNmzYYMTHxxu//fabcffddxu1a9c2rl275pBtROGwNZdSU1ONbdu2Gdu2bTMqVqxojBo1yti2bZtx4MCBPI+JO09B5NHIkSON1atXG/Hx8ca6deuMsLAwo3z58kZiYmKhbx8Kj625NGXKFMPNzc347rvvjNOnT1sely9fturDd6Xix965xHel4snWPHrnnXeM5cuXG4cOHTLi4uKM9957zyhRooTx2WefWfrYa59EQe8gH330kVGtWjXDzc3NaNGihfHnn39anmvfvr0RERFh1X/BggVGnTp1DDc3N6N+/frGkiVLrJ43m83GG2+8YQQEBBju7u5Gp06djH379hXGpsDB7JlLV65cMR544AHD39/fKFmypBEUFGQMGTKEAqyYsCWX4uPjDUmZHu3bt8/zmLgz2TuPevfubVSsWNFwc3MzKleubPTu3ds4ePBgIW4RHMWWXAoKCsoyl8aPH2/pw3el4sueucR3peLLljwaO3asUatWLcPDw8MoU6aM0apVK+Pbb7+1Gs9e+ySTYRiGbcf0AQAAAACAo3ENPQAAAAAAToiCHgAAAAAAJ0RBDwAAAACAE6KgBwAAAADACVHQAwAAAADghCjoAQAAAABwQhT0AAAAAAA4IQp6AAAAAACcEAU9AAAodEeOHJHJZNL27dsdHQoAAE6Lgh4AgNs0YMAAde/ePVP76tWrZTKZdPHixTyP1aFDB7300kt2ieuzzz5T48aN5eXlJT8/PzVp0kSTJ0+2y9iOtmPHDnXt2lUVKlSQh4eHgoOD1bt3byUmJjo6NAAACl0JRwcAAADsZ/bs2XrppZf073//W+3bt1dqaqp27typ3bt3Ozq0fDt79qw6deqkRx55RMuWLZOfn5+OHDmin376SSkpKQW23uvXr6tkyZIFNj4AALeLI/QAABSw8+fPq2/fvqpcubI8PT3VsGFDffPNN5bnBwwYoDVr1mj69OkymUwymUw6cuSIJGn37t3q3LmzvLy8FBAQoP79++vcuXPZruunn35Sr169NHjwYNWqVUv169dX3759NWnSJKv1de/eXRMmTJC/v798fHz07LPPKi0tzdLHbDZr8uTJql69ukqVKqXGjRvru+++s1pXbrGZzWZNnTpVtWrVkru7u6pVq2YVhyQdPnxYHTt2lKenpxo3bqwNGzZku23r1q3TpUuXNGvWLDVp0kTVq1dXx44d9eGHH6p69eqWfnv27NEjjzwiHx8feXt7q23btjp06JAlprfeektVqlSRu7u7QkNDtXTpUstrMy4FmD9/vtq3by8PDw/NmzdPkjRr1izddddd8vDwUL169fSf//wn21gBACgMFPQAABSwa9euqWnTplqyZIl2796toUOHqn///tq0aZMkafr06WrVqpWGDBmi06dP6/Tp06pataouXryo++67T02aNNHmzZu1dOlSJSQkqFevXtmuKzAwUH/++aeOHj2aY0wxMTH666+/tHr1an3zzTdatGiRJkyYYHl+8uTJ+uKLLzRz5kzt2bNHL7/8svr166c1a9ZIUp5iGzNmjKZMmaI33nhDcXFx+vrrrxUQEGAVx9ixYzVq1Cht375dderUUd++fXXjxo1st+3GjRv64YcfZBhGln1Onjypdu3ayd3dXStXrtSWLVs0aNAgy5jTp0/X+++/r/fee087d+5UeHi4unbtqgMHDliNM3r0aA0fPlx//fWXwsPDNW/ePI0bN06TJk3SX3/9pXfeeUdvvPGG5s6dm+P7DABAgTIAAMBtiYiIMFxdXY3SpUtbPTw8PAxJxt9//53tax9++GFj5MiRluX27dsbw4cPt+ozceJE44EHHrBqO378uCHJ2LdvX5bjnjp1yrjnnnsMSUadOnWMiIgIY/78+UZ6erpV3GXLljVSUlIsbTNmzDC8vLyM9PR049q1a4anp6exfv16q7EHDx5s9O3bN0+xJSUlGe7u7sZnn32WZZzx8fGGJGPWrFmWtj179hiSjL/++ivL1xiGYfzrX/8ySpQoYZQtW9Z48MEHjalTpxpnzpyxPD9mzBijevXqRlpaWpavr1SpkjFp0iSrtubNmxvPP/+8VVxRUVFWfWrWrGl8/fXXVm0TJ040WrVqlW2sAAAUNK6hBwAgHzp27KgZM2ZYtW3cuFH9+vWzLKenp+udd97RggULdPLkSaWlpSk1NVWenp45jr1jxw6tWrVKXl5emZ47dOiQ6tSpk6m9YsWK2rBhg3bv3q21a9dq/fr1ioiI0KxZs7R06VK5uNw8Oa9x48ZW62/VqpWSk5N1/PhxJScn68qVK7r//vutxk5LS1OTJk3yFNvFixeVmpqqTp065biNjRo1sopdkhITE1WvXr0s+0+aNEkjRozQypUrtXHjRs2cOVPvvPOO1q5dq4YNG2r79u1q27Ztlte8JyUl6dSpU2rdurVVe+vWrbVjxw6rtmbNmln+n5KSokOHDmnw4MEaMmSIpf3GjRvy9fXNcfsAAChIFPQAAORD6dKlVatWLau2EydOWC1PmzZN06dPV1RUlBo2bKjSpUvrpZdesrpmPSvJycnq0qWL3n333UzPZRS/2WnQoIEaNGig559/Xs8++6zatm2rNWvWqGPHjrluU3JysiRpyZIlqly5stVz7u7ueYrt8OHDua5HklXhbTKZJN28zj0n5cqV0+OPP67HH39c77zzjpo0aaL33ntPc+fOValSpfK03tyULl3a8v+M9+Ozzz5Ty5Ytrfq5urraZX0AANwOCnoAAArYunXr1K1bN8tRe7PZrP379yskJMTSx83NTenp6Vavu/vuu/X9998rODhYJUrc/q/sjPXcOhP8jh07dPXqVUsB/Oeff8rLy0tVq1ZV2bJl5e7urmPHjql9+/ZZjplbbLVr11apUqUUExOjp59++rZjz42bm5tq1qxp2bZGjRpp7ty5Wc5M7+Pjo0qVKmndunVW27Vu3Tq1aNEi23UEBASoUqVKOnz4sJ588smC2RAAAG4Dk+IBAFDAateurRUrVmj9+vX666+/9MwzzyghIcGqT3BwsDZu3KgjR47o3LlzMpvNioyM1IULF9S3b1/Fxsbq0KFDWrZsmQYOHJip+M/w3HPPaeLEiVq3bp2OHj2qP//8U0899ZT8/f3VqlUrS7+0tDQNHjxYcXFx+uWXXzR+/HgNGzZMLi4u8vb21qhRo/Tyyy9r7ty5OnTokLZu3aqPPvrIMglcbrF5eHjotdde06uvvqovvvhChw4d0p9//qn//ve/t/0+/vzzz+rXr59+/vln7d+/X/v27dN7772nX375Rd26dZMkDRs2TElJSerTp482b96sAwcO6Msvv9S+ffskSa+88oreffddzZ8/X/v27dPo0aO1fft2DR8+PMd1T5gwQZMnT9a///1v7d+/X7t27dLnn3+uDz744La3BwCA/OIIPQAABez111/X4cOHFR4eLk9PTw0dOlTdu3fXpUuXLH1GjRqliIgIhYSE6OrVq4qPj1dwcLDWrVun1157TQ888IBSU1MVFBSkBx980HIt/D+FhYVp9uzZmjFjhs6fP6/y5curVatWiomJUbly5Sz9OnXqpNq1a6tdu3ZKTU1V37599eabb1qenzhxovz9/TV58mQdPnxYfn5+uvvuu/Wvf/1LkixHunOK7Y033lCJEiU0btw4nTp1ShUrVtSzzz572+9jSEiIPD09NXLkSB0/flzu7u6qXbu2Zs2apf79+0u6eTr+ypUr9corr6h9+/ZydXVVaGio5br5F198UZcuXdLIkSOVmJiokJAQ/fTTT6pdu3aO63766afl6empadOm6ZVXXlHp0qXVsGFDvfTSS7e9PQAA5JfJMLK57wsAALgjDRgwQBcvXtTixYsdHQoAAMgHTrkHAAAAAMAJUdADAAAAAOCEOOUeAAAAAAAnxBF6AAAAAACcEAU9AAAAAABOiIIeAAAAAAAnREEPAAAAAIAToqAHAAAAAMAJUdADAAAAAOCEKOgBAAAAAHBCFPQAAAAAADih/wfo4nKCt5TejgAAAABJRU5ErkJggg==", + "image/png": 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", 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", + "image/png": 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//tp4eXkZLy8v06hRI/Pss8+aFStWmNTU1Gzf248//mhvO3jwoPH39zedO3e2t+V0POV0+//+++/GarWazp07m4yMDIe+NpvN/jwqKirLdouPj3f6eX+lCxcumCpVqhhJJioqyvTu3du8/fbb5sSJE1n6Nm/e3BQqVCjL7/fltXTq1Mn4+vqavXv32tuOHj1qChUqZJo3b25vy/z/RNOmTU16erq9PSkpyYSGhpp+/fo5rOP48eOmcOHC9vYzZ84YSeaVV1656vsD4H4c7g7gmhITEyXJvmcsN/Tt29f+3MvLS7fccouMMerTp4+9PTQ0VFWqVNG+ffvctt7L996eOXNGCQkJatasmbZs2XJdywsJCVHbtm21ePFih8OoFy1apNtvv11ly5aVJC1ZskQ2m01dunTRqVOn7I/IyEhVqlQp2z2arggODr7qVd4z94B99tln132RNT8/P6eH/manZ8+eDuPo/vvvV4kSJfTll19e1/pz6ssvv5SXl5eeeuoph/ahQ4fKGKOvvvrKob1169YOR0vUrl1bISEh1xx7X375pSIjI9W9e3d7m4+Pj5566imdO3dOa9eudbl2i8WiFStW6KWXXlKRIkX04YcfasCAAYqKilLXrl3t56QbY/Tpp5+qffv2MsY4jKuYmBglJCTYx/Xy5ctVqlQpdejQwb4ef3//bI+6CA4Otu8hlqQqVaooNDRU1apVc9hrl/k8czu5UlOm2NhYhz3UmUdJuPP33pmPP/5YzZo1U5EiRRzqbN26tTIyMuyHXbds2VLFixfXokWL7POeOXNGK1euVNeuXR2WV61aNVWtWtVheS1btpSk6/4d79ixo1auXJnlkXnI+uUu/3zL3AN/++23S1KOPuNu9HPqrrvu0saNG9WhQwdt375dU6ZMUUxMjEqVKqXPP/88S/9GjRqpQYMG9tdly5ZVx44dtWLFCmVkZLg0nnK6/ZcuXSqbzaYxY8bIanX8WnzlHu/q1avbx6N06WiDnPw/KSAgQN999539ZzR//nz16dNHJUqU0JNPPmk//P7kyZNat26dHnnkEfv/K66sJSMjQ19//bU6derkcGpViRIl9OCDD+rbb7+1/z87U79+/RyunbJy5UqdPXtW3bt3d9g2Xl5eatiwoX3bBAQEyNfXV2vWrNGZM2eu+h4BuBeHuwO4pszz5dx1Wy9nrvxCUrhwYfn7+9sPvby8/fLzKW/UF198oZdeeknbtm1zOE/xRu4n3rVrVy1dulQbN25U48aNtXfvXm3evNnhllG///67jDGqVKmS02W448Jn586dy3Le55V1zp07V3379tXw4cPVqlUr3Xvvvbr//vuzfFnNTqlSpVy6SNyV79disahixYq5fn7jwYMHVbJkySx/aKpWrZp9+uWuHI+SVKRIkWt+UT148KAqVaqUZftlt56c8vPz08iRIzVy5EgdO3ZMa9eu1cyZM7V48WL5+Pjogw8+0MmTJ3X27Fn961//yvYuC5kX6zp48KAqVKiQZZxXrFjR6XylS5fO0rdw4cIqU6ZMljZJ9u3kSk2Zrtz2RYoUcVjm1Rw/fjxLPTk9jeL333/XTz/9lO1pB5l1ent767777tPChQuVkpIiPz8/LVmyRGlpaQ4h/ffff9fOnTuvuTxXlS5dWq1bt87S/scff2RpO336tMaPH6+PPvooy/oSEhKuuS53fE7deuutWrJkiVJTU7V9+3b9+9//1vTp03X//fdr27ZtDhdPdLaeypUr68KFCzp58qSsVmuOx1NOt//evXtltVpzdBHH6/1ckC6NxSlTpmjKlCk6ePCg4uLiNHXqVL3xxhsqXLiwXnrpJXvYv9qpASdPntSFCxdUpUqVLNOqVasmm82mw4cPOxx+X65cOYd+v//+uyTZ/2Bxpcz/5/v5+enll1/W0KFDFRERodtvv13t2rVTz549FRkZec33DOD6EdIBXFNISIhKliypHTt25Kh/dgE3IyMj23mcXSE9u6umX76H+nrWlem///2vOnTooObNm+uf//ynSpQoIR8fH73zzjtauHDhNefPTvv27RUYGKjFixercePGWrx4saxWqx544AF7H5vNJovFoq+++srp+7zRc2z/+OMPJSQkZBu6pEt7SdatW6fVq1dr2bJlWr58uRYtWqSWLVvq66+/ztFV6105jzynrvYzzasr6edk7OWXEiVKqFu3brrvvvtUo0YNLV68WPPnz7cfDfHwww+rV69eTue9/JZ8rshue1xrO11PTTey7a+8qOU777zj9CKEzthsNt1111169tlnnU6vXLmy/Xm3bt301ltv6auvvlKnTp20ePFiVa1aVXXq1HFYXq1atTRt2jSny7vyDxy5oUuXLtqwYYOeeeYZ1a1bV8HBwbLZbGrTpk2Ojp5x5+eUr6+vbr31Vt16662qXLmyYmNj9fHHH+foFoSX1yPlbDzlxvZ31+dCVFSUHnnkEXXu3Fnly5fXggUL9NJLL7lcT05d+TmduR3ff/99p2H78uteDB48WO3bt9fSpUu1YsUKjR49WpMmTdKqVatUr169XKsZKOgI6QBypF27dvrXv/6ljRs3qlGjRlftm7nn68pbQ13vXsTcWtenn34qf39/rVixwuE+5++8806Wvq7sWQ8KClK7du308ccfa9q0aVq0aJGaNWvmcB/cChUqyBijcuXKOXz5d5f3339fkhQTE3PVflarVa1atVKrVq00bdo0TZw4USNHjtTq1avVunXrGzqiwJnMPTiZjDHas2ePQ1ArUqSI09uKHTx40OHwTldqi4qK0jfffKOkpCSHvem7du2yT3eHqKgo/fTTT7LZbA570929HunSXszatWvr999/16lTpxQWFqZChQopIyPD6Z7WK+v89ddfZYxx2I6XX0XdHVypyRXZ/exXrlzp8PpqF/O6UoUKFXTu3Lkc1dm8eXOVKFFCixYtUtOmTbVq1aosV9+uUKGCtm/frlatWrn99ygnzpw5o7i4OI0fP15jxoyxt1/5Oyhlvz1z63Mq8y4Xx44dc2h3Vtvu3bsVGBho3yOe0/GU0+1foUIF2Ww2/frrr6pbt66L7+TGFClSRBUqVLD/ATzz8+1qfxAPCwtTYGCgfvvttyzTdu3aJavVes0/QGSeyhMeHp6j8V6hQgUNHTpUQ4cO1e+//666devq1Vdfdbh7CAD34px0ADny7LPPKigoSH379tWJEyeyTN+7d69mzpwp6dKe9+LFi2e5dc4///lPt9eV+WXj8nVlZGRkeyjk5by8vGSxWBz2uh84cMDpVZeDgoJcuh91165ddfToUc2dO1fbt293OAxWku699155eXlp/PjxWfbCGGNu6JD+VatW6cUXX1S5cuX00EMPZdvv9OnTWdoyv6RmHvqfeT9dd92L+7333nM4beKTTz7RsWPH1LZtW3tbhQoVtGnTJod7B3/xxRdZbvnkSm3/+Mc/lJGRoTfeeMOhffr06bJYLA7rvxH/+Mc/dPz4cYfzldPT0/X6668rODhYLVq0cHmZv//+uw4dOpSl/ezZs9q4caOKFCmisLAweXl56b777tOnn37q9Ev+yZMn7c9jYmJ05MgRh/OCk5OTNWfOHJfruxpXanJFUFCQ08O1W7du7fBwdrvI7HTp0kUbN27UihUrskw7e/as0tPT7a+tVqvuv/9+/ec//9H777+v9PT0LL/jXbp00ZEjR5xu04sXL9qvsp1bMvf6Xvn5cvlpN5my+1260c+p1atXO93LnHkNiisP2d64caPDufKHDx/WZ599prvvvlteXl4ujaecbv9OnTrJarXqhRdeyHJ0gbuOnNm+fbvTK/IfPHhQv/76q307hIWFqXnz5po3b16W3/nMWry8vHT33Xfrs88+czhN6MSJE1q4cKGaNm2a5ZZuV4qJiVFISIgmTpyotLS0LNMzt+OFCxeUnJzsMK1ChQoqVKhQnt1GECio2JMOIEcqVKighQsX2m+V1rNnT9WsWVOpqanasGGD/TZTmfr27avJkyerb9++uuWWW7Ru3Trt3r3b7XXVqFFDt99+u0aMGKHTp0+raNGi+uijjxy+UGfnnnvu0bRp09SmTRs9+OCDio+P16xZs1SxYkX99NNPDn0bNGigb775RtOmTVPJkiVVrly5LLe5udw//vEPFSpUSMOGDbN/sbxchQoV9NJLL2nEiBE6cOCAOnXqpEKFCmn//v3697//rf79+zvcbzk7X331lXbt2qX09HSdOHFCq1at0sqVKxUVFaXPP/9c/v7+2c77wgsvaN26dbrnnnsUFRWl+Ph4/fOf/1Tp0qXVtGlTe52hoaGaPXu2ChUqpKCgIDVs2DDLOY45VbRoUTVt2lSxsbE6ceKEZsyYoYoVKzpcsKxv37765JNP1KZNG3Xp0kV79+7VBx98kOW2d67U1r59e915550aOXKkDhw4oDp16ujrr7/WZ599psGDB2d7Sz1X9e/fX2+99ZZ69+6tzZs3Kzo6Wp988onWr1+vGTNmXNfFF7dv364HH3xQbdu2VbNmzVS0aFEdOXJE7777ro4ePaoZM2bYA9nkyZO1evVqNWzYUP369VP16tV1+vRpbdmyRd988439DzOPPvqo3njjDXXv3l2DBg1SiRIltGDBAvt4ceee35zW5IoGDRpo0aJFGjJkiG699VYFBwerffv215zv008/tR/VcLlevXrpmWee0eeff6527drZb6t1/vx5/fzzz/rkk0904MABh2tkdO3aVa+//rrGjh2rWrVq2a87kKlHjx5avHixHnvsMa1evVpNmjRRRkaGdu3apcWLF2vFihX2Pcq5ISQkRM2bN9eUKVOUlpamUqVK6euvv9b+/fuz9M28WNvIkSPVrVs3+fj4qH379jf8OfXkk0/qwoUL6ty5s6pWrWr//8WiRYsUHR2d5aKTNWvWVExMjMMt2KRLt0jLlNPxlNPtX7FiRY0cOVIvvviimjVrpnvvvVd+fn764YcfVLJkSU2aNOmGfxYrV67U2LFj1aFDB91+++0KDg7Wvn37NG/ePKWkpGjcuHH2vq+99pqaNm2q+vXrq3///ipXrpwOHDigZcuWadu2bZKkl156SStXrlTTpk31xBNPyNvbW2+99ZZSUlI0ZcqUa9YTEhKiN998Uz169FD9+vXVrVs3hYWF6dChQ1q2bJmaNGmiN954Q7t371arVq3UpUsXVa9eXd7e3vr3v/+tEydOOFxIEkAuyJuLyAP4u9i9e7fp16+fiY6ONr6+vqZQoUKmSZMm5vXXXzfJycn2fhcuXDB9+vQxhQsXNoUKFTJdunQx8fHx2d6C7crbgPXq1csEBQVlWX+LFi1MjRo1HNr27t1rWrdubfz8/ExERIR5/vnnzcqVK3N0C7a3337bVKpUyfj5+ZmqVauad955J8utyoy5dMup5s2bm4CAACPJfruk7G4BZ4wxDz30kJFkWrdune32/PTTT03Tpk1NUFCQCQoKMlWrVjUDBgwwv/32W7bzXL7ezIevr6+JjIw0d911l5k5c6bDrb4yXfm+4uLiTMeOHU3JkiWNr6+vKVmypOnevXuWW1B99tlnpnr16sbb29vh9lrOfhaZsrsF24cffmhGjBhhwsPDTUBAgLnnnnuc3krs1VdfNaVKlTJ+fn6mSZMm5scff8yyzKvV5uxnnZSUZJ5++mlTsmRJ4+PjYypVqmReeeUVh1sbGXPpVlADBgzIUlNOb5N14sQJExsba4oXL258fX1NrVq1nN4mLqe3YDtx4oSZPHmyadGihSlRooTx9vY2RYoUMS1btjSffPKJ0/4DBgwwZcqUMT4+PiYyMtK0atXK/Otf/3Lot2/fPnPPPfeYgIAAExYWZoYOHWo+/fRTI8nhtmrZ/Zyzq9/Z9stJTdndJs3ZLR3PnTtnHnzwQRMaGmq/rdXVZC47u0fmbbqSkpLMiBEjTMWKFY2vr68pXry4ady4sZk6dWqW24bZbDZTpkwZp7f2y5SammpefvllU6NGDePn52eKFCliGjRoYMaPH28SEhIctmVOb8HmbGwa4/y2jH/88Yfp3LmzCQ0NNYULFzYPPPCAOXr0aJbPYWMu3YKuVKlSxmq1ZvlMu97Pqa+++so88sgjpmrVqiY4ONj4+vqaihUrmieffDLL7ccy39sHH3xg/0yuV6+ew2d4ppyO8Zxuf2OMmTdvnqlXr569X4sWLczKlSvt07Mb784+l660b98+M2bMGHP77beb8PBw4+3tbcLCwsw999xjVq1alaX/jh077D83f39/U6VKFTN69GiHPlu2bDExMTEmODjYBAYGmjvvvNNs2LDBoc+1btW5evVqExMTYwoXLmz8/f1NhQoVTO/eve23wTt16pQZMGCAqVq1qgkKCjKFCxc2DRs2NIsXL77q+wVw4yzGeMBVcAAAQL6bMWOGnn76af3xxx8qVapUfpeDAsRisWjAgAFZTkkBgIKIc9IBACiALl686PA6OTlZb731lipVqkRABwAgH3FOOgAABdC9996rsmXLqm7dukpISNAHH3ygXbt2acGCBfldGgAABRohHQCAAigmJkZz587VggULlJGRoerVq+ujjz7KcpVyAACQtzgnHQAAAAAAD8E56QAAAAAAeAhCOgAAAAAAHqLAnZNus9l09OhRFSpUSBaLJb/LAQAAAAD8zRljlJSUpJIlS8pqvfq+8gIX0o8ePaoyZcrkdxkAAAAAgALm8OHDKl269FX7FLiQXqhQIUmXNk5ISEg+V3N1NptNJ0+eVFhY2DX/2oKCg3EBZxgXcIZxAWcYF3CGcYHsMDbcIzExUWXKlLHn0aspcCE98xD3kJCQmyKkJycnKyQkhF8I2DEu4AzjAs4wLuAM4wLOMC6QHcaGe+XklGu2MgAAAAAAHoKQDgAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAACAh8jXkL5u3Tq1b99eJUuWlMVi0dKlS685z5o1a1S/fn35+fmpYsWKmj9/fq7XCQAAAABAXsjXkH7+/HnVqVNHs2bNylH//fv365577tGdd96pbdu2afDgwerbt69WrFiRy5UCAAAAAJD7vPNz5W3btlXbtm1z3H/27NkqV66cXn31VUlStWrV9O2332r69OmKiYnJrTIBAPjbshnJmEv/ZlzjecZfry9/ZBjJZrvytZHNGNlsf81rM8qwmUvTbebSsmy61Oev+TOn2/6aP3Odl/5j/+cvRjL2Z7rixf97XTaTw/yOC7s02///49BoZHE+j3HomO3yr5gt63qvfG6fwXLZtMvr+n/7ldPstV62fRzb/z9HeppV3j5nr9LPsbLLt0NhrzR1Czns9D1lecNX2wCu9nc2jf5uY5FUODlZFn//XFsHPNQ1xpVFUujNMDaqVZNKl87vKtwiX0O6qzZu3KjWrVs7tMXExGjw4MHZzpOSkqKUlBT768TEREmSzWaTzWbLlTrdxWazyRjj8XUibzEu4AzjIu8YI6XZpNQMKd126Xm6TUrPuOy57VIIzQy1l4fbDNtfITbDpowM21/P/wqxmWHWlhlUjT0A29vN5YHWogwjpRsp3ViUYbMoXRalGYsyjEXpsirDhMqmdBlJGbLIyCKbLJeeG4uMxVkwu1EWyWngg0dJv77ZwtPPSnu2uLUU5D+LpID8LgIeySLJw+O5JMlWqtSlv/p6KFe+o91UIf348eOKiIhwaIuIiFBiYqIuXryogICsHy2TJk3S+PHjs7SfPHlSycnJuVarO9hsNiUkJMgYI6uVa/zhEsYFnPk7jov/78W9FEQv//dSUHVsT0j1klX6K7ReCqmXgvJl/eyvLUq/chmZwfqvcO0wry1zHotSjFU2t5wt5uWGZdwgcjQA4G8iKSlJF+Pj87uMbCUlJeW4700V0q/HiBEjNGTIEPvrxMRElSlTRmFhYQoJCcnHyq7NZrPJYrEoLCzsb/OlGzeOcQFnbnRc2IyUlvH/PcSpGZdep/61VzgzMGcefWyMZJOT9svaLqRJyRlSarpFKX8tMyVDOpIo+Xr9FaYvC8RZn5Mgr4e3yZC3yZCXsclbl/61ylx6OHlukZHVGHnJdum5JKtsslr0/76Z7RYjr8vbLJfavRxe/9Xnsvm9LEYWi+ztXn8dPO1l+atf5nIyn1skq0WyWCSL5f975S0ymY3/f8N/PbXY2xynWS7ra7FYHCbbjyKwWC4tO3M9l7U7rMdi+f/sVyzLPi2zXssV7Zd3vXLZly0ya8cr358ca7j0Tq4yLes6jTE6l5SkQoUKXfZes6wy6zL+emmVkc23TZb6nXbO7vW1+l9rnvzof73Lcjbd3dvDDWw2m06fPq2iRYv+vb9f5PJ2/Duy2Wz68/RpFStaVFYvD/gDczYK+fqqkI9PfpeRLX8XThe4qUJ6ZGSkTpw44dB24sQJhYSEON2LLkl+fn7y8/PL0m61Wj36AyjdJv1ySjpxOkAh6VZZLM5rdceZSTlaRg465WQ57liXu87GyslpXW5blxtWlNnFGCkpKVCFkrOOi7z8WV5zGTfp9r3RPnm2fa98bZPOnQ9WcJJVlr8+24yRktOlkxekhGTJ23pZ+P4rgGcG8nTPPTos31wKuhnylu2v4GuTlzLkb0tTgEmVr0mXt/lrmjLk81c49jYZ8vkrHHvJyMsqWa0WWa0WeXlZZLVY5GW1yPpXu1fmtMv7/TXdy2r9fz8v66V/va3/7+d96f9l3t5WeXlZ5O1llcXbS3/NJJt06YtVWJisXt6Xvpxarc7/5YtrgWGz2WTi41U8PNyjvwshj9lssqWkyFq4MOMCjmw2meRkWUNCGBs3wJVtd1OF9EaNGunLL790aFu5cqUaNWqUTxXlngU/SxsOWyWF5ncp8DhWSYXzuwh4HKukQvldhMsCbCl/hV+bPRB7GZvDXmAvh5DsbNr/29MtVoWlJzrMk9nX29jkbbHJ2yp5WS3y9roUjH2skpe39a/Xlr8C719h18tL8va+9K+z597ekrffpde+vpKPz6VH5rT8/jJjsykjJUUKDs7/WgAAQI7ka0g/d+6c9uzZY3+9f/9+bdu2TUWLFlXZsmU1YsQIHTlyRO+9954k6bHHHtMbb7yhZ599Vo888ohWrVqlxYsXa9myZfn1FnLNvjP5XQGAvysfq+TrZS79a7HJVzb5KF2+Jl2+tnT52NLkm5Eq3/QU+aSnyCct5a/DoI0s5q9/JYc2S+Zh05f18TPpCrClyM+ky8+kyfevf/1MugJtKfLK7pgCL6//h93LH97ejgE487WPj+QXcFm/EtkHa4IqAADwcPka0n/88Ufdeeed9teZ54736tVL8+fP17Fjx3To0CH79HLlymnZsmV6+umnNXPmTJUuXVpz5879W99+zdtidF81k+3h7nY5PEoxJ91ytKgcdHLXgZM5qtlNK8vL93696zI2mxITExUSEiKL1XpTbuecri9Pt3VOlpOH48zVdRljU8LZBBUOKSRLWpqUkiJLSor80pIVkHZB/snnVTglQb4Xk2S9eFG6cEHKyLiu+rOVuTc5yyPg0r9+fpf+vTJgZ/bLfO7B57sBAADktnwN6XfccYfDPUyvNH/+fKfzbN26NRer8hBpaZJ85GNL051bPr/xkJGL99XM1XXlZd3uXF8u1m2MUYbNJi+r9f8X/GGb37x1u2lZRpf+gGNNS7vxejJ5eUkBAf9/+PtfCtH+/pcewcFSYOD/X3vfVGdQAQAAeCS+UXmq5GTJ4iMZI8vp0/ldDTyIRfziIiuX7krt738pdAcGXvo3KOjS88zXmf/6+nIxMQAAgDzGd31PZYz9G7fx8srhocHuPBY5H76YU3+OlmWMkc1mkzWn48IN68zXZeX1+m7SsWMkZWRkyCsoSJbMwH156M58HhDA4eQAAAAejJB+EzCxsfZbKgHGZtPJ+HiFh4czLmBnbDadYlwAAADc9PgmBwAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CHyPaTPmjVL0dHR8vf3V8OGDfX9999ftf+MGTNUpUoVBQQEqEyZMnr66aeVnJycR9UCAAAAAJB78jWkL1q0SEOGDNHYsWO1ZcsW1alTRzExMYqPj3faf+HChRo+fLjGjh2rnTt36u2339aiRYv0/PPP53HlAAAAAAC4X76G9GnTpqlfv36KjY1V9erVNXv2bAUGBmrevHlO+2/YsEFNmjTRgw8+qOjoaN19993q3r37Nfe+AwAAAABwM/DOrxWnpqZq8+bNGjFihL3NarWqdevW2rhxo9N5GjdurA8++EDff/+9brvtNu3bt09ffvmlevToke16UlJSlJKSYn+dmJgoSbLZbLLZbG56N7nrZqkTecNms8kYw7iAA8YFnGFcwBnGBZxhXCA7jA33cGX75VtIP3XqlDIyMhQREeHQHhERoV27djmd58EHH9SpU6fUtGlTGWOUnp6uxx577KqHu0+aNEnjx4/P0n7y5EmPPpfdmAD78/j4eFmt+X75AHgIm82mhIQEGWMYF7BjXMAZxgWcYVzAGcYFssPYcI+kpKQc9823kH491qxZo4kTJ+qf//ynGjZsqD179mjQoEF68cUXNXr0aKfzjBgxQkOGDLG/TkxMVJkyZRQWFqaQkJC8Kt1lFss5+/Pw8HB+IWBns9lksVgUFhbGuIAd4wLOMC7gDOMCzjAukB3Ghnv4+/vnuG++hfTixYvLy8tLJ06ccGg/ceKEIiMjnc4zevRo9ejRQ3379pUk1apVS+fPn1f//v01cuRIp4PGz89Pfn5+WdqtVutNM8huplqRNywWC+MCWTAu4AzjAs4wLuAM4wLZYWzcOFe2Xb5tZV9fXzVo0EBxcXH2NpvNpri4ODVq1MjpPBcuXMjy5ry8vCRJxpjcKxYAAAAAgDyQr4e7DxkyRL169dItt9yi2267TTNmzND58+cVGxsrSerZs6dKlSqlSZMmSZLat2+vadOmqV69evbD3UePHq327dvbwzoAAAAAADerfA3pXbt21cmTJzVmzBgdP35cdevW1fLly+0Xkzt06JDDnvNRo0bJYrFo1KhROnLkiMLCwtS+fXtNmDAhv94CAAAAAABuk+8Xjhs4cKAGDhzodNqaNWscXnt7e2vs2LEaO3ZsHlQGAAAAAEDe4sx/AAAAAAA8BCEdAAAAAAAPQUgHAAAAAMBDENIBAAAAAPAQhHQAAAAAADwEIR0AAAAAAA9BSAcAAAAAwEMQ0gEAAAAA8BCEdAAAAAAAPAQhHQAAAAAAD0FIBwAAAADAQxDSAQAAAADwEIR0AAAAAAA8BCEdAAAAAAAPQUgHAAAAAMBDENIBAAAAAPAQhHQAAAAAADwEIR0AAAAAAA9BSAcAAAAAwEMQ0gEAAAAA8BCEdAAAAAAAPAQhHQAAAAAAD0FIBwAAAADAQxDSAQAAAADwEIR0AAAAAAA8BCEdAAAAAAAPQUgHAAAAAMBDENIBAAAAAPAQhHQAAAAAADwEIR0AAAAAAA9BSAcAAAAAwEMQ0gEAAAAA8BCEdAAAAAAAPAQhHQAAAAAAD0FIBwAAAADAQxDSAQAAAADwEIR0AAAAAAA8BCEdAAAAAAAPQUgHAAAAAMBDENIBAAAAAPAQhHQAAAAAADyEyyH9/PnzuVEHAAAAAAAFnsshPSIiQo888oi+/fbb3KgHAAAAAIACy+WQ/sEHH+j06dNq2bKlKleurMmTJ+vo0aO5URsAAAAAAAWKyyG9U6dOWrp0qY4cOaLHHntMCxcuVFRUlNq1a6clS5YoPT09N+oEAAAAAOBv77ovHBcWFqYhQ4bop59+0rRp0/TNN9/o/vvvV8mSJTVmzBhduHDBnXUCAAAAAPC35329M544cULvvvuu5s+fr4MHD+r+++9Xnz599Mcff+jll1/Wpk2b9PXXX7uzVgAAAAAA/tZcDulLlizRO++8oxUrVqh69ep64okn9PDDDys0NNTep3HjxqpWrZo76wQAAAAA4G/P5ZAeGxurbt26af369br11lud9ilZsqRGjhx5w8UBAAAAAFCQuBzSjx07psDAwKv2CQgI0NixY6+7KAAAAAAACiKXLxxXqFAhxcfHZ2n/888/5eXl5ZaiAAAAAAAoiFwO6cYYp+0pKSny9fW94YIAAAAAACiocny4+2uvvSZJslgsmjt3roKDg+3TMjIytG7dOlWtWtX9FQIAAAAAUEDkOKRPnz5d0qU96bNnz3Y4tN3X11fR0dGaPXu2+ysEAAAAAKCAyHFI379/vyTpzjvv1JIlS1SkSJFcKwoAAAAAgILI5au7r169OjfqAAAAAACgwMtRSB8yZIhefPFFBQUFaciQIVftO23aNLcUBgAAAABAQZOjkL5161alpaXZn2fHYrG4pyoAAAAAAAqgHIX0yw9x53B3AAAAAAByh8v3SQcAAAAAALkjR3vS77333hwvcMmSJdddDAAAAAAABVmOQnrhwoVzuw4AAAAAAAq8HIX0d955J7frAAAAAACgwOOcdAAAAAAAPESO9qTXr19fcXFxKlKkiOrVq3fVW61t2bLFbcUBAAAAAFCQ5Cikd+zYUX5+fpKkTp065WY9AAAAAAAUWDkK6WPHjnX6HAAAAAAAuE+OQrozP/74o3bu3ClJql69uho0aOC2ogAAAAAAKIhcDul//PGHunfvrvXr1ys0NFSSdPbsWTVu3FgfffSRSpcu7e4aAQAAAAAoEFy+unvfvn2VlpamnTt36vTp0zp9+rR27twpm82mvn375kaNAAAAAAAUCC7vSV+7dq02bNigKlWq2NuqVKmi119/Xc2aNXNrcQAAAAAAFCQu70kvU6aM0tLSsrRnZGSoZMmSbikKAAAAAICCyOWQ/sorr+jJJ5/Ujz/+aG/78ccfNWjQIE2dOtWtxQEAAAAAUJDkKKQXKVJERYsWVdGiRRUbG6tt27apYcOG8vPzk5+fnxo2bKgtW7bokUcecbmAWbNmKTo6Wv7+/mrYsKG+//77q/Y/e/asBgwYoBIlSsjPz0+VK1fWl19+6fJ6AQAAAADwNDk6J33GjBm5svJFixZpyJAhmj17tho2bKgZM2YoJiZGv/32m8LDw7P0T01N1V133aXw8HB98sknKlWqlA4ePGi/yjwAAAAAADezHIX0Xr165crKp02bpn79+ik2NlaSNHv2bC1btkzz5s3T8OHDs/SfN2+eTp8+rQ0bNsjHx0eSFB0dnSu1AQAAAACQ11y+uvvlkpOTlZqa6tAWEhKSo3lTU1O1efNmjRgxwt5mtVrVunVrbdy40ek8n3/+uRo1aqQBAwbos88+U1hYmB588EE999xz8vLycjpPSkqKUlJS7K8TExMlSTabTTabLUe15rebpU7kDZvNJmMM4wIOGBdwhnEBZxgXcIZxgewwNtzDle3nckg/f/68nnvuOS1evFh//vlnlukZGRk5Ws6pU6eUkZGhiIgIh/aIiAjt2rXL6Tz79u3TqlWr9NBDD+nLL7/Unj179MQTTygtLU1jx451Os+kSZM0fvz4LO0nT55UcnJyjmrND8YE2J/Hx8fLanX5Gn/4m7LZbEpISJAxhnEBO8YFnGFcwBnGBZxhXCA7jA33SEpKynFfl0P6s88+q9WrV+vNN99Ujx49NGvWLB05ckRvvfWWJk+e7OriXGKz2RQeHq5//etf8vLyUoMGDXTkyBG98sor2Yb0ESNGaMiQIfbXiYmJKlOmjMLCwnK81z8/WCzn7M/Dw8P5hYCdzWaTxWJRWFgY4wJ2jAs4w7iAM4wLOMO4QHYYG+7h7++f474uh/T//Oc/eu+993THHXcoNjZWzZo1U8WKFRUVFaUFCxbooYceytFyihcvLi8vL504ccKh/cSJE4qMjHQ6T4kSJeTj4+NwaHu1atV0/PhxpaamytfXN8s8mVegv5LVar1pBtnNVCvyhsViYVwgC8YFnGFcwBnGBZxhXCA7jI0b58q2c3krnz59WuXLl5d06fzz06dPS5KaNm2qdevW5Xg5vr6+atCggeLi4uxtNptNcXFxatSokdN5mjRpoj179jgcz797926VKFHCaUAHAAAAAOBm4nJIL1++vPbv3y9Jqlq1qhYvXizp0h52V2+FNmTIEM2ZM0fvvvuudu7cqccff1znz5+3X+29Z8+eDheWe/zxx3X69GkNGjRIu3fv1rJlyzRx4kQNGDDA1bcBAAAAAIDHcflw99jYWG3fvl0tWrTQ8OHD1b59e73xxhtKS0vTtGnTXFpW165ddfLkSY0ZM0bHjx9X3bp1tXz5cvvF5A4dOuRwWECZMmW0YsUKPf3006pdu7ZKlSqlQYMG6bnnnnP1bQAAAAAA4HEsxhhzIws4cOCAtmzZoooVK6p27druqivXJCYmqnDhwkpISPDoC8eN/TxRx60hCrClaFo7H87/gJ3NZlN8fDwXFIQDxgWcYVzAGcYFnGFcIDuMDfdwJYfe0H3SJSk6OlrR0dE3uhgAAAAAAAq86/pTSFxcnNq1a6cKFSqoQoUKateunb755ht31wYAAAAAQIHickj/5z//qTZt2qhQoUIaNGiQBg0apJCQEP3jH//QrFmzcqNGAAAAAAAKBJcPd584caKmT5+ugQMH2tueeuopNWnShCutAwAAAABwA1zek3727Fm1adMmS/vdd9+thIQEtxQFAAAAAEBB5HJI79Chg/79739naf/ss8/Url07txQFAAAAAEBBlKPD3V977TX78+rVq2vChAlas2aNGjVqJEnatGmT1q9fr6FDh+ZOlQAAAAAAFAA5CunTp093eF2kSBH9+uuv+vXXX+1toaGhmjdvnkaNGuXeCgEAAAAAKCByFNL379+f23UAAAAAAFDgXdd90jMZY2SMcVctAAAAAAAUaNcV0t977z3VqlVLAQEBCggIUO3atfX++++7uzYAAAAAAAoUl++TPm3aNI0ePVoDBw5UkyZNJEnffvutHnvsMZ06dUpPP/2024sEAAAAAKAgcDmkv/7663rzzTfVs2dPe1uHDh1Uo0YNjRs3jpAOAAAAAMB1cvlw92PHjqlx48ZZ2hs3bqxjx465pSgAAAAAAAoil0N6xYoVtXjx4iztixYtUqVKldxSFAAAAAAABZHLh7uPHz9eXbt21bp16+znpK9fv15xcXFOwzsAAAAAAMgZl/ek33ffffr+++9VvHhxLV26VEuXLlXx4sX1/fffq3PnzrlRIwAAAAAABYJLe9LT0tL06KOPavTo0frggw9yqyYAAAAAAAokl/ak+/j46NNPP82tWgAAAAAAKNBcPty9U6dOWrp0aS6UAgAAAABAwebyheMqVaqkF154QevXr1eDBg0UFBTkMP2pp55yW3EAAAAAABQkLof0t99+W6Ghodq8ebM2b97sMM1isRDSAQAAAAC4Ti6H9P379+dGHQAAAAAAFHguhfRNmzbpP//5j1JTU9WqVSu1adMmt+oCAAAAAKDAyXFI/+STT9S1a1cFBATIx8dH06ZN08svv6xhw4blZn0AAAAAABQYOb66+6RJk9SvXz8lJCTozJkzeumllzRx4sTcrA0AAAAAgAIlxyH9t99+07Bhw+Tl5SVJGjp0qJKSkhQfH59rxQEAAAAAUJDkOKRfuHBBISEh9te+vr7y9/fXuXPncqUwAAAAAAAKGpcuHDd37lwFBwfbX6enp2v+/PkqXry4vY1bsAEAAAAAcH1yHNLLli2rOXPmOLRFRkbq/ffft7/mPukAAAAAAFy/HIf0AwcO5GIZAAAAAAAgx+ekAwAAAACA3EVIBwAAAADAQxDSAQAAAADwEIR0AAAAAAA8BCEdAAAAAAAPcV0hfe/evRo1apS6d++u+Ph4SdJXX32lX375xa3FAQAAAABQkLgc0teuXatatWrpu+++05IlS3Tu3DlJ0vbt2zV27Fi3FwgAAAAAQEHhckgfPny4XnrpJa1cuVK+vr729pYtW2rTpk1uLQ4AAAAAgILE5ZD+888/q3Pnzlnaw8PDderUKbcUBQAAAABAQeRySA8NDdWxY8eytG/dulWlSpVyS1EAAAAAABRELof0bt266bnnntPx48dlsVhks9m0fv16DRs2TD179syNGgEAAAAAKBBcDukTJ05U1apVVaZMGZ07d07Vq1dX8+bN1bhxY40aNSo3agQAAAAAoEDwdnUGX19fzZkzR6NHj9aOHTt07tw51atXT5UqVcqN+gAAAAAAKDBcDunffvutmjZtqrJly6ps2bK5URMAAAAAAAWSy4e7t2zZUuXKldPzzz+vX3/9NTdqAgAAAACgQHI5pB89elRDhw7V2rVrVbNmTdWtW1evvPKK/vjjj9yoDwAAAACAAsPlkF68eHENHDhQ69ev1969e/XAAw/o3XffVXR0tFq2bJkbNQIAAAAAUCC4HNIvV65cOQ0fPlyTJ09WrVq1tHbtWnfVBQAAAABAgXPdIX39+vV64oknVKJECT344IOqWbOmli1b5s7aAAAAAAAoUFy+uvuIESP00Ucf6ejRo7rrrrs0c+ZMdezYUYGBgblRHwAAAAAABYbLIX3dunV65pln1KVLFxUvXjw3agIAAAAAoEByOaSvX78+N+oAAAAAAKDAy1FI//zzz9W2bVv5+Pjo888/v2rfDh06uKUwAAAAAAAKmhyF9E6dOun48eMKDw9Xp06dsu1nsViUkZHhrtoAAAAAAChQchTSbTab0+cAAAAAAMB9XL4F23vvvaeUlJQs7ampqXrvvffcUhQAAAAAAAWRyyE9NjZWCQkJWdqTkpIUGxvrlqIAAAAAACiIXA7pxhhZLJYs7X/88YcKFy7slqIAAAAAACiIcnwLtnr16slischisahVq1by9v7/rBkZGdq/f7/atGmTK0UCAAAAAFAQ5DikZ17Vfdu2bYqJiVFwcLB9mq+vr6Kjo3Xfffe5vUAAAAAAAAqKHIf0sWPHSpKio6PVtWtX+fv751pRAAAAAAAURDkO6Zl69eqVG3UAAAAAAFDguRzSMzIyNH36dC1evFiHDh1Samqqw/TTp0+7rTgAAAAAAAoSl6/uPn78eE2bNk1du3ZVQkKChgwZonvvvVdWq1Xjxo3LhRIBAAAAACgYXA7pCxYs0Jw5czR06FB5e3ure/fumjt3rsaMGaNNmzblRo0AAAAAABQILof048ePq1atWpKk4OBgJSQkSJLatWunZcuWubc6AAAAAAAKEJdDeunSpXXs2DFJUoUKFfT1119Lkn744Qf5+fm5tzoAAAAAAAoQl0N6586dFRcXJ0l68sknNXr0aFWqVEk9e/bUI4884vYCAQAAAAAoKFy+uvvkyZPtz7t27aqyZctq48aNqlSpktq3b+/W4gAAAAAAKEhcDulXatSokRo1auSOWgAAAAAAKNByFNI///zzHC+wQ4cO110MAAAAAAAFWY5CeqdOnXK0MIvFooyMjBupBwAAAACAAitHF46z2Ww5elxvQJ81a5aio6Pl7++vhg0b6vvvv8/RfB999JEsFkuO/4gAAAAAAIAnc/nq7u62aNEiDRkyRGPHjtWWLVtUp04dxcTEKD4+/qrzHThwQMOGDVOzZs3yqFIAAAAAAHKXyxeOe+GFF646fcyYMS4tb9q0aerXr59iY2MlSbNnz9ayZcs0b948DR8+3Ok8GRkZeuihhzR+/Hj997//1dmzZ11aJwAAAAAAnsjlkP7vf//b4XVaWpr2798vb29vVahQwaWQnpqaqs2bN2vEiBH2NqvVqtatW2vjxo3ZzvfCCy8oPDxcffr00X//+9+rriMlJUUpKSn214mJiZL+fwj/zeBmqRN5w2azyRjDuIADxgWcYVzAGcYFnGFcIDuMDfdwZfu5HNK3bt2apS0xMVG9e/dW586dXVrWqVOnlJGRoYiICIf2iIgI7dq1y+k83377rd5++21t27YtR+uYNGmSxo8fn6X95MmTSk5OdqnevGRMgP15fHy8rNZ8PzMBHsJmsykhIUHGGMYF7BgXcIZxAWcYF3CGcYHsMDbcIykpKcd9b/g+6ZIUEhKi8ePHq3379urRo4c7FulUUlKSevTooTlz5qh48eI5mmfEiBEaMmSI/XViYqLKlCmjsLAwhYSE5FapN8xiOWd/Hh4ezi8E7Gw2mywWi8LCwhgXsGNcwBnGBZxhXMAZxgWyw9hwD39//xz3dUtIl6SEhAQlJCS4NE/x4sXl5eWlEydOOLSfOHFCkZGRWfrv3btXBw4cUPv27e1tmYcNeHt767ffflOFChUc5vHz85Ofn1+WZVmt1ptmkN1MtSJvWCwWxgWyYFzAGcYFnGFcwBnGBbLD2Lhxrmw7l0P6a6+95vDaGKNjx47p/fffV9u2bV1alq+vrxo0aKC4uDj7bdRsNpvi4uI0cODALP2rVq2qn3/+2aFt1KhRSkpK0syZM1WmTBnX3gwAAAAAAB7E5ZA+ffp0h9dWq1VhYWHq1auXwwXgcmrIkCHq1auXbrnlFt12222aMWOGzp8/b7/ae8+ePVWqVClNmjRJ/v7+qlmzpsP8oaGhkpSlHQAAAACAm43LIX3//v1uLaBr1646efKkxowZo+PHj6tu3bpavny5/WJyhw4d4rAKAAAAAECB4LZz0m/EwIEDnR7eLklr1qy56rzz5893f0EAAAAAAOQDl0N6cnKyXn/9da1evVrx8fFZ7ve2ZcsWtxUHAAAAAEBB4nJI79Onj77++mvdf//9uu2222SxWHKjLgAAAAAAChyXQ/oXX3yhL7/8Uk2aNMmNegAAAAAAKLBcviJbqVKlVKhQodyoBQAAAACAAs3lkP7qq6/queee08GDB3OjHgAAAAAACiyXD3e/5ZZblJycrPLlyyswMFA+Pj4O00+fPu224gAAAAAAKEhcDundu3fXkSNHNHHiREVERHDhOAAAAAAA3MTlkL5hwwZt3LhRderUyY16AAAAAAAosFw+J71q1aq6ePFibtQCAAAAAECB5nJInzx5soYOHao1a9bozz//VGJiosMDAAAAAABcH5cPd2/Tpo0kqVWrVg7txhhZLBZlZGS4pzIAAAAAAAoYl0P66tWrc6MOAAAAAAAKPJdDeosWLXKjDgAAAAAACjyXQ/q6deuuOr158+bXXQwAAAAAAAWZyyH9jjvuyNJ2+b3SOScdAAAAAIDr4/LV3c+cOePwiI+P1/Lly3Xrrbfq66+/zo0aAQAAAAAoEFzek164cOEsbXfddZd8fX01ZMgQbd682S2FAQAAAABQ0Li8Jz07ERER+u2339y1OAAAAAAAChyX96T/9NNPDq+NMTp27JgmT56sunXruqsuAAAAAAAKHJdDet26dWWxWGSMcWi//fbbNW/ePLcVBgAAAABAQeNySN+/f7/Da6vVqrCwMPn7+7utKAAAAAAACiKXQ3pUVFRu1AEAAAAAQIGX4wvHrVq1StWrV1diYmKWaQkJCapRo4b++9//urU4AAAAAAAKkhyH9BkzZqhfv34KCQnJMq1w4cJ69NFHNW3aNLcWBwAAAABAQZLjkL59+3a1adMm2+l3330390gHAAAAAOAG5DiknzhxQj4+PtlO9/b21smTJ91SFAAAAAAABVGOQ3qpUqW0Y8eObKf/9NNPKlGihFuKAgAAAACgIMpxSP/HP/6h0aNHKzk5Ocu0ixcvauzYsWrXrp1biwMAAAAAoCDJ8S3YRo0apSVLlqhy5coaOHCgqlSpIknatWuXZs2apYyMDI0cOTLXCgUAAAAA4O8uxyE9IiJCGzZs0OOPP64RI0bIGCNJslgsiomJ0axZsxQREZFrhQIAAAAA8HeX45AuSVFRUfryyy915swZ7dmzR8YYVapUSUWKFMmt+gAAAAAAKDBcCumZihQpoltvvdXdtQAAAAAAUKDl+MJxAAAAAAAgdxHSAQAAAADwEIR0AAAAAAA8BCEdAAAAAAAPQUgHAAAAAMBDENIBAAAAAPAQhHQAAAAAADwEIR0AAAAAAA9BSAcAAAAAwEMQ0gEAAAAA8BCEdAAAAAAAPAQhHQAAAAAAD0FIBwAAAADAQxDSAQAAAADwEIR0AAAAAAA8BCEdAAAAAAAPQUgHAAAAAMBDENIBAAAAAPAQhHQAAAAAADwEIR0AAAAAAA9BSAcAAAAAwEMQ0gEAAAAA8BCEdAAAAAAAPAQhHQAAAAAAD0FIBwAAAADAQxDSAQAAAADwEIR0AAAAAAA8BCEdAAAAAAAPQUgHAAAAAMBDENIBAAAAAPAQhHQAAAAAADwEIR0AAAAAAA9BSAcAAAAAwEMQ0gEAAAAA8BCEdAAAAAAAPAQhHQAAAAAAD0FIBwAAAADAQxDSAQAAAADwEIR0AAAAAAA8BCEdAAAAAAAPQUgHAAAAAMBDeERInzVrlqKjo+Xv76+GDRvq+++/z7bvnDlz1KxZMxUpUkRFihRR69atr9ofAAAAAICbRb6H9EWLFmnIkCEaO3astmzZojp16igmJkbx8fFO+69Zs0bdu3fX6tWrtXHjRpUpU0Z33323jhw5kseVAwAAAADgXvke0qdNm6Z+/fopNjZW1atX1+zZsxUYGKh58+Y57b9gwQI98cQTqlu3rqpWraq5c+fKZrMpLi4ujysHAAAAAMC9vPNz5ampqdq8ebNGjBhhb7NarWrdurU2btyYo2VcuHBBaWlpKlq0qNPpKSkpSklJsb9OTEyUJNlsNtlsthuoPu/cLHUib9hsNhljGBdwwLiAM4wLOMO4gDOMC2SHseEermy/fA3pp06dUkZGhiIiIhzaIyIitGvXrhwt47nnnlPJkiXVunVrp9MnTZqk8ePHZ2k/efKkkpOTXS86jxgTYH8eHx8vqzXfD3qAh7DZbEpISJAxhnEBO8YFnGFcwBnGBZxhXCA7jA33SEpKynHffA3pN2ry5Mn66KOPtGbNGvn7+zvtM2LECA0ZMsT+OjExUWXKlFFYWJhCQkLyqlSXWSzn7M/Dw8P5hYCdzWaTxWJRWFgY4wJ2jAs4w7iAM4wLOMO4QHYYG+6RXV51Jl9DevHixeXl5aUTJ044tJ84cUKRkZFXnXfq1KmaPHmyvvnmG9WuXTvbfn5+fvLz88vSbrVab5pBdjPVirxhsVgYF8iCcQFnGBdwhnEBZxgXyA5j48a5su3ydSv7+vqqQYMGDhd9y7wIXKNGjbKdb8qUKXrxxRe1fPly3XLLLXlRKgAAAAAAuS7fD3cfMmSIevXqpVtuuUW33XabZsyYofPnzys2NlaS1LNnT5UqVUqTJk2SJL388ssaM2aMFi5cqOjoaB0/flySFBwcrODg4Hx7HwAAAAAA3Kh8D+ldu3bVyZMnNWbMGB0/flx169bV8uXL7ReTO3TokMOhAW+++aZSU1N1//33Oyxn7NixGjduXF6WDgAAAACAW+V7SJekgQMHauDAgU6nrVmzxuH1gQMHcr8gAAAAAADyAWf+AwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIfwzu8CAAAAAMBTZWRkKC0tLb/LyDc2m01paWlKTk6W1co+3qvx9fV1yzYipAMAAADAFYwxOn78uM6ePZvfpeQrY4xsNpuSkpJksVjyuxyPZrVaVa5cOfn6+t7QcgjpAAAAAHCFzIAeHh6uwMDAAhtQjTFKT0+Xt7d3gd0GOWGz2XT06FEdO3ZMZcuWvaFtRUgHAAAAgMtkZGTYA3qxYsXyu5x8RUjPubCwMB09elTp6eny8fG57uVwUgEAAAAAXCbzHPTAwMB8rgQ3k8zD3DMyMm5oOYR0AAAAAHCCPcdwhbvGCyEdAAAAAAAPQUgHAAAAABQ4vXv3VqdOnXLc/8CBA7JYLNq2bVuu1SQR0gEAAADgb+X48eN68sknVb58efn5+alMmTJq37694uLi3LaOO+64Q4MHD3bb8vLDzJkzNX/+/PwuIwuu7g4AAAAAfxMHDhxQkyZNFBoaqldeeUW1atVSWlqaVqxYoQEDBmjXrl15VosxRhkZGfL2zvvYmZqaes37lRcuXDiPqnENe9IBAAAA4G/iiSeekMVi0ffff6/77rtPlStXVo0aNTRkyBBt2rRJknTo0CF17NhRwcHBCgkJUZcuXXTixAn7MsaNG6e6devq/fffV7ly5VS8eHF1795dSUlJki4dJr527VrNnDlTFotFFotFBw4c0Jo1a2SxWPTVV1+pQYMG8vPz07fffquUlBQ99dRTCg8Pl7+/v5o2baoffvhB0qX7i5cuXVpvvvmmw/vYunWrrFarDh48KEk6e/as+vbtq7CwMIWEhKhly5bavn17lprnzp2rcuXKyd/fX5L0ySefqFatWgoICFCxYsXUunVrnT9/3v4+Lj/cffny5WratKlCQ0NVrFgxtWvXTnv37nXzT+ja2JMOAAAAANeyZIl08WLerzcgQLr33hx1PX36tJYvX64JEyYoKCgoy/TQ0FDZbDZ7QF+7dq3S09M1YMAAde3aVWvWrLH33bt3r5YuXar//Oc/OnXqlB588EFNnjxZEyZM0MyZM7V7927VrFlTL7zwgqRL9wg/cOCAJGn48OGaOnWqypcvryJFiujZZ5/Vp59+qnfffVdRUVGaMmWKYmJitGfPHhUtWlTdu3fXwoUL9fjjj9vXv2DBAjVp0kRRUVGSpAceeEABAQH66quvVLhwYb311ltq1aqVdu/eraJFi0qS9uzZo08//VRLliyRl5eXjh07pu7du2vKlCnq3LmzkpKS9N///lfGGKfb7/z58xoyZIhq166tc+fOacyYMercubO2bdsmqzXv9m8T0gEAAADgWi5elP7aA+up9uzZI2OMqlatmm2fuLg4/fzzz9q/f7/KlCkjSXrvvfdUo0YN/fDDD7r11lslXdrDPX/+fAUHBys9PV0PP/yw4uLiNGHCBBUuXFi+vr4KDAxUZGRklnW88MILuuuuuyRdCr5vvvmm5s+fr7Zt20qS5syZo5UrV+rtt9/WM888o4ceekivvvqqDh06pLJly8pms+mjjz7SqFGjJEnffvutvv/+e8XHx8vPz0+SNHXqVC1dulSffPKJ+vfvL+nSIe7vvfeewsLCJElbtmxRenq67r33XnvYr1WrVrbb5r777nN4PW/ePIWFhenXX39VzZo1r7H13YfD3QEAAADgWgICpKCgvH8EBOS4xOz2EF9u586dKlOmjD2gS1L16tUVGhqqnTt32tuio6NVqFAh++sSJUooPj4+R3Xccsst9ud79+5VWlqamjRpYm/z8fHRbbfdZl9f3bp1Va1aNS1cuFCStHbtWsXHx+uBBx6QJG3fvl3nzp1TsWLFFBwcbH/s37/f4XD0qKgoe0CXpDp16qhVq1aqVauWHnjgAc2ZM0dnzpzJtu7ff/9d3bt3V/ny5RUSEqLo6GhJl04PyEvsSQcAAACAa8nhIef5qVKlSrJYLG65OJyPj4/Da4vFIpvNlqN5nR1qfy0PPfSQFi5cqOHDh2vhwoVq06aNihUrJkk6d+6cSpQo4XA4fqbQ0NBs1+vl5aWVK1dqw4YN+vrrr/X6669r5MiR+u6771SuXLksy2rfvr2ioqI0Z84clSxZUjabTTVr1lRqaqrL7+dGsCcdAAAAAP4GihYtqpiYGM2aNct+cbTLnT17VtWqVdPhw4d1+PBhe/uvv/6qs2fPqnr16jlel6+vrzIyMq7Zr0KFCvL19dX69evtbWlpafrhhx8c1vfggw9qx44d2rx5sz755BM99NBD9mn169fX8ePH5e3trYoVKzo8ihcvftX1WywWNWnSROPHj9fWrVvl6+urf//731n6/fnnn/rtt980atQotWrVStWqVbvqXvfcxJ50AAAAAPibmDVrlpo0aaLbbrtNL7zwgmrXrq309HStXLlSb775pn799VfVqlVLDz30kGbMmKH09HQ98cQTatGihcNh6tcSHR2t7777TgcOHFBwcLD94m1XCgoK0uOPP65nnnlGRYsWVdmyZTVlyhRduHBBffr0cVhe48aN1adPH2VkZKhDhw72aa1bt1ajRo3UqVMnTZkyRZUrV9bRo0e1bNkyde7cOdu6v/vuO8XFxenuu+9WeHi4vvvuO508eVLVqlXL0rdIkSIqVqyY/vWvf6lEiRI6dOiQhg8fnuPt4U7sSQcAAACAv4ny5ctry5YtuvPOOzV06FDVrFlTd911l+Li4vTmm2/KYrHos88+U5EiRdS8eXO1bt1a5cuX16JFi1xaz7Bhw+Tl5aXq1asrLCzsqudtT548Wffdd5969Oih+vXra8+ePVqxYoWKFCni0O+hhx7S9u3b1blzZwVcdi6+xWLRl19+qebNmys2NlaVK1dWt27ddPDgQUVERGS73pCQEK1bt07/+Mc/VLlyZY0aNUqvvvqq/QJ2l7Narfroo4+0efNm1axZU08//bReeeUVl7aJu1hMTq4u8DeSmJiowoULKyEhQSEhIfldTrbGfp6o49YQBdhSNK2dT55e8h+ezWazKT4+XuHh4YwL2DEu4AzjAs4wLuAM48JRcnKy9u/f73C/7YLKGKP09HR5e3vLYrHkdzke7WrjxpUcym8gAAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAADIE71791anTp2u2ueOO+7Q4MGD86QeT+QRIX3WrFmKjo6Wv7+/GjZsqO+///6q/T/++GNVrVpV/v7+qlWrlr788ss8qhQAAAAAPFNOArA7rVmzRhaLJctj1KhR2c4zc+ZMzZ8/P89qvBnle0hftGiRhgwZorFjx2rLli2qU6eOYmJiFB8f77T/hg0b1L17d/Xp00dbt25Vp06d1KlTJ+3YsSOPKwcAAAAA/Pbbbzp27Jj9MXz48Cx9MjIyZLPZVLhwYYWGhuZ9kTeRfA/p06ZNU79+/RQbG6vq1atr9uzZCgwM1Lx585z2nzlzptq0aaNnnnlG1apV04svvqj69evrjTfeyOPKAQAAAODmsWPHDrVt21bBwcGKiIhQjx49dOrUKUmX9or7+vrqv//9r73/lClTFBERoRMnTlx1ueHh4YqMjLQ/goODNX/+fIWGhurzzz9X9erV5efnp0OHDmXZ23/+/Hn17NlTwcHBKlGihF599dUsy4+OjtZLL71k7xcVFaXPP/9cJ0+eVMeOHRUcHKzatWvrxx9/tM/z559/qnv37ipVqpQCAwNVq1Ytffjhhw7LveOOO/TUU0/p2WefVdGiRRUZGalx48Zdx5Z1L+/8XHlqaqo2b96sESNG2NusVqtat26tjRs3Op1n48aNGjJkiENbTEyMli5d6rR/SkqKUlJS7K8TExMlSTabTTab7QbfQd64WepE3rDZbDLGMC7ggHEBZxgXcIZxAWcYF44yt0fmQ5ImfislplxjxlwQ4ic939S1eTJrvtzZs2fVsmVL9enTR9OmTdPFixc1fPhwdenSRXFxcWrRooUGDRqkHj16aNu2bdq3b59Gjx6txYsXKzw83OlyM19fvp0un3bhwgW9/PLLmjNnjooVK6awsLAs8w4bNkxr167V0qVLFR4erpEjR9qPsL58mdOnT9eECRM0atQoTZ8+XT169FDjxo0VGxurKVOmaPjw4erZs6d27Nghi8Wiixcvqn79+nr22WcVEhKiZcuWqUePHipfvrxuu+02+3LfffddPf3009q0aZM2btyo2NhYNW7cWHfddZdrG/2y7eAsa7ryu5WvIf3UqVPKyMhQRESEQ3tERIR27drldJ7jx4877X/8+HGn/SdNmqTx48dnaT958qSSk5Ovs/LcZ0yA/Xl8fLys1nw/6AEewmazKSEhQcYYxgXsGBdwhnEBZxgXcIZx4SgtLU02m03p6elKT0+XJCUme+tsiiXvizHGXsO1ZIZDZ/1fe+011a1bVy+88IK97a233lL58uX166+/qnLlyho3bpxWrlypfv366ZdfflGPHj3Utm1bZWRkSJIsFsf3n9lepkwZh/Y9e/bIZrMpLS1NM2fOVJ06dZzWeO7cOc2bN0/z589XixYtJElz585VuXLlZK54323atFGfPn0kSc8//7xmz56t+vXrq3PnzpKkoUOHqlmzZjpy5IgiIyMVERHhcPG5xx9/XCtWrNCiRYtUv379vzatUa1atTRy5EhJUrly5fTGG29o5cqVuvPOO3O0zS+Xnp4um82mP//8Uz4+Pg7TkpKScrycfA3peWHEiBEOe94TExNVpkwZhYWFKSQkJB8ru7qnm15Uekaizp45o/DwUnxYws5ms8lisSgsLIxxATvGBZxhXMAZxgWcYVw4Sk5OVlJSkry9veXtfSkyhfhLsmTdQ53bQvxkr+FarFarrFar0/47duzQmjVrVKRIkSzTDh48qOrVq8vb21sLFixQnTp1FBUVpRkzZtjD5pWhU5K8vLwkSevWrVOhQoXs7ZnjyNfXV/Xr13cI95fXePDgQaWmpqpx48b2msPDw1WlShVZLBaH91GnTh3761KlSmVpK1mypCTp9OnTKl26tDIyMjRx4kR9/PHHOnLkiFJTU5WSkqKgoCD7PBaLRbVr13ZYT8mSJXXq1Kkcb/PLeXt7y2q1qlixYvL393eYduXrqy7H5TW7UfHixeXl5ZXlHIcTJ04oMjLS6TyRkZEu9ffz85Ofn1+W9szB4amKFAuSzWZThu2Cx9eKvGexWBgXyIJxAWcYF3CGcQFnGBf/Z7VaHa5WLkkjm+VzUS64co+3JJ07d07t27fXyy+/nGVaiRIl7PNknnZ8+vRpnTlzRkFBQfZpVy4383X58uWzXAzOYrEoICAg2/F0+ba9/Lmz6ZLk6+ubpY7L2zLXY4yRxWLR1KlT9dprr2nGjBmqVauWgoKCNHjwYKWmpma73MxlZy7DVZk1O/s9cuX3Kl9/A319fdWgQQPFxcXZ22w2m+Li4tSoUSOn8zRq1MihvyStXLky2/4AAAAAUNDVr19fv/zyi6Kjo1WxYkWHR1BQkCRp7969evrppzVnzhw1bNhQvXr1ytXrFFSoUEE+Pj767rvv7G1nzpzR7t27b3jZ69evV8eOHfXwww+rTp06Kl++vFuWmxfy/c9kQ4YM0Zw5c/Tuu+9q586devzxx3X+/HnFxsZKknr27OlwYblBgwZp+fLlevXVV7Vr1y6NGzdOP/74owYOHJhfbwEAAAAAPEJCQoK2bdvm8Dh8+LAGDBig06dPq3v37vrhhx+0d+9erVixQrGxscrIyFBGRoYefvhhxcTEKDY2Vu+8845++uknp1dbd5fg4GD16dNHzzzzjFatWqUdO3aod+/ebjmao1KlSlq5cqU2bNignTt36tFHH73mVeo9Rb6fk961a1edPHlSY8aM0fHjx1W3bl0tX77cfnG4Q4cOOfyQGjdurIULF2rUqFF6/vnnValSJS1dulQ1a9bMr7cAAAAAAB5hzZo1qlevnkNbnz59NHfuXK1fv17PPfec7r77bqWkpCgqKkpt2rSR1WrViy++qIMHD+qLL76QdOkQ+H/961/q3r27WrZsqQYNGuRKva+88or9UPxChQpp6NChSkhIuOHljho1Svv27VNMTIwCAwPVv39/derUyS3Lzm0W4+z6/H9jiYmJKly4sBISEjz6wnHSpUP/4+PjFR4ezrlBsGNcwBnGBZxhXMAZxgWcYVw4Sk5O1v79+1WuXDmXLvj1d5R5lXVvb+/rOk+7ILnauHElh/IbCAAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpAOAAAAAE4UsGts4wa5a7wQ0gEAAADgMj4+PpKkCxcu5HMluJmkpqZKkry8vG5oOfl+n3QAAAAA8CReXl4KDQ1VfHy8JCkwMLDA3n6MW7DljM1m08mTJxUYGChv7xuL2YR0AAAAALhCZGSkJNmDekFljJHNZpPVaiWkX4PValXZsmVveDsR0gEAAADgChaLRSVKlFB4eLjS0tLyu5x8Y7PZ9Oeff6pYsWKyWjlb+mp8fX3dso0I6QAAAACQDS8vrxs+x/hmZrPZ5OPjI39/f0J6HmErAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAeosCdk555g/nExMR8ruTabDabkpKSOP8DDhgXcIZxAWcYF3CGcQFnGBfIDmPDPTLzZ2YevZoCF9KTkpIkSWXKlMnnSgAAAAAABUlSUpIKFy581T4Wk5Mo/zdis9l09OhRFSpUyOPv85eYmKgyZcro8OHDCgkJye9y4CEYF3CGcQFnGBdwhnEBZxgXyA5jwz2MMUpKSlLJkiWveURCgduTbrVaVbp06fwuwyUhISH8QiALxgWcYVzAGcYFnGFcwBnGBbLD2Lhx19qDnomTCgAAAAAA8BCEdAAAAAAAPAQh3YP5+flp7Nix8vPzy+9S4EEYF3CGcQFnGBdwhnEBZxgXyA5jI+8VuAvHAQAAAADgqdiTDgAAAACAhyCkAwAAAADgIQjpAAAAAAB4CEI6AAAAAAAegpCeh2bNmqXo6Gj5+/urYcOG+v7776/a/+OPP1bVqlXl7++vWrVq6csvv3SYbozRmDFjVKJECQUEBKh169b6/fffc/MtIBe4e1z07t1bFovF4dGmTZvcfAvIBa6Mi19++UX33XefoqOjZbFYNGPGjBteJjyXu8fGuHHjsnxmVK1aNRffAXKDK+Nizpw5atasmYoUKaIiRYqodevWWfrzHePvwd3jgu8Yfw+ujIslS5bolltuUWhoqIKCglS3bl29//77Dn34vHA/QnoeWbRokYYMGaKxY8dqy5YtqlOnjmJiYhQfH++0/4YNG9S9e3f16dNHW7duVadOndSpUyft2LHD3mfKlCl67bXXNHv2bH333XcKCgpSTEyMkpOT8+pt4QblxriQpDZt2ujYsWP2x4cffpgXbwdu4uq4uHDhgsqXL6/JkycrMjLSLcuEZ8qNsSFJNWrUcPjM+Pbbb3PrLSAXuDou1qxZo+7du2v16tXauHGjypQpo7vvvltHjhyx9+E7xs0vN8aFxHeMm52r46Jo0aIaOXKkNm7cqJ9++kmxsbGKjY3VihUr7H34vMgFBnnitttuMwMGDLC/zsjIMCVLljSTJk1y2r9Lly7mnnvucWhr2LChefTRR40xxthsNhMZGWleeeUV+/SzZ88aPz8/8+GHH+bCO0BucPe4MMaYXr16mY4dO+ZKvcgbro6Ly0VFRZnp06e7dZnwHLkxNsaOHWvq1KnjxiqR12709zs9Pd0UKlTIvPvuu8YYvmP8Xbh7XBjDd4y/A3d8H6hXr54ZNWqUMYbPi9zCnvQ8kJqaqs2bN6t169b2NqvVqtatW2vjxo1O59m4caNDf0mKiYmx99+/f7+OHz/u0Kdw4cJq2LBhtsuEZ8mNcZFpzZo1Cg8PV5UqVfT444/rzz//dP8bQK64nnGRH8tE3svNn+Pvv/+ukiVLqnz58nrooYd06NChGy0XecQd4+LChQtKS0tT0aJFJfEd4+8gN8ZFJr5j3LxudFwYYxQXF6fffvtNzZs3l8TnRW4hpOeBU6dOKSMjQxEREQ7tEREROn78uNN5jh8/ftX+mf+6skx4ltwYF9Klw9Dee+89xcXF6eWXX9batWvVtm1bZWRkuP9NwO2uZ1zkxzKR93Lr59iwYUPNnz9fy5cv15tvvqn9+/erWbNmSkpKutGSkQfcMS6ee+45lSxZ0v4lm+8YN7/cGBcS3zFudtc7LhISEhQcHCxfX1/dc889ev3113XXXXdJ4vMit3jndwEA3Ktbt27257Vq1VLt2rVVoUIFrVmzRq1atcrHygB4orZt29qf165dWw0bNlRUVJQWL16sPn365GNlyAuTJ0/WRx99pDVr1sjf3z+/y4GHyG5c8B2jYCpUqJC2bdumc+fOKS4uTkOGDFH58uV1xx135Hdpf1vsSc8DxYsXl5eXl06cOOHQfuLEiWwv5BMZGXnV/pn/urJMeJbcGBfOlC9fXsWLF9eePXtuvGjkuusZF/mxTOS9vPo5hoaGqnLlynxm3CRuZFxMnTpVkydP1tdff63atWvb2/mOcfPLjXHhDN8xbi7XOy6sVqsqVqyounXraujQobr//vs1adIkSXxe5BZCeh7w9fVVgwYNFBcXZ2+z2WyKi4tTo0aNnM7TqFEjh/6StHLlSnv/cuXKKTIy0qFPYmKivvvuu2yXCc+SG+PCmT/++EN//vmnSpQo4Z7CkauuZ1zkxzKR9/Lq53ju3Dnt3buXz4ybxPWOiylTpujFF1/U8uXLdcsttzhM4zvGzS83xoUzfMe4ubjr/yM2m00pKSmS+LzINfl95bqC4qOPPjJ+fn5m/vz55tdffzX9+/c3oaGh5vjx48YYY3r06GGGDx9u779+/Xrj7e1tpk6danbu3GnGjh1rfHx8zM8//2zvM3nyZBMaGmo+++wz89NPP5mOHTuacuXKmYsXL+b5+8P1cfe4SEpKMsOGDTMbN240+/fvN998842pX7++qVSpkklOTs6X9wjXuTouUlJSzNatW83WrVtNiRIlzLBhw8zWrVvN77//nuNl4uaQG2Nj6NChZs2aNWb//v1m/fr1pnXr1qZ48eImPj4+z98fro+r42Ly5MnG19fXfPLJJ+bYsWP2R1JSkkMfvmPc3Nw9LviO8ffg6riYOHGi+frrr83evXvNr7/+aqZOnWq8vb3NnDlz7H34vHA/Qnoeev31103ZsmWNr6+vue2228ymTZvs01q0aGF69erl0H/x4sWmcuXKxtfX19SoUcMsW7bMYbrNZjOjR482ERERxs/Pz7Rq1cr89ttvefFW4EbuHBcXLlwwd999twkLCzM+Pj4mKirK9OvXjyB2E3JlXOzfv99IyvJo0aJFjpeJm4e7x0bXrl1NiRIljK+vrylVqpTp2rWr2bNnTx6+I7iDK+MiKirK6bgYO3asvQ/fMf4e3Dku+I7x9+HKuBg5cqSpWLGi8ff3N0WKFDGNGjUyH330kcPy+LxwP4sxxuTtvnsAAAAAAOAM56QDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeAhCOgAAAAAAHoKQDgAAAACAhyCkAwCAXHfgwAFZLBZt27Ytv0sBAMCjEdIBAMih3r17q1OnTlna16xZI4vForNnz+Z4WXfccYcGDx7slrrmzJmjOnXqKDg4WKGhoapXr54mTZrklmXnt+3bt6tDhw4KDw+Xv7+/oqOj1bVrV8XHx+d3aQAA5Arv/C4AAABcv3nz5mnw4MF67bXX1KJFC6WkpOinn37Sjh078ru0G3by5Em1atVK7dq104oVKxQaGqoDBw7o888/1/nz53NtvWlpafLx8cm15QMAcDXsSQcAwM3+/PNPde/eXaVKlVJgYKBq1aqlDz/80D69d+/eWrt2rWbOnCmLxSKLxaIDBw5Iknbs2KG2bdsqODhYERER6tGjh06dOpXtuj7//HN16dJFffr0UcWKFVWjRg11795dEyZMcFhfp06dNH78eIWFhSkkJESPPfaYUlNT7X1sNpsmTZqkcuXKKSAgQHXq1NEnn3zisK5r1Waz2TRlyhRVrFhRfn5+Klu2rEMdkrRv3z7deeedCgwMVJ06dbRx48Zs39v69euVkJCguXPnql69eipXrpzuvPNOTZ8+XeXKlbP3++WXX9SuXTuFhISoUKFCatasmfbu3Wuv6YUXXlDp0qXl5+enunXravny5fZ5Mw/DX7RokVq0aCF/f38tWLBAkjR37lxVq1ZN/v7+qlq1qv75z39mWysAAO5CSAcAwM2Sk5PVoEEDLVu2TDt27FD//v3Vo0cPff/995KkmTNnqlGjRurXr5+OHTumY8eOqUyZMjp79qxatmypevXq6ccff9Ty5ct14sQJdenSJdt1RUZGatOmTTp48OBVa4qLi9POnTu1Zs0affjhh1qyZInGjx9vnz5p0iS99957mj17tn755Rc9/fTTevjhh7V27VpJylFtI0aM0OTJkzV69Gj9+uuvWrhwoSIiIhzqGDlypIYNG6Zt27apcuXK6t69u9LT07N9b+np6fr3v/8tY4zTPkeOHFHz5s3l5+enVatWafPmzXrkkUfsy5w5c6ZeffVVTZ06VT/99JNiYmLUoUMH/f777w7LGT58uAYNGqSdO3cqJiZGCxYs0JgxYzRhwgTt3LlTEydO1OjRo/Xuu+9edTsDAHDDDAAAyJFevXoZLy8vExQU5PDw9/c3ksyZM2eynfeee+4xQ4cOtb9u0aKFGTRokEOfF1980dx9990ObYcPHzaSzG+//eZ0uUePHjW33367kWQqV65sevXqZRYtWmQyMjIc6i5atKg5f/68ve3NN980wcHBJiMjwyQnJ5vAwECzYcMGh2X36dPHdO/ePUe1JSYmGj8/PzNnzhynde7fv99IMnPnzrW3/fLLL0aS2blzp9N5jDHm+eefN97e3qZo0aKmTZs2ZsqUKeb48eP26SNGjDDlypUzqampTucvWbKkmTBhgkPbrbfeap544gmHumbMmOHQp0KFCmbhwoUObS+++KJp1KhRtrUCAOAOnJMOAIAL7rzzTr355psObd99950efvhh++uMjAxNnDhRixcv1pEjR5SamqqUlBQFBgZeddnbt2/X6tWrFRwcnGXa3r17Vbly5SztJUqU0MaNG7Vjxw6tW7dOGzZsUK9evTR37lwtX75cVuulg+bq1KnjsP5GjRrp3LlzOnz4sM6dO6cLFy7orrvuclh2amqq6tWrl6Pazp49q5SUFLVq1eqq77F27doOtUtSfHy8qlat6rT/hAkTNGTIEK1atUrfffedZs+erYkTJ2rdunWqVauWtm3bpmbNmjk9hzwxMVFHjx5VkyZNHNqbNGmi7du3O7Tdcsst9ufnz5/X3r171adPH/Xr18/enp6ersKFC1/1/QEAcKMI6QAAuCAoKEgVK1Z0aPvjjz8cXr/yyiuaOXOmZsyYoVq1aikoKEiDBw92OAfcmXPnzql9+/Z6+eWXs0zLDLTZqVmzpmrWrKknnnhCjz32mJo1a6a1a9fqzjvvvOZ7OnfunCRp2bJlKlWqlMM0Pz+/HNW2b9++a65HkkOYtlgski6dN341xYoV0wMPPKAHHnhAEydOVL169TR16lS9++67CggIyNF6ryUoKMj+PHN7zJkzRw0bNnTo5+Xl5Zb1AQCQHUI6AAButn79enXs2NG+d91ms2n37t2qXr26vY+vr68yMjIc5qtfv74+/fRTRUdHy9v7+v8Xnbmey6+Avn37dl28eNEeajdt2qTg4GCVKVNGRYsWlZ+fnw4dOqQWLVo4Xea1aqtUqZICAgIUFxenvn37Xnft1+Lr66sKFSrY31vt2rX17rvvOr0ie0hIiEqWLKn169c7vK/169frtttuy3YdERERKlmypPbt26eHHnood94IAADZ4MJxAAC4WaVKlbRy5Upt2LBBO3fu1KOPPqoTJ0449ImOjtZ3332nAwcO6NSpU7LZbBowYIBOnz6t7t2764cfftDevXu1YsUKxcbGZgn0mR5//HG9+OKLWr9+vQ4ePKhNmzapZ8+eCgsLU6NGjez9UlNT1adPH/3666/68ssvNXbsWA0cOFBWq1WFChXSsGHD9PTTT+vdd9/V3r17tWXLFr3++uv2C6VdqzZ/f38999xzevbZZ/Xee+9p79692rRpk95+++3r3o5ffPGFHn74YX3xxRfavXu3fvvtN02dOlVffvmlOnbsKEkaOHCgEhMT1a1bN/3444/6/fff9f777+u3336TJD3zzDN6+eWXtWjRIv32228aPny4tm3bpkGDBl113ePHj9ekSZP02muvaffu3fr555/1zjvvaNq0adf9fgAAyAn2pAMA4GajRo3Svn37FBMTo8DAQPXv31+dOnVSQkKCvc+wYcPUq1cvVa9eXRcvXtT+/fsVHR2t9evX67nnntPdd9+tlJQURUVFqU2bNvZzy6/UunVrzZs3T2+++ab+/PNPFS9eXI0aNVJcXJyKFStm79eqVStVqlRJzZs3V0pKirp3765x48bZp7/44osKCwvTpEmTtG/fPoWGhqp+/fp6/vnnJcm+R/pqtY0ePVre3t4aM2aMjh49qhIlSuixxx677u1YvXp1BQYGaujQoTp8+LD8/PxUqVIlzZ07Vz169JB06VD4VatW6ZlnnlGLFi3k5eWlunXr2s9Df+qpp5SQkKChQ4cqPj5e1atX1+eff65KlSpddd19+/ZVYGCgXnnlFT3zzDMKCgpSrVq1NHjw4Ot+PwAA5ITFmGzuaQIAAP4WevfurbNnz2rp0qX5XQoAALgGDncHAAAAAMBDENIBAAAAAPAQHO4OAAAAAICHYE86AAAAAAAegpAOAAAAAICHIKQDAAAAAOAhCOkAAAAAAHgIQjoAAAAAAB6CkA4AAAAAgIcgpAMAAAAA4CEI6QAAAAAAeIj/AbVcS7vc2+ZoAAAAAElFTkSuQmCC", "text/plain": [ "
" ] @@ -2072,34 +2072,34 @@ "Segment-level statistics for hate speech scores:\n", "\n", "Controversial:\n", - " Count: 63780\n", - " Mean: 0.0032\n", + " Count: 52185\n", + " Mean: 0.0024\n", " Median: 0.0000\n", - " Std Dev: 0.0199\n", + " Std Dev: 0.0174\n", " Min: 0.0000\n", - " Max: 0.2883\n", + " Max: 0.3151\n", " 25th Percentile: 0.0000\n", " 75th Percentile: 0.0000\n", " 90th Percentile: 0.0000\n", " 95th Percentile: 0.0000\n", - " 99th Percentile: 0.1236\n", + " 99th Percentile: 0.1166\n", " High risk segments (> 0.5): 0 (0.00%)\n", - " Medium risk segments (0.1-0.5): 1624 (2.55%)\n", + " Medium risk segments (0.1-0.5): 976 (1.87%)\n", "\n", "Lex Fridman:\n", - " Count: 7226\n", - " Mean: 0.0002\n", + " Count: 7008\n", + " Mean: 0.0013\n", " Median: 0.0000\n", - " Std Dev: 0.0047\n", + " Std Dev: 0.0134\n", " Min: 0.0000\n", - " Max: 0.1622\n", + " Max: 0.2053\n", " 25th Percentile: 0.0000\n", " 75th Percentile: 0.0000\n", " 90th Percentile: 0.0000\n", " 95th Percentile: 0.0000\n", - " 99th Percentile: 0.0000\n", + " 99th Percentile: 0.1017\n", " High risk segments (> 0.5): 0 (0.00%)\n", - " Medium risk segments (0.1-0.5): 10 (0.14%)\n" + " Medium risk segments (0.1-0.5): 72 (1.03%)\n" ] } ], @@ -2244,7 +2244,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "id": "a9f35cf0", "metadata": {}, "outputs": [ @@ -2292,87 +2292,87 @@ " \n", " \n", " 0\n", - " max_score\n", - " 0.964444\n", - " 0.933333\n", - " 0.933333\n", - " 0.372843\n", - " 0.950820\n", - " 0.935484\n", - " 0.966667\n", - " 0.142586\n", - " 3.427207\n", + " skewness\n", + " 0.894444\n", + " 0.900000\n", " 0.900000\n", + " 0.440945\n", + " 0.918033\n", + " 0.903226\n", + " 0.933333\n", + " 0.083502\n", + " 1.067583\n", + " 0.833333\n", " \n", " \n", " 1\n", - " skewness\n", - " 0.960000\n", - " 0.900000\n", - " 0.900000\n", - " 0.376975\n", - " 0.935484\n", - " 0.906250\n", - " 0.966667\n", - " 0.140606\n", - " 2.752996\n", - " 0.866667\n", + " max_score\n", + " 0.860000\n", + " 0.766667\n", + " 0.766667\n", + " 0.476998\n", + " 0.848485\n", + " 0.777778\n", + " 0.933333\n", + " 0.104175\n", + " 1.792746\n", + " 0.666667\n", " \n", " \n", " 2\n", - " top_k_mean\n", - " 0.960000\n", - " 0.933333\n", + " percentile_score\n", + " 0.852222\n", + " 0.766667\n", + " 0.766667\n", + " 0.485390\n", + " 0.848485\n", + " 0.777778\n", " 0.933333\n", - " 0.376975\n", - " 0.950820\n", - " 0.935484\n", - " 0.966667\n", - " 0.130700\n", - " 3.322732\n", - " 0.900000\n", + " 0.093693\n", + " 1.803957\n", + " 0.666667\n", " \n", " \n", " 3\n", - " percentile_score\n", - " 0.945556\n", - " 0.933333\n", + " top_k_mean\n", + " 0.847778\n", + " 0.766667\n", + " 0.766667\n", + " 0.490228\n", + " 0.848485\n", + " 0.777778\n", " 0.933333\n", - " 0.390578\n", - " 0.950820\n", - " 0.935484\n", - " 0.966667\n", - " 0.123039\n", - " 3.272535\n", - " 0.900000\n", + " 0.094076\n", + " 1.756884\n", + " 0.666667\n", " \n", " \n", " 4\n", - " mean_of_positives\n", - " 0.922222\n", - " 0.933333\n", + " softmax_weighted_mean\n", + " 0.796667\n", + " 0.766667\n", + " 0.766667\n", + " 0.548223\n", + " 0.848485\n", + " 0.777778\n", " 0.933333\n", - " 0.413127\n", - " 0.950820\n", - " 0.935484\n", - " 0.966667\n", - " 0.101905\n", - " 3.062712\n", - " 0.900000\n", + " 0.079249\n", + " 1.715674\n", + " 0.666667\n", " \n", " \n", " 5\n", - " softmax_weighted_mean\n", - " 0.922222\n", - " 0.933333\n", + " mean_of_positives\n", + " 0.795556\n", + " 0.766667\n", + " 0.766667\n", + " 0.549534\n", + " 0.848485\n", + " 0.777778\n", " 0.933333\n", - " 0.413127\n", - " 0.950820\n", - " 0.935484\n", - " 0.966667\n", - " 0.102232\n", - " 3.065614\n", - " 0.900000\n", + " 0.078998\n", + " 1.714080\n", + " 0.666667\n", " \n", " \n", "\n", @@ -2380,23 +2380,23 @@ ], "text/plain": [ " aggregator roc_auc recall_at_n precision_at_n rank_ratio \\\n", - "0 max_score 0.964444 0.933333 0.933333 0.372843 \n", - "1 skewness 0.960000 0.900000 0.900000 0.376975 \n", - "2 top_k_mean 0.960000 0.933333 0.933333 0.376975 \n", - "3 percentile_score 0.945556 0.933333 0.933333 0.390578 \n", - "4 mean_of_positives 0.922222 0.933333 0.933333 0.413127 \n", - "5 softmax_weighted_mean 0.922222 0.933333 0.933333 0.413127 \n", + "0 skewness 0.894444 0.900000 0.900000 0.440945 \n", + "1 max_score 0.860000 0.766667 0.766667 0.476998 \n", + "2 percentile_score 0.852222 0.766667 0.766667 0.485390 \n", + "3 top_k_mean 0.847778 0.766667 0.766667 0.490228 \n", + "4 softmax_weighted_mean 0.796667 0.766667 0.766667 0.548223 \n", + "5 mean_of_positives 0.795556 0.766667 0.766667 0.549534 \n", "\n", " f1 precision recall mean_separation cohens_d ks_statistic \n", - "0 0.950820 0.935484 0.966667 0.142586 3.427207 0.900000 \n", - "1 0.935484 0.906250 0.966667 0.140606 2.752996 0.866667 \n", - "2 0.950820 0.935484 0.966667 0.130700 3.322732 0.900000 \n", - "3 0.950820 0.935484 0.966667 0.123039 3.272535 0.900000 \n", - "4 0.950820 0.935484 0.966667 0.101905 3.062712 0.900000 \n", - "5 0.950820 0.935484 0.966667 0.102232 3.065614 0.900000 " + "0 0.918033 0.903226 0.933333 0.083502 1.067583 0.833333 \n", + "1 0.848485 0.777778 0.933333 0.104175 1.792746 0.666667 \n", + "2 0.848485 0.777778 0.933333 0.093693 1.803957 0.666667 \n", + "3 0.848485 0.777778 0.933333 0.094076 1.756884 0.666667 \n", + "4 0.848485 0.777778 0.933333 0.079249 1.715674 0.666667 \n", + "5 0.848485 0.777778 0.933333 0.078998 1.714080 0.666667 " ] }, - "execution_count": null, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2471,13 +2471,13 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "id": "da17416d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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Y9xTF/Pnz1alTp0J/MPTll1+qSZMmCggIUMOGDd0uRXX48GE9+OCDioiIkL+/v+rWravnn39eNpvNqd+sWbMUExOjihUrKjg4WI0aNdLEiRMlnVgS/5ZbbpEkdezYsUSXa42IiJDFUrT/hI6JiVHVqlVLZE4HAEBp2bp1qy6//HKXYp8khYaGurS99957jnlG1apV1bdvX+3atcupz8nzozZt2igwMFAXX3yx0zLZ0tmdx5owYYLjPNbvv/9epDG2b9+ukJAQSXL8uPnkexS7u4dffn6+nnrqKce+oqKi9Nhjj7nM4+z3Kl65cqVatmypgIAA1alTp8hXk82fP1+tWrVyKUSNHj1aVatW1dSpU52KfXaxsbFO90g+cOCA7rjjDoWFhSkgIEDR0dFurxq0K8r5wI0bN6pXr16qWrWqAgIC1Lx5c3366adOfey3Ivr222+VmJiokJAQBQUF6cYbb1R6evoZj3/SpEm6/PLLVb58eVWpUkXNmzfXBx98cMbtdu/erZ49eyooKEihoaEaPnx4ofdOvuaaa5Sdna2vvvrqjOMCFwKu8AO8kP1+JFWqVHG0ZWZm6p133lG/fv00ZMgQZWVlacqUKYqNjdXatWvVpEkTpzE++OADZWVl6a677pKPj49eeOEF3XTTTfrrr7/cTiakE8tDxsbGqnnz5lqwYIECAwNPG+dzzz0ni8Wihx9+WEeOHNELL7ygW2+9VWvWrHH0eeONNzR06FBdddVVGj58uLZv366ePXuqSpUqRSqWrVq1Ss2aNTtjPzt3ufviiy9UUFCggQMHFrrdwIEDtXTpUi1atEiDBw/WF198ofz8fA0YMKDI+y7Mp59+qqNHj6pv376qUaOGOnToUCJLM3z66acKDAxUr169iryNfcnPb7/9Vk2bNj2n/QMA4M2KMo+RpEOHDqlr167q27evbrvtNoWFhclms+mGG27QypUrdeedd+qyyy7TL7/8oldeeUV//vmn5s+fL0n67bffdP3116tx48Z68skn5e/vry1btujbb789q3imT5+uhIQEtWjRQuPGjVNaWpomTpyob7/9Vj/++KPbk3l2gwcP1nvvvaf+/furTZs2WrJkibp3716kXO3Zs0c7d+4sdE62efNm9enTR3fffbfi4+M1bdo03XLLLVq0aJGuueYaSSeujGzfvr327Nmju+66S7Vr19aqVauUlJSkffv2acKECZJOFEj79eunzp076/nnn5d04qrMb7/9VsOGDdPVV1+tBx54QK+++qoee+wxXXbZZZLk+N/TycjIcHputVqd5ozF1axZM7fvJQAA54vIyEitXr1av/766xmvXn/mmWc0evRo9e7dW4MHD1Z6eromTZqkq6++2mWe8ffff6tbt27q3bu3+vXrpzlz5uiee+6Rn5+fbr/9dknFP481bdo05eTk6M4775S/v7+qVq1apDFCQkL0xhtv6J577tGNN96om266SZLUuHHjQo918ODBmjFjhnr16qWHHnpIa9as0bhx4/THH3/ok08+ceq7ZcsW9erVS3fccYfi4+M1depUDRo0SDExMbr88ssL3cfx48f1/fff65577nFq37x5szZu3Kjbb79dFStWPO17Ip34EXmHDh20ZcsWDR06VBdffLE++ugjDRo0SIcPH9awYcOc+hflfOBvv/2mtm3bqlatWho5cqSCgoI0Z84c9ezZU3PnztWNN97oNOb999+vKlWqKDk5Wdu3b9eECRM0dOhQzZ49u9C43377bT3wwAPq1auXhg0bppycHP38889as2bNac+H/fPPP+rcubN27typBx54QDVr1tTMmTO1ZMkSt/0bNmyowMBAffvtty5xAxckA+C8NW3aNCPJfP311yY9Pd3s2rXLfPzxxyYkJMT4+/ubXbt2Ofrm5+eb3Nxcp+3//vtvExYWZm6//XZH27Zt24wkU61aNZORkeFoX7BggZFk/ve//zna4uPjTVBQkDHGmJUrV5rg4GDTvXt3k5OT47Sf9u3bm/bt2zueL1261Egyl112mVNMEydONJLML7/8YowxJjc311SrVs20aNHCHD9+3NFv+vTpRpLTmO4cP37c+Pj4mIceesjlteTkZCPJbNq0yaSnp5vt27ebqVOnmsDAQBMSEmKys7MdfR988EEjyfz444+F7mv9+vVGkklMTDTGGDN8+PAzblNU119/vWnbtq3j+VtvvWV8fX3NgQMHnPqd/H64ExQUZOLj4x3Pq1SpYqKjo4sdj5+fn7nnnnuKvR0AAN7ivvvuM4X9p1BR5zHGnJgDSTIpKSlOY8ycOdNYLBbzzTffOLWnpKQYSebbb781xhjzyiuvGEkmPT290FiLGk9eXp4JDQ01V1xxhfnnn38c/T777DMjyYwZM8bRZp8n2W3YsMFIMvfee6/Tvvv3728kmeTk5ELjM8aYr7/+2mUeaRcZGWkkmblz5zrajhw5YsLDw03Tpk0dbU899ZQJCgoyf/75p9P2I0eONFar1ezcudMYY8ywYcNMcHCwyc/PLzSejz76yEgyS5cuPW3cdvZ8nPqIjIwsdJvLL7/8jHPVO++80wQGBhYpBgAAysKXX35prFarsVqtpnXr1mbEiBFm8eLFJi8vz6nf9u3bjdVqNc8884xT+y+//GJ8fX2d2u3zo5dfftnRlpuba5o0aWJCQ0MdYxf3PFZwcLDLeZKijpGenl7onKawedHgwYOd+j388MNGklmyZImjzT7PWbFihaPtwIEDxt/f3+25qpNt2bLFSDKTJk1yarefn3vllVdOu73dhAkTjCTz3nvvOdry8vJM69atTYUKFUxmZqYxpnjnAzt37mwaNWrkdP7PZrOZNm3amEsvvdTRZj9v2aVLF2Oz2Rztw4cPN1ar1Rw+fLjQuHv06GEuv/zyIh2ju+OdM2eOoy07O9vUrVu30PlfvXr1TNeuXYu9L8AbsaQn4AW6dOmikJAQRUREqFevXgoKCtKnn37qdAWc1Wp1rF1us9mUkZGh/Px8NW/eXOvXr3cZs0+fPk6/WLYvdfnXX3+59F26dKliY2PVuXNnzZs3T/7+/kWKOyEhwWk99VP38cMPP+jQoUMaMmSI03rpt956a5F+TZ2RkSFjzGn71q9fXyEhIYqKitLtt9+uunXr6osvvnC6t05WVpYknfaXU/bXMjMznf63KL+2Op1Dhw5p8eLF6tevn6Pt5ptvdiyJei4yMzPPKr4qVaro4MGD57RvAAC83ZnmMXb+/v5KSEhwavvoo4902WWXqUGDBjp48KDjYb+Xm32ZKfsv4RcsWOCybGVx4/nhhx904MAB3XvvvQoICHD06969uxo0aKCFCxcWOvbnn38uSXrggQec2t0tW+7OoUOHJKnQOVnNmjWdflEdHBysgQMH6scff9T+/fslncjZVVdd5ZiH2B9dunRRQUGBVqxYIelEzkprWaa5c+fqq6++cjzef//9cxqvSpUq+ueff3Ts2LESihAAgJJ1zTXXaPXq1brhhhv0008/6YUXXlBsbKxq1arltHzjvHnzZLPZ1Lt3b6d/p2vUqKFLL73UZRlOX19f3XXXXY7nfn5+uuuuu3TgwAGtW7dOUvHPY918882OpTntijtGUdjnRYmJiU7tDz30kCS5zKkaNmzomJdJUkhIiOrXr+/2/NrJCps/Ffd80+eff64aNWo4nVcqV66cHnjgAR09elTLly936n+m84EZGRlasmSJevfuraysLMd7fejQIcXGxmrz5s0uy8XfeeedTsuiXnXVVSooKNCOHTsKjbty5cravXt3kW4vdOrxhoeHO61mVb58ed15552FbsN5LvyXsKQn4AUmT56sevXq6ciRI5o6dapWrFjhtug2Y8YMvfzyy9q4caPTzXsvvvhil761a9d2em7/x/7vv/92as/JyVH37t0VExOjOXPmFOtGxmfah/0f/rp16zr18/X1VVRUVJH3Y4wp9LW5c+cqODhY6enpevXVV7Vt2zaXpUjtkyh74c+dU4uCwcHBZ9zGLi8vz2WJqJCQEFmtVs2ePVvHjx9X06ZNne7D06pVK73//vu67777zjj+yU6eYAUHBxcpvlMZYwq9/w4AAP8VRZ0r1apVy6kQJ51YiumPP/5wOSlld+DAAUknTri88847Gjx4sEaOHKnOnTvrpptuUq9evVzuFVfUeVX9+vVd9tegQQOtXLmy0GPdsWOHLBaLyz2R3Y11OoXNyerWresyt6hXr56kE8ut16hRQ5s3b9bPP/98xpzde++9mjNnjrp27apatWrp2muvVe/evXXdddcVK1Z3rr76alWvXv2cx7Gz54N5FQDgfNaiRQvNmzdPeXl5+umnn/TJJ5/olVdeUa9evbRhwwY1bNhQmzdvljFGl156qdsxTr01TM2aNRUUFOTUdvK//VdeeaWk4p3HctdW3DGKwj4vOvVcVY0aNVS5cmWXItapczTpxDzt1DljYU6dPxXnfJM93ksvvdRl7mhfzvxM8Z46p9yyZYuMMRo9erRGjx7tdp8HDhxQrVq1ijymO48++qi+/vprtWzZUnXr1tW1116r/v37q23btoVuYz8ed3PL081bOc+F/xIKfoAXaNmypZo3by5J6tmzp9q1a6f+/ftr06ZNjhv7vvfeexo0aJB69uypRx55RKGhobJarRo3bpy2bt3qMqbVanW7r1MnGv7+/urWrZsWLFigRYsWOd0U+EyKuo+zVbVqVfn4+Jx2AnHyiZu4uDg1atRIt956q9atW+eYDNknQT///LPLGvF2P//8s6QTv9ySTpw4k6Rffvml0G3sVq1apY4dOzq1bdu2TVFRUY5fjhc2ofnrr79Up04dSVJAQIByc3PdTlSMMcrJyXH6RX+DBg20YcMG5eXluZyIPJ3Dhw+X6MkuAAC8UVHnMe7uaWyz2dSoUSONHz/e7RgRERGObVesWKGlS5dq4cKFWrRokWbPnq1OnTrpyy+/dIqhtOdV56JatWqSTn9S50xsNpuuueYajRg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", "text/plain": [ "
" ] @@ -2516,7 +2516,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "id": "b196a11d", "metadata": {}, "outputs": [ @@ -2562,90 +2562,90 @@ " skewness\n", " 5\n", " 0.0\n", - " 0.995556\n", - " 0.966667\n", - " 4.111937\n", + " 0.976667\n", + " 0.935484\n", + " 2.755040\n", " \n", " \n", " 1\n", " skewness\n", - " 10\n", + " 3\n", " 0.0\n", - " 0.993333\n", - " 0.967742\n", - " 4.091533\n", + " 0.970000\n", + " 0.933333\n", + " 2.669152\n", " \n", " \n", " 2\n", " skewness\n", - " 3\n", - " 0.1\n", - " 0.991111\n", - " 0.952381\n", - " 3.518106\n", + " 10\n", + " 0.0\n", + " 0.953333\n", + " 0.915254\n", + " 2.471844\n", " \n", " \n", " 3\n", - " skewness\n", + " top_k_mean\n", " 3\n", " 0.0\n", - " 0.990000\n", - " 0.952381\n", - " 3.699453\n", + " 0.904444\n", + " 0.885246\n", + " 1.833128\n", " \n", " \n", " 4\n", - " max_score\n", + " skewness\n", " 5\n", - " 0.0\n", - " 0.978889\n", - " 0.950820\n", - " 3.084493\n", + " 0.1\n", + " 0.894444\n", + " 0.918033\n", + " 1.067583\n", " \n", " \n", " 5\n", - " top_k_mean\n", + " max_score\n", " 3\n", - " 0.1\n", - " 0.977778\n", - " 0.967742\n", - " 3.555129\n", + " 0.0\n", + " 0.891111\n", + " 0.857143\n", + " 1.714937\n", " \n", " \n", " 6\n", " top_k_mean\n", - " 3\n", + " 5\n", " 0.0\n", - " 0.977778\n", - " 0.967742\n", - " 3.419085\n", + " 0.890000\n", + " 0.878788\n", + " 1.714391\n", " \n", " \n", " 7\n", - " top_k_mean\n", - " 10\n", - " 0.0\n", - " 0.976667\n", - " 0.933333\n", - " 2.928998\n", + " skewness\n", + " 3\n", + " 0.1\n", + " 0.886667\n", + " 0.900000\n", + " 1.170104\n", " \n", " \n", " 8\n", - " top_k_mean\n", - " 5\n", - " 0.0\n", - " 0.976667\n", - " 0.952381\n", - " 3.349176\n", + " max_score\n", + " 3\n", + " 0.1\n", + " 0.880000\n", + " 0.857143\n", + " 1.721444\n", " \n", " \n", " 9\n", - " max_score\n", + " top_k_mean\n", " 3\n", " 0.1\n", - " 0.975556\n", - " 0.967742\n", - " 3.656795\n", + " 0.868889\n", + " 0.848485\n", + " 1.631476\n", " \n", " \n", "\n", @@ -2653,19 +2653,19 @@ ], "text/plain": [ " aggregator top_k min_score_to_consider roc_auc f1 cohens_d\n", - "0 skewness 5 0.0 0.995556 0.966667 4.111937\n", - "1 skewness 10 0.0 0.993333 0.967742 4.091533\n", - "2 skewness 3 0.1 0.991111 0.952381 3.518106\n", - "3 skewness 3 0.0 0.990000 0.952381 3.699453\n", - "4 max_score 5 0.0 0.978889 0.950820 3.084493\n", - "5 top_k_mean 3 0.1 0.977778 0.967742 3.555129\n", - "6 top_k_mean 3 0.0 0.977778 0.967742 3.419085\n", - "7 top_k_mean 10 0.0 0.976667 0.933333 2.928998\n", - "8 top_k_mean 5 0.0 0.976667 0.952381 3.349176\n", - "9 max_score 3 0.1 0.975556 0.967742 3.656795" + "0 skewness 5 0.0 0.976667 0.935484 2.755040\n", + "1 skewness 3 0.0 0.970000 0.933333 2.669152\n", + "2 skewness 10 0.0 0.953333 0.915254 2.471844\n", + "3 top_k_mean 3 0.0 0.904444 0.885246 1.833128\n", + "4 skewness 5 0.1 0.894444 0.918033 1.067583\n", + "5 max_score 3 0.0 0.891111 0.857143 1.714937\n", + "6 top_k_mean 5 0.0 0.890000 0.878788 1.714391\n", + "7 skewness 3 0.1 0.886667 0.900000 1.170104\n", + "8 max_score 3 0.1 0.880000 0.857143 1.721444\n", + "9 top_k_mean 3 0.1 0.868889 0.848485 1.631476" ] }, - "execution_count": null, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2691,13 +2691,13 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 21, "id": "20e57208", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -2746,6 +2746,7 @@ }, { "cell_type": "markdown", + "id": "c7c28001", "metadata": {}, "source": [ "### What we found\n", @@ -2757,8 +2758,7 @@ "**The noise floor deserves a sweep, not an assumption.** `min_score_to_consider` affects `skewness` far more than the other aggregators, and there is a concrete reason. `skewness` computes `(mean - median) / std` over the *entire* array of segment scores, zeros included, while the other five first filter with `scores[scores > 0]`. Raising the floor therefore reshapes the distribution that `skewness` sees, whereas for the others it simply removes inputs. Its best value also interacts with `top_k`, so sweep the two together rather than tuning either on its own.\n", "\n", "**Do not over-read small gaps.** With 30 episodes per class, differences of a few thousandths of ROC-AUC sit inside the margin of error: re-running against a different sample can reorder the leading aggregators. Read the table as \"this group is clearly ahead of that group\" rather than as an exact ranking, and prefer a configuration surrounded by other strong cells in the heatmap over the single best cell, which may just be lucky. Raising the episode counts is the way to tighten this up." - ], - "id": "c7c28001" + ] }, { "cell_type": "markdown",