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from fasthtml.common import * |
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from fasthtml.components import * |
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from plotly import graph_objects as go |
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from fh_plotly import plotly2fasthtml |
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import pandas as pd |
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import json |
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from data_viewer import view_data, gen_random_id |
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from rich import print |
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import uuid |
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import plotly.express as px |
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overview = Div( |
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H2("Curated Source Processing Overview"), |
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H3("What This Section Contains"), |
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P("This section provides a complete discussion on the filtering applied to the 14 curated sources that comprise the non-web data section of TxT360. The section is split into the following topic areas: "), |
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Ul( |
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Li("Curated Sources Data Processing Summary", style = "margin-bottom: 5px"), |
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Li("Individual Filtering Discussion for Each Source", style = "margin-bottom: 5px"), |
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), |
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), |
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overview_text = P("Curated sources comprise high-quality datasets that contain domain-specificity. These sources, such as Arxiv, Wikipedia, and Stack Exchange, provide valuable data that is excluded from the web dataset mentioned above. Analyzing and processing non-web data can yield insights and opportunities for various applications. Details about each of the sources are provided below. ") |
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copyright_disclaimer = P("We respect the copyright of the data sources and have not included the controversial data that was used in Pile like YouTube and Opensubtitles, Reddit threads, and books.") |
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treemap_data = { |
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'Source': ['ArXiv', 'PubMed Central', 'PubMed Abstract', 'S2ORC Full Text', 'S2ORC Abstract', 'PhilPapers', 'Wikipedia', 'StackExchange', 'EuroParl', 'Ubuntu IRC', 'Freelaw', 'PG19', 'USPTO', 'HackerNews', 'DM Maths'], |
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'Category': ['Papers', 'Papers', 'Papers', 'Papers', 'Papers', 'Papers', 'Internet', 'Conversational', 'Legal/Formal', 'Conversational', 'Legal/Formal', 'Books', 'Legal/Formal', 'Conversational', 'Reasoning'], |
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'Count': [100, 200, 150, 120, 80, 90, 300, 250, 180, 150, 150, 250, 180, 120, 90], |
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'Details': [ |
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'A repository of scientific papers in various disciplines, including computer science, physics, mathematics, and more.', |
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'A database of biomedical and life sciences research articles.', |
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'Abstracts of biomedical literature from various sources.', |
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'Full-text articles from the Semantic Scholar Open Research Corpus.', |
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'Abstracts of articles from the Semantic Scholar Open Research Corpus.', |
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'Papers from the PhilPapers database, a comprehensive index and bibliography of philosophy research.', |
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'A collaborative online encyclopedia that covers a wide range of topics.', |
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'A network of question-and-answer websites on various subjects, including programming, science, mathematics, and more.', |
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'A collection of multilingual parallel corpora of parliamentary debates from the European Parliament.', |
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'Chat logs from the Ubuntu Internet Relay Chat (IRC) channels.', |
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'Legal documents and court cases from various jurisdictions.', |
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'A collection of books from Project Gutenberg, a digital library of public domain works.', |
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'Patent documents from the United States Patent and Trademark Office.', |
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'User-generated news and discussion platform focused on technology and startups.', |
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'Deep Mind Maths dataset with generated questions.' |
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] |
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} |
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total_count = sum(treemap_data['Count']) |
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treemap_data['Percentage'] = [count / total_count * 100 for count in treemap_data['Count']] |
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fig = px.treemap(treemap_data, path=['Category', 'Source'], values='Count', hover_data=['Details', 'Percentage'], hover_name='Source') |
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treemap_chart = fig |
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wikipedia_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"Wikipedia", |
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], |
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"Lines Downloaded": [ |
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"61614907", |
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], |
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"Percent Removed After Language Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"1.86%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_wikipedia = wikipedia_filter.to_html(index=False, border=0) |
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table_div_wikipedia = Div(NotStr(table_html_wikipedia), style="margin: 40px;") |
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freelaw_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"FreeLaw", |
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], |
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"Lines Downloaded": [ |
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"75971288", |
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], |
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"Percent Removed After Language Filter": [ |
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"3.00%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"7.49%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.07%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_freelaw = freelaw_filter.to_html(index=False, border=0) |
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table_div_freelaw = Div(NotStr(table_html_freelaw), style="margin: 40px;") |
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dmm_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"DM Math", |
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], |
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"Lines Downloaded": [ |
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"112559888", |
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], |
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"Percent Removed After Language Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_dmm = dmm_filter.to_html(index=False, border=0) |
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table_div_dmm = Div(NotStr(table_html_dmm), style="margin: 40px;") |
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uspto_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"USPTO", |
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], |
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"Lines Downloaded": [ |
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"6880276", |
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], |
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"Percent Removed After Language Filter": [ |
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"0.02%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"1.88%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.01%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_uspto = uspto_filter.to_html(index=False, border=0) |
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table_div_uspto = Div(NotStr(table_html_uspto), style="margin: 40px;") |
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pg19_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"PG-19", |
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], |
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"Lines Downloaded": [ |
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"28752", |
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], |
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"Percent Removed After Language Filter": [ |
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"0.24%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.17%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_pg19 = pg19_filter.to_html(index=False, border=0) |
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table_div_pg19 = Div(NotStr(table_html_pg19), style="margin: 40px;") |
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hn_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"HackerNews", |
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], |
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"Lines Downloaded": [ |
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"2064931", |
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], |
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"Percent Removed After Language Filter": [ |
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"2.62%%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"0.02%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.34%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_hn = hn_filter.to_html(index=False, border=0) |
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table_div_hn = Div(NotStr(table_html_hn), style="margin: 40px;") |
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uirc_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"Ubunutu IRC", |
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], |
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"Lines Downloaded": [ |
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"37966", |
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], |
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"Percent Removed After Language Filter": [ |
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"38.10%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"0.14%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"1.12%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_uirc = uirc_filter.to_html(index=False, border=0) |
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table_div_uirc = Div(NotStr(table_html_uirc), style="margin: 40px;") |
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up_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"EuroParl", |
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], |
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"Lines Downloaded": [ |
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"69814", |
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], |
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"Percent Removed After Language Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_up = up_filter.to_html(index=False, border=0) |
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table_div_up = Div(NotStr(table_html_up), style="margin: 40px;") |
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se_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"StackExchange", |
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], |
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"Lines Downloaded": [ |
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"23246548", |
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], |
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"Percent Removed After Language Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_se = se_filter.to_html(index=False, border=0) |
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table_div_se = Div(NotStr(table_html_se), style="margin: 40px;") |
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arx_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"ArXiv", |
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], |
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"Lines Downloaded": [ |
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"1911867", |
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], |
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"Percent Removed After Language Filter": [ |
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"2.22%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"5.65%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.07%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_arx = arx_filter.to_html(index=False, border=0) |
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table_div_arx = Div(NotStr(table_html_arx), style="margin: 40px;") |
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s2o_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"S2ORC", |
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], |
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"Lines Downloaded": [ |
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"12963563", |
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], |
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"Percent Removed After Language Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_s2o = s2o_filter.to_html(index=False, border=0) |
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table_div_s2o = Div(NotStr(table_html_s2o), style="margin: 40px;") |
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med_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"PubMed - Central", |
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], |
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"Lines Downloaded": [ |
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"5230932", |
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], |
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"Percent Removed After Language Filter": [ |
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"7.66%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"1.29%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.02%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_med = med_filter.to_html(index=False, border=0) |
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table_div_med = Div(NotStr(table_html_med), style="margin: 40px;") |
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phil_filter = pd.DataFrame( |
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{ |
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"Dataset": [ |
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"Phil Papers", |
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], |
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"Lines Downloaded": [ |
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"49389", |
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], |
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"Percent Removed After Language Filter": [ |
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"20.68%", |
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], |
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"Percent Removed After Min Word Count Filter": [ |
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"0.00%", |
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], |
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"Percent Removed After Unigram Probability Filter": [ |
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"0.12%", |
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], |
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"Percent Removed After Local Dedup": [ |
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"", |
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], |
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"Total Percentage Remaining": [ |
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"98.14%", |
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], |
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} |
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) |
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table_html_phil = phil_filter.to_html(index=False, border=0) |
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table_div_phil = Div(NotStr(table_html_phil), style="margin: 40px;") |
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filtering_process = Div( |
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Section( |
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P("This section contains the specific steps taken to filter all 14 curated source datasets.") |
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), |
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Section( |
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H3("Wikipedia"), |
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H4("Download and Extraction"), |
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Ol( |
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Li("Downloaded from Wikimedia official dump of wikipedia on huggingface https://huggingface.co/datasets/wikimedia/wikipedia/tree/main"), |
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Li("Data is originally in parqet format so we use huggingface dataset.to_json function to convert it to the jsonl format"), |
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), |
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H4("Filtering"), |
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Ol( |
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Li("As we expect the dataset to be already of high quality so only one filter is applied which is to remove all documents (articles) with less than 10 words (not inclusive)"), |
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), |
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H4("Local Deduplication Process"), |
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Ol( |
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Li("Whole wikipedia was deduped using minhash generation following Slim pajama code"), |
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), |
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H4("Global Deduplication Process"), |
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Ol( |
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Li("After local dedup, remaining wikipedia was deduped again with all the datasets combined"), |
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), |
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table_div_wikipedia, |
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|
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), |
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Section( |
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H3("ArXiv"), |
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H4("Download and Extraction"), |
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Ol( |
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Li("All the data was downloaded in original latex format from Arxiv official S3 dump s3://arxic/src"), |
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Li("We try to encode the downloaded data into utf-8 or guess encoding using chardet library"), |
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Li("After that pandoc was used to extract information from the latex files and saved as markdown format - code: pandoc -s {tex} -o out/{out_name}.md --wrap=none"), |
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Li("All markdowns were combined to create jsonl files"), |
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), |
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H4("Filtering"), |
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P("Multiple filters are used here after manually verifying output of all the filters as suggested by peS2o dataset"), |
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Ol( |
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Li("min_word: less than 500 words (not inclusive) are discarded"), |
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Li("Language: any language other than English are discarded"), |
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Li("Frequency: The most frequent word in the paper consists of alpha characters only, and it appears in less than 7.5% of the document. Words are obtained by splitting the text on whitespace."), |
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Li("Unigram log probablity: Must have higher than -20 average unigram log probability. To calculate the average log word probability, we use word frequencies extracted from the 1T Web Ngram corpus; specifically, we use the list available created by Rachel Tatman. A copy is hosted here."), |
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Li("number 4 above had hyperlinks that need to be included"), |
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), |
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H4("Local Deduplication Process"), |
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Ol( |
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Li("Local dedup was done with all papers combined."), |
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), |
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H4("Global Deduplication Process"), |
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Ol( |
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Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup."), |
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), |
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table_div_arx, |
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), |
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Section( |
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H3("S2ORC"), |
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H4("Download and Extraction"), |
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Ol( |
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Li("This was downloaded directly in zip format using S2ORC api key and normal get request. code: response = urllib.request.urlopen(url)"), |
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Li("There were two kind of datasets that was downloaded S2ORC and S2ORC abstract"), |
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), |
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H4("Filtering - S2ORC"), |
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P("1. Multiple filters are used here after manually verifying output of all the filters as suggested by peS2o dataset"), |
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Ol( |
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Li("title_abstract: must have title and abstract"), |
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Li("The paper must be in English. To determine the language of each document, we use the pycld3 library. We run pycld3 on the first 2000 characters of each paragraph in the paper. The language of the paper is the most common language of the paragraphs."), |
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Li("word_count: less than 500 words (not inclusive) are discarded"), |
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Li("paragraph_count: The paper must have at least 5 paragraphs after removing paragraphs with less than -20 average log world probability"), |
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Li("frequency: The most frequent word in the paper consists of alpha characters only, and it appears in less than 7.5% of the document. Words are obtained by splitting the text on whitespace."), |
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), |
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H4("Filtering - S2ORC Abstract"), |
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P("1. Multiple filters are used here after manually verifying output of all the filters as suggested by peS2o dataset. The frequency filter was not used as suggested by peS2o because it was removing good samples as inspected manually"), |
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Ol( |
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Li("title_abstract: must have title and abstract"), |
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Li("language: abstract must be in English"), |
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Li("word_count: less than 20 (not inclusive) are discarded"), |
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Li("Unigram log probablity"), |
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Li("frequency: The most frequent word in the paper consists of alpha characters only, and it appears in less than 7.5% of the document. Words are obtained by splitting the text on whitespace."), |
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), |
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H4("Local Deduplication Process"), |
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Ol( |
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Li("Local dedup was done with all papers combined."), |
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), |
|
H4("Global Deduplication Process"), |
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Ol( |
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Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup"), |
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), |
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table_div_s2o, |
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), |
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Section( |
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H3("PubMed"), |
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H4("Download and Extraction"), |
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Ol( |
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Li("First all the urls of PMC and PMA files are parsed and stored as text file from FTP server https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_package/"), |
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Li("All the urls are downloaded and the downloaded data is in xml.tar format"), |
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Li("For pubmed central First tar files are opened using tarfile library and then converted to markdown format using pandoc: pandoc -f jats {nxml} -o {pmcid}.md --wrap=none"), |
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Li("All the markdown files are combined to create jsonl files. In jsonl files, 1 line correspond to 1 markdown file."), |
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Li("For pubmed abstract, the XML files are in very simple format and beautiful soup is directly used to extract the abstract, title and pmid and stored in jsonl format"), |
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), |
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H4("Filtering"), |
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P("1. Multiple filters are used here after manually verifying output of all the filters as suggested by peS2o dataset."), |
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Ol( |
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Li("min_word: less than 100 words (not inclusive) are discarded, less than 20 words for pubmed abstract"), |
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Li("Language: any language other than English are discarded"), |
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Li("Frequency: The most frequent word in the paper consists of alpha characters only, and it appears in less than 7.5% of the document. Words are obtained by splitting the text on whitespace. This filter is not used for pubmed abstract"), |
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Li("Unigram log probablity: Must have higher than -20 average unigram log probability. To calculate the average log word probability, we use word frequencies extracted from the 1T Web Ngram corpus; specifically, we use the list available created by Rachel Tatman. A copy is hosted here."), |
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Li("need to add the hyperlinks for the section above"), |
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), |
|
H4("Local Deduplication Process"), |
|
Ol( |
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Li("Local dedup was done with all papers combined."), |
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), |
|
H4("Global Deduplication Process"), |
|
Ol( |
|
Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup."), |
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), |
|
table_div_med, |
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), |
|
Section( |
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H3("Phil Papers"), |
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H4("Download and Extraction"), |
|
Ol( |
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Li("Original pdf files download location was downloaded from https://philarchive.org/oai.pl "), |
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Li("All pdf files were downloaded"), |
|
Li("Pdf was converted to text using java -jar ../philpapers_resources/src/pdfbox-app-2.0.21.jar ExtractText {f0} {FOUT.name}"), |
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Li("Language was detected and added using langdetect library"), |
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), |
|
H4("Filtering"), |
|
Ol( |
|
Li("Many filters were used to clean the phil papers like double whitespaces, new lines etc. All filter details are here: https://github.com/thoppe/The-Pile-PhilPapers/blob/master/pdf_filter.py"), |
|
), |
|
H4("Local Deduplication Process"), |
|
Ol( |
|
Li("Local dedup was done with all papers combined."), |
|
), |
|
H4("Global Deduplication Process"), |
|
Ol( |
|
Li("This data was part of paper domain which are combined together and minhash was generated and deduped together with all the datasets after doing local dedup."), |
|
), |
|
table_div_phil, |
|
), |
|
Section( |
|
H3("Europarl"), |
|
H4("Download and Extraction"), |
|
Ol( |
|
Li("Original data was downloaded from http://www.statmt.org/europarl/v7/europarl.tgz"), |
|
Li("Finally the remaining files are converted to jsonl lines"), |
|
), |
|
H4("Filtering"), |
|
Ol( |
|
Li("Smaller than 200 characters of documents are removed while downloading so no others filtered were run"), |
|
Li("Tags were also removed while downloading"), |
|
), |
|
H4("Local Deduplication Process"), |
|
Ol( |
|
Li("Local dedup was done within europarl itself"), |
|
), |
|
H4("Global Deduplication Process"), |
|
Ol( |
|
Li("After local dedup, remaining europarl was deduped again with all the datasets combined"), |
|
), |
|
table_div_up, |
|
), |
|
Section( |
|
H3("HackerNews"), |
|
H4("Download and Extraction"), |
|
Ol( |
|
Li("Data was parsed using hackernews story ids starting using https://hacker-news.firebaseio.com/v0/item/"), |
|
Li("Story ids was started from 1 till 37500000 (all stories that gives error while pinging the url was removed). Each post is a story, with each reply another story"), |
|
Li("As there were too many requests error, there was a wait(2 sec) statement included in the code"), |
|
Li("As the number of stories were large and containing all the replies was time consuming and possibility of introducing too much error, only longest depth threads were included from 3rd level onwards. So we include the title then all the replies (2nd level) but replies to those replies (3rd level) were only the ones which has maximum depth."), |
|
), |
|
H4("Filtering"), |
|
Ol( |
|
Li("Min word: 10"), |
|
Li("Language: Only english"), |
|
Li("Unigram log probablity"), |
|
), |
|
H4("Local Deduplication Process"), |
|
Ol( |
|
Li("Local dedup was done within hackernews itself"), |
|
), |
|
H4("Global Deduplication Process"), |
|
Ol( |
|
Li("After local dedup, remaining data was deduped again with all the datasets combined"), |
|
), |
|
table_div_hn, |
|
), |
|
Section( |
|
H3("USPTO"), |
|
H4("Download and Extraction"), |
|
Ol( |
|
Li("Data was downloaded and extracted using tags from https://bulkdata.uspto.gov/data/patent/grant/redbook/fulltext/"), |
|
Li("There were three different format that needed three different functions to download and extract the data based on year: Pre_2002, 2002_to_2004, post_2004"), |
|
|
|
), |
|
H4("Filtering"), |
|
Ol( |
|
Li("Min word: 50"), |
|
Li("Language: Only english"), |
|
Li("Unigram log probablity"), |
|
), |
|
H4("Local Deduplication Process"), |
|
Ol( |
|
Li("Local dedup was done within USPTO itself"), |
|
), |
|
H4("Global Deduplication Process"), |
|
Ol( |
|
Li("After local dedup, remaining data was deduped again with all the datasets combined"), |
|
), |
|
table_div_uspto, |
|
), |
|
Section( |
|
H3("FreeLaw"), |
|
H4("Download and Extraction"), |
|
Ol( |
|
Li("CSV format bulk data was downloaded from https://storage.courtlistener.com/bulk-data/"), |
|
Li("They have multiple dumps as shown below with lot of duplicates (exact number is given in the table at the top)"), |
|
Li("there is an image to show here!"), |
|
Li("As these are csv files, they have multiple columns where text can be present, so we extracted text from the following columns using html2text function which just convert and extract tags from html tags"), |
|
Li("image to show"), |
|
Li("Text was also extracted from row named 'plain_text'"), |
|
Li("Priority is always given to plain_text first then from 6 to 1 in the subsequent order following pile logic"), |
|
), |
|
H4("Filtering"), |
|
Ol( |
|
Li("Min word: 50"), |
|
Li("Language: Only english"), |
|
Li("Unigram log probablity"), |
|
), |
|
H4("Local Deduplication Process"), |
|
Ol( |
|
Li("Local dedup was done within freelaw itself which removed 90%+ duplicates"), |
|
), |
|
H4("Global Deduplication Process"), |
|
Ol( |
|
Li("After local dedup, remaining data was deduped again with all the datasets combined"), |
|
), |
|
table_div_freelaw, |
|
), |
|
Section( |
|
H3("StackExchange"), |
|
H4("Download and Extraction"), |
|
Ol( |
|
Li("Archive dump was used to download data from all the stackexchange sub urls, eg., math.stackexchange etc."), |
|
Li("Raw data is in XML format with lot of metadata. We only used two files Posts.xml and Comments.xml"), |
|
Li("We parsed using post_id to connect each question to answer and then to comments so our data has same hierarchy as stackexchange UI"), |
|
Li(""" |
|
1. Questions: |
|
2. Comment1: |
|
3. Comment2: |
|
4. Answer1: |
|
5. Comment1: |
|
6. Comment2: |
|
7. Answer2: |
|
8. Comment1: |
|
9. Comment2: |
|
"""), |
|
), |
|
H4("Filtering"), |
|
Ol( |
|
Li("Min word: 10"), |
|
), |
|
H4("Local Deduplication Process"), |
|
Ol( |
|
Li("Local dedup was done within stackexchange itself"), |
|
), |
|
H4("Global Deduplication Process"), |
|
Ol( |
|
Li("After local dedup, remaining data was deduped again with all the datasets combined"), |
|
), |
|
table_div_se, |
|
), |
|
Section( |
|
H3("Ubuntu IRC"), |
|
H4("Download and Extraction"), |
|
Ol( |
|
Li("All the data was downloaded from https://irclogs.ubuntu.com/{date.year}/{date.month:02d}/{date.day:02d}/ based on the year"), |
|
Li("During extraction, we cleaned the logs using following functions"), |
|
Li("image here"), |
|
), |
|
H4("Filtering"), |
|
Ol( |
|
Li("Min word: 10"), |
|
Li("Language: Only english"), |
|
Li("Unigram log probablity"), |
|
), |
|
H4("Local Deduplication Process"), |
|
Ol( |
|
Li("Local dedup was done within Ubuntu IRC itself"), |
|
), |
|
H4("Global Deduplication Process"), |
|
Ol( |
|
Li("After local dedup, remaining data was deduped again with all the datasets combined"), |
|
), |
|
table_div_uirc, |
|
), |
|
Section( |
|
H3("DM Maths"), |
|
H4("Download and Extraction"), |
|
Ol( |
|
Li("Directly downloaded from hugging-face dump dm_maths/"), |
|
Li("Data was converted in jsonl format where each lines are : Question: TEXT Answer: TEXT"), |
|
), |
|
H4("Filtering"), |
|
Ol( |
|
Li("None"), |
|
), |
|
H4("Local Deduplication Process"), |
|
Ol( |
|
Li("None"), |
|
), |
|
H4("Global Deduplication Process"), |
|
Ol( |
|
Li("None"), |
|
), |
|
table_div_dmm, |
|
), |
|
Section( |
|
H3("PG19"), |
|
H4("Download and Extraction"), |
|
Ol( |
|
Li("Directly downloaded from hugging-face dump pg19/"), |
|
), |
|
H4("Filtering"), |
|
Ol( |
|
Li("Min word: 20"), |
|
Li("Language: ???"), |
|
Li("Unigram log probablity"), |
|
), |
|
H4("Local Deduplication Process"), |
|
Ol( |
|
Li("Local dedup was done within PG19 itself"), |
|
), |
|
H4("Global Deduplication Process"), |
|
Ol( |
|
Li("After local dedup, remaining data was deduped again with all the datasets combined"), |
|
), |
|
table_div_pg19, |
|
), |
|
) |
|
|
|
|
|
|
|
|
|
|
|
|
|
local_dedup_text = P("Each curated data source has been prepared using its specific rules and has been locally deduped using min-hash near deduplication. Details about the dataset are shown below in the table:") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
data_pipeline_table = pd.DataFrame( |
|
{ |
|
"Data Source": [ |
|
"Papers", |
|
"Wikipedia", |
|
"StackExchange", |
|
"Europarl", |
|
"Ubuntu IRC", |
|
"HackerNews", |
|
"PG-19", |
|
"USPTO", |
|
"Freelaw", |
|
"DM Math", |
|
], |
|
"Percent Filtered": [ |
|
"15%", |
|
"21%", |
|
"<0.1%", |
|
"1%", |
|
"0.4%", |
|
"60%", |
|
"0.8%", |
|
"22.5%", |
|
"94%", |
|
"0", |
|
], |
|
"Unique Document Percentage": [ |
|
"75.99%", |
|
"91.91%", |
|
"98.02%", |
|
"98.87%", |
|
"100%", |
|
"99.91%", |
|
"31.81%", |
|
"99.94%", |
|
"91.01%", |
|
"0", |
|
], |
|
"2 - 5 Duplicates": [ |
|
"19.4%", |
|
"4.7%", |
|
"1.27%", |
|
"0.94%", |
|
"0", |
|
"0.05%", |
|
"20.03%", |
|
"0.05%", |
|
"6,87%", |
|
"0", |
|
], |
|
"6 - 10 Duplicates": [ |
|
"2.89%", |
|
"1.58%", |
|
"0.35%", |
|
"0.09%", |
|
"0", |
|
"0.02%", |
|
"24.27%", |
|
"0.01%", |
|
"1.07%", |
|
"0", |
|
], |
|
"11 - 100 Duplicates": [ |
|
"1.17%", |
|
"1.76%", |
|
"0.35%", |
|
"0.1", |
|
"0", |
|
"0.02%", |
|
"22.26%", |
|
"0.01%", |
|
"1.05%", |
|
"0", |
|
], |
|
"101 - 1000 Duplicates": [ |
|
"0.01%", |
|
"0.05%", |
|
"0.01%", |
|
"0", |
|
"0", |
|
"<0.01%", |
|
"1.58%", |
|
"<0.01%", |
|
"0.01%", |
|
"0", |
|
], |
|
"1001+ Duplicates": [ |
|
"<0.01%", |
|
"<0.01%", |
|
"<0.01%", |
|
"0", |
|
"0", |
|
"<0.01%", |
|
"0.06%", |
|
"0", |
|
"0", |
|
"0", |
|
], |
|
} |
|
) |
|
|
|
table_html_data_pipe = data_pipeline_table.to_html(index=False, border=0) |
|
table_div_data_pipe = Div(NotStr(table_html_data_pipe), style="margin: 40px;") |
|
|
|
data_descriptions = pd.DataFrame( |
|
{ |
|
"Source": [ |
|
"Papers - ArXiv", |
|
"Papers - PhilPapers", |
|
"Papers - S2ORC", |
|
"Papers - PubMed Central", |
|
"Papers - PubMed Abstract", |
|
"Wikipedia", |
|
"StackExchange", |
|
"EuroParl", |
|
"Ubuntu IRC", |
|
"Freelaw", |
|
"PG-19", |
|
"USPTO", |
|
"HackerNews", |
|
"DM Maths", |
|
], |
|
"Description": [ |
|
"The ArXiv dataset is a vast collection of preprint research papers primarily in Mathematics, Computer Science, and Physics. Established in 1991, it offers high-quality text and mathematical knowledge, making it an invaluable resource for academic and scientific research. ArXiv papers are typically written in LaTeX, a popular typesetting system for these fields. We have extracted the information from latex and converted it into a text format.", |
|
"Papers from the PhilPapers database, a comprehensive index and bibliography of philosophy research maintained by the Center for Digital Philosophy at the University of Western Ontario.", |
|
"The Semantic Scholar Open Research Corpus (S2ORC) is a comprehensive dataset designed for natural language processing (NLP) and text-mining research over scientific papers. It includes rich metadata, and abstract and full-text content for millions of academic papers across various disciplines. This dataset is further divided into two components, S2ORC abstract and S2ORC full text.", |
|
"The PubMed Central (PMC) dataset is a comprehensive collection of full-text biomedical and life sciences journal articles run by the United States of America’s National Center for Biotechnology Information (NCBI). It provides open access to a wealth of scientific literature, facilitating research and discovery in the medical and biological fields starting from 2008 by the NIH Public Access Policy. Articles in PMC are available for text mining and other secondary analyses, making it an invaluable resource for researchers and developers and other downstream tasks.", |
|
"Abstracts of more than 30 million publications of biomedical literature from various sources mainly including biomedical articles run by the National Library of Medicine. ", |
|
"Wikipedia is an encyclopedia form of high-quality text data used for language modeling. We have included filtered and deduplicated versions of complete Wikipedia data directly provided by the Wikipedia Foundation for more than 350 languages.", |
|
"A network of question-and-answer websites on various subjects, including programming, science, mathematics, and more. This is one of the largest publicly available repositories for question-answer pairs. We have included comments also to include an overall discussion on each post.", |
|
"A collection of multilingual parallel corpora of parliamentary debates from the European Parliament. This is a high-quality legacy dataset earlier used for translation tasks.", |
|
"Chat logs from the Ubuntu Internet Relay Chat (IRC) channels on the Freenode IRC chat server. This data is also another form of dialog dataset on niche topics.", |
|
"Legal documents and court cases from various jurisdictions provided by US-registered non-profit firm Free Law Project. We have included data from CourtListener which included millions of legal opinions from federal and state courts.", |
|
"A collection of books from Project Gutenberg, a digital library of public domain works. This contains all the books that were published before 1919.", |
|
"Patent documents from the United States Patent and Trademark Office.", |
|
"High-quality dialog-based dataset where user comments on the links as the head post aggregated by Y Combinator.", |
|
"DeepMind Maths dataset with generated questions from various topics like algebra, calculus, geometry, etc. Maths data is included to improve model reasoning abilities in the downstream tasks.", |
|
], |
|
|
|
} |
|
) |
|
|
|
table_html_desc = data_descriptions.to_html(index=False, border=0) |
|
table_desc = Div(NotStr(table_html_desc), style="margin: 40px;") |
|
|
|
|
|
data_sources = [ |
|
"Freelaw", |
|
"Wikipedia", |
|
"PhilPapers", |
|
"Arxiv", |
|
"S2ORC", |
|
"S2ORC Abstract", |
|
"Pubmed", |
|
"USPTO", |
|
"Hackernews", |
|
"Ubuntu IRC", |
|
"StackExchange", |
|
"DM Maths", |
|
"PG19", |
|
"Europarl", |
|
] |
|
|
|
|
|
def get_data(data_source: str = "Freelaw", doc_id: int = 3, target: str = "foo"): |
|
doc_id = max(0, min(int(doc_id), 9)) |
|
|
|
if data_source == "Freelaw": |
|
raw_sample_doc = json.load(open("data/curated_samples/freelaw_raw.json")) |
|
extracted_sample_doc = json.load( |
|
open("data/curated_samples/freelaw_extract.json") |
|
) |
|
elif data_source == "Wikipedia": |
|
raw_sample_doc = extracted_sample_doc = json.load( |
|
open("data/curated_samples/wiki.json") |
|
) |
|
elif data_source == "StackExchange": |
|
raw_sample_doc = json.load(open("data/curated_samples/stackexchange_raw.json")) |
|
extracted_sample_doc = json.load( |
|
open("data/curated_samples/stackexchange_extract.json") |
|
) |
|
elif data_source == "PhilPapers": |
|
raw_sample_doc = extracted_sample_doc = json.load( |
|
open("data/curated_samples/philpapers_raw.json") |
|
) |
|
elif data_source == "Arxiv": |
|
raw_sample_doc = json.load(open("data/curated_samples/arxiv_raw.json")) |
|
extracted_sample_doc = json.load( |
|
open("data/curated_samples/arxiv_extract.json") |
|
) |
|
elif data_source == "S2ORC": |
|
raw_sample_doc = extracted_sample_doc = json.load( |
|
open("data/curated_samples/s2orc_raw.json") |
|
) |
|
elif data_source == "S2ORC Abstract": |
|
raw_sample_doc = extracted_sample_doc = json.load( |
|
open("data/curated_samples/s2orc_abstract_raw.json") |
|
) |
|
elif data_source == "Pubmed": |
|
raw_sample_doc = json.load(open("data/curated_samples/pubmed_raw.json")) |
|
extracted_sample_doc = json.load( |
|
open("data/curated_samples/pubmed_extract.json") |
|
) |
|
elif data_source == "DM Maths": |
|
raw_sample_doc = json.load(open("data/curated_samples/dm_maths_raw.json")) |
|
extracted_sample_doc = json.load( |
|
open("data/curated_samples/dm_maths_extract.json") |
|
) |
|
elif data_source == "PG19": |
|
raw_sample_doc = extracted_sample_doc = json.load( |
|
open("data/curated_samples/pg19_raw.json") |
|
) |
|
elif data_source == "Europarl": |
|
raw_sample_doc = extracted_sample_doc = json.load( |
|
open("data/curated_samples/europarl_raw.json") |
|
) |
|
else: |
|
raw_sample_doc = extracted_sample_doc = [{} for _ in range(10)] |
|
|
|
raw_json = raw_sample_doc[doc_id] |
|
extracted_json = extracted_sample_doc[doc_id] |
|
return view_data( |
|
raw_json, |
|
extracted_json, |
|
doc_id=doc_id, |
|
data_source=data_source, |
|
data_sources=data_sources, |
|
target=target, |
|
) |
|
|
|
|
|
def update(target: str, request): |
|
params = request.query_params |
|
if data_source := params.get(f"data_source_{target}"): |
|
return get_data( |
|
data_source, params.get(f"doc_id_{target}", 3), target) |
|
if doc_id := params.get(f"doc_id_{target}"): |
|
return get_data( |
|
params.get(f"data_source_{target}"), doc_id, target) |
|
|
|
|
|
data = { |
|
'Filter': ['Downloaded Lines', 'Language Filter', 'Min Word Count', 'Unigram Log Probability'], |
|
'Wikipedia': [61614907, 61614907, 60468491, 60468491], |
|
'Freelaw': [75971288, 73690766, 68171834, 68123174], |
|
'DM Maths': [112559888, 112559888, 112559888, 112559888], |
|
'USPTO': [6880276, 6878964, 6749922, 6749389], |
|
'PG19': [28752, 28683, 28682, 28632], |
|
'Hackernews': [2064931, 2010802, 2010488, 2003636], |
|
'Ubuntu IRC': [37966, 23501, 23468, 23205], |
|
'Europarl': [69814, 69814, 69814, 69814], |
|
'StackExchange': [23246548, 23246548, 23246352, 23246352], |
|
'Arxiv': [1911867, 1869441, 1763840, 1762661], |
|
'S2ORC': [12963563, 12963563, 12963563, 12963563], |
|
'S2ORC Abstract': [102324176, 83867601, 82889293, 82777912], |
|
'Pubmed Central': [5230932, 4830486, 4768310, 4767474], |
|
'Pubmed Abstract': [25787474, 25784374, 25747955, 25746724], |
|
'Phil Papers': [49389, 39175, 39175, 39128] |
|
} |
|
|
|
|
|
df = pd.DataFrame(data) |
|
|
|
|
|
fig = go.Figure() |
|
|
|
|
|
for dataset in df.columns[1:]: |
|
fig.add_trace(go.Bar( |
|
name=dataset, |
|
x=df['Filter'], |
|
y=df[dataset] |
|
)) |
|
|
|
|
|
fig.update_layout( |
|
barmode='stack', |
|
title='Stacked Bar Chart of Line Reductions by Filter for Each Dataset', |
|
xaxis_title='Filter', |
|
yaxis_title='Number of Lines', |
|
legend_title='Dataset', |
|
height=600, |
|
width=1000 |
|
) |
|
|
|
|
|
diff2_stacked_bar = fig |
|
|
|
|
|
|
|
def curated(request): |
|
|
|
|
|
params = dict(request.query_params) |
|
if target := params.get("target"): |
|
if data_source := params.get(f"data_source_{target}"): |
|
return get_data( |
|
data_source, params.get(f"doc_id_{target}", 3), params.get("target") |
|
) |
|
if doc_id := params.get(f"doc_id_{target}"): |
|
return get_data( |
|
params.get(f"data_source_{target}"), doc_id, params.get("target") |
|
) |
|
|
|
data_preparation_steps = pd.DataFrame( |
|
{ |
|
"Method": [ |
|
"HTTP/FTP dumps", |
|
"Web crawling", |
|
"Archive snapshot", |
|
"Generated", |
|
"Curated", |
|
], |
|
"Description": [ |
|
"Acquiring data from HTTP/FTP dumps", |
|
"Crawling websites to extract data", |
|
"Working with archive dumps", |
|
"Generating synthetic data", |
|
"High quality curated data", |
|
], |
|
"Source": [ |
|
"Freelaw | Wikipedia | PhilPapers | Arxiv | S2ORC | Pubmeds", |
|
"USPTO | Hackernews | Ubuntu IRC", |
|
"StackExchange", |
|
"DM Maths", |
|
"PG19 | Europarl", |
|
], |
|
} |
|
) |
|
|
|
table_html = data_preparation_steps.to_html(index=False, border=0) |
|
table_div = Div(NotStr(table_html), style="margin: 40px;") |
|
|
|
text = P("""This initial stage serves as the foundation for the entire |
|
process. Here, we focus on acquiring and extracting the raw data, which can |
|
come from various sources such as crawling websites, using HTTP/FTP dumps, |
|
or working with archive dumps. For instance, to download and prepare a |
|
dataset, we can specific downloaders based on the data source. Each dataset |
|
might have its own downloader script which can be updated in real time to |
|
handle changes in the data source. Here is a general outline of the data |
|
preparation process: It's worth noting that some pipelines might require |
|
invoking additional functions or scripts to handle specific data sources or |
|
formats. These helper scripts can be located within specific directories |
|
or modules dedicated to the dataset.""") |
|
|
|
data_preparation_div = Div( |
|
H2("examples"), |
|
Div( |
|
get_data(target=gen_random_id()), |
|
style="border: 1px solid #ccc; padding: 20px;", |
|
), |
|
) |
|
|
|
text = P("""Data preprocessing is a crucial step in the data science |
|
pipeline. It involves cleaning and transforming raw data into a format that |
|
is suitable for analysis. This process includes handling missing values, |
|
normalizing data, encoding categorical variables, and more.""") |
|
|
|
preprocessing_steps = pd.DataFrame( |
|
{ |
|
"Step": [ |
|
"Language Filter", |
|
"Min Word Count", |
|
"Title Abstract", |
|
"Majority Language", |
|
"Paragraph Count", |
|
"Frequency", |
|
"Unigram Log Probability", |
|
], |
|
"Description": [ |
|
"Filtering data based on language", |
|
"Setting a minimum word count threshold", |
|
"Extracting information from the title and abstract", |
|
"Identifying the majority language in the dataset", |
|
"Counting the number of paragraphs in each document", |
|
"Calculating the frequency of each word in the dataset", |
|
"Calculating the log probability of each unigram", |
|
], |
|
"Need": [ |
|
"To remove documents in unwanted languages", |
|
"To filter out documents with very few words", |
|
"To extract relevant information for analysis", |
|
"To understand the distribution of languages in the dataset", |
|
"To analyze the structure and length of documents", |
|
"To identify important words in the dataset", |
|
"To measure the significance of individual words", |
|
], |
|
"Pros": [ |
|
"Improves data quality by removing irrelevant documents", |
|
"Filters out low-quality or incomplete documents", |
|
"Provides additional information for analysis", |
|
"Enables language-specific analysis and insights", |
|
"Helps understand the complexity and content of documents", |
|
"Identifies important terms and topics in the dataset", |
|
"Quantifies the importance of individual words", |
|
], |
|
"Cons": [ |
|
"May exclude documents in less common languages", |
|
"May remove documents with valuable information", |
|
"May introduce bias in the analysis", |
|
"May not accurately represent the language distribution", |
|
"May not capture the complexity of document structure", |
|
"May be sensitive to noise and outliers", |
|
"May not capture the semantic meaning of words", |
|
], |
|
} |
|
) |
|
|
|
table_html = preprocessing_steps.to_html(index=False, border=0) |
|
table_div = Div(NotStr(table_html), style="margin: 40px;") |
|
data_preprocessing_div = Div(H3("Data Preprocessing"), text, table_div) |
|
|
|
return Div( |
|
overview, |
|
H2("Curated Sources: Overview"), |
|
overview_text, |
|
copyright_disclaimer, |
|
plotly2fasthtml(treemap_chart), |
|
H2("Curated Sources Defined"), |
|
table_desc, |
|
data_preprocessing_div, |
|
|
|
|
|
|
|
|
|
plotly2fasthtml(diff2_stacked_bar), |
|
H2("Curated Sources Processing"), |
|
filtering_process, |
|
data_preparation_div, |
|
H2("Local Deduplication"), |
|
local_dedup_text, |
|
table_div_data_pipe, |
|
id="inner-text", |
|
) |
|
|
|
|
