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| from fasthtml.common import * | |
| from fasthtml.components import * | |
| from plotly import graph_objects as go | |
| from fh_plotly import plotly2fasthtml | |
| import pandas as pd | |
| import json | |
| from data_viewer import view_data, gen_random_id | |
| from rich import print | |
| import uuid | |
| import plotly.express as px | |
| from fasthtml.components import D_code | |
| overview = Div( | |
| H2("Curated Source Processing Overview"), | |
| H3("What This Section Contains"), | |
| 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: "), | |
| Ul( | |
| Li("Curated Sources Data Processing Summary", style = "margin-bottom: 5px"), | |
| Li("Individual Filtering Discussion for Each Source", style = "margin-bottom: 5px"), | |
| ), | |
| ), | |
| 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. ") | |
| 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.") | |
| treemap_data = { | |
| 'Source': ['ArXiv', 'PubMed Central', 'PubMed Abstract', 'S2ORC Full Text', 'S2ORC Abstract', 'PhilPapers', 'Wikipedia', 'StackExchange', 'EuroParl', 'Ubuntu IRC', 'Freelaw', 'PG19', 'USPTO', 'HackerNews', 'DM Maths'], | |
| 'Category': ['Papers', 'Papers', 'Papers', 'Papers', 'Papers', 'Papers', 'Internet', 'Conversational', 'Legal/Formal', 'Conversational', 'Legal/Formal', 'Books', 'Legal/Formal', 'Conversational', 'Reasoning'], | |
| 'Count': [100, 200, 150, 120, 80, 90, 300, 250, 180, 150, 150, 250, 180, 120, 90], | |
| 'Details': [ | |
| 'A repository of scientific papers in various disciplines, including computer science, physics, mathematics, and more.', | |
| 'A database of biomedical and life sciences research articles.', | |
| 'Abstracts of biomedical literature from various sources.', | |
| 'Full-text articles from the Semantic Scholar Open Research Corpus.', | |
| 'Abstracts of articles from the Semantic Scholar Open Research Corpus.', | |
| 'Papers from the PhilPapers database, a comprehensive index and bibliography of philosophy research.', | |
| 'A collaborative online encyclopedia that covers a wide range of topics.', | |
| 'A network of question-and-answer websites on various subjects, including programming, science, mathematics, and more.', | |
| 'A collection of multilingual parallel corpora of parliamentary debates from the European Parliament.', | |
| 'Chat logs from the Ubuntu Internet Relay Chat (IRC) channels.', | |
| 'Legal documents and court cases from various jurisdictions.', | |
| 'A collection of books from Project Gutenberg, a digital library of public domain works.', | |
| 'Patent documents from the United States Patent and Trademark Office.', | |
| 'User-generated news and discussion platform focused on technology and startups.', | |
| 'Deep Mind Maths dataset with generated questions.' | |
| ] | |
| } | |
| # Calculate percentage for each data source | |
| total_count = sum(treemap_data['Count']) | |
| treemap_data['Percentage'] = [count / total_count * 100 for count in treemap_data['Count']] | |
| # Create treemap | |
| fig = px.treemap(treemap_data, path=['Category', 'Source'], values='Count', hover_data=['Details', 'Percentage'], hover_name='Source') | |
| # Set the size of the chart | |
| # Display treemap if you want to update the size.update_layout(width=800, height=600) | |
| treemap_chart = fig | |
| wikipedia_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "Wikipedia", | |
| ], | |
| "Lines Downloaded": [ | |
| "61614907", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "1.86%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "", | |
| ], | |
| } | |
| ) | |
| table_html_wikipedia = wikipedia_filter.to_html(index=False, border=0) | |
| table_div_wikipedia = Div(NotStr(table_html_wikipedia), style="margin: 40px;") | |
| freelaw_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "FreeLaw", | |
| ], | |
| "Lines Downloaded": [ | |
| "75971288", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "3.00%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "7.49%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.07%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_freelaw = freelaw_filter.to_html(index=False, border=0) | |
| table_div_freelaw = Div(NotStr(table_html_freelaw), style="margin: 40px;") | |
| dmm_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "DM Math", | |
| ], | |
| "Lines Downloaded": [ | |
| "112559888", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_dmm = dmm_filter.to_html(index=False, border=0) | |
| table_div_dmm = Div(NotStr(table_html_dmm), style="margin: 40px;") | |
| uspto_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "USPTO", | |
| ], | |
| "Lines Downloaded": [ | |
| "6880276", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "0.02%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "1.88%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.01%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_uspto = uspto_filter.to_html(index=False, border=0) | |
| table_div_uspto = Div(NotStr(table_html_uspto), style="margin: 40px;") | |
| pg19_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "PG-19", | |
| ], | |
| "Lines Downloaded": [ | |
| "28752", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "0.24%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.17%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_pg19 = pg19_filter.to_html(index=False, border=0) | |
| table_div_pg19 = Div(NotStr(table_html_pg19), style="margin: 40px;") | |
| hn_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "HackerNews", | |
| ], | |
| "Lines Downloaded": [ | |
| "2064931", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "2.62%%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "0.02%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.34%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_hn = hn_filter.to_html(index=False, border=0) | |
| table_div_hn = Div(NotStr(table_html_hn), style="margin: 40px;") | |
| uirc_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "Ubunutu IRC", | |
| ], | |
| "Lines Downloaded": [ | |
| "37966", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "38.10%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "0.14%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "1.12%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_uirc = uirc_filter.to_html(index=False, border=0) | |
| table_div_uirc = Div(NotStr(table_html_uirc), style="margin: 40px;") | |
| up_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "EuroParl", | |
| ], | |
| "Lines Downloaded": [ | |
| "69814", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_up = up_filter.to_html(index=False, border=0) | |
| table_div_up = Div(NotStr(table_html_up), style="margin: 40px;") | |
| se_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "StackExchange", | |
| ], | |
| "Lines Downloaded": [ | |
| "23246548", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_se = se_filter.to_html(index=False, border=0) | |
| table_div_se = Div(NotStr(table_html_se), style="margin: 40px;") | |
| arx_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "ArXiv", | |
| ], | |
| "Lines Downloaded": [ | |
| "1911867", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "2.22%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "5.65%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.07%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_arx = arx_filter.to_html(index=False, border=0) | |
| table_div_arx = Div(NotStr(table_html_arx), style="margin: 40px;") | |
| s2o_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "S2ORC", | |
| ], | |
| "Lines Downloaded": [ | |
| "12963563", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_s2o = s2o_filter.to_html(index=False, border=0) | |
| table_div_s2o = Div(NotStr(table_html_s2o), style="margin: 40px;") | |
| med_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "PubMed - Central", | |
| ], | |
| "Lines Downloaded": [ | |
| "5230932", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "7.66%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "1.29%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.02%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_med = med_filter.to_html(index=False, border=0) | |
| table_div_med = Div(NotStr(table_html_med), style="margin: 40px;") | |
| phil_filter = pd.DataFrame( | |
| { | |
| "Dataset": [ | |
| "Phil Papers", | |
| ], | |
| "Lines Downloaded": [ | |
| "49389", | |
| ], | |
| "Percent Removed After Language Filter": [ | |
| "20.68%", | |
| ], | |
| "Percent Removed After Min Word Count Filter": [ | |
| "0.00%", | |
| ], | |
| "Percent Removed After Unigram Probability Filter": [ | |
| "0.12%", | |
| ], | |
| "Percent Removed After Local Dedup": [ | |
| "", | |
| ], | |
| "Total Percentage Remaining": [ | |
| "%", | |
| ], | |
| } | |
| ) | |
| table_html_phil = phil_filter.to_html(index=False, border=0) | |
| table_div_phil = Div(NotStr(table_html_phil), style="margin: 40px;") | |
| filtering_process = Div( | |
| Section( | |
| P("This section contains the specific steps taken to filter all 14 curated source datasets.") | |
| ), | |
| Section( | |
| H3("Wikipedia"), | |
| H4("Download and Extraction"), | |
| P("The Wikimedia dataset was downloaded from the official snapshot on Huggingface: ", A("https://huggingface.co/datasets/wikimedia/wikipedia/tree/main", href="https://huggingface.co/datasets/wikimedia/wikipedia/tree/main"), ". The", D_code("huggingface dataset.to_json", language="python"), " function was used to convert the original parqet format to the jsonl format."), | |
| H4("Filtering"), | |
| P("Manual inspection of the dataset demostrated high quality content. Only one filter was used to remove articles with few words. Based normal sentence constructs, the article was kept if it contained 10 or more words. Any article with fewer than 10 words was removed."), | |
| H4("Local Deduplication Process"), | |
| Ol( | |
| Li("Whole wikipedia was deduped using minhash generation following Slim pajama code"), | |
| ), | |
| table_div_wikipedia, | |
| ), | |
| Section( | |
| H3("ArXiv"), | |
| H4("Download and Extraction"), | |
| P("All the data was downloaded in original latex format from Arxiv official S3 dump ", A("s3://arxic/src", href="s3://arxic/src"), ". We try to encode the downloaded data into utf-8 or guess encoding using chardet library. After that pandoc was used to extract information from the latex files and saved as markdown format", D_code("pandoc -s {tex} -o out/{out_name}.md --wrap=none", language="python"), ". All markdowns were combined to create jsonl files."), | |
| H4("Filtering"), | |
| P("Multiple filters are used here after manually verifying output of all the filters as suggested by peS2o dataset (citation needed)"), | |
| Ol( | |
| Li("Language Filter: any language other than English are discarded"), | |
| Li("Minimum Word Count Filter: less than 500 words (not inclusive) are discarded"), | |
| Li("Unigram Log Probablity Filter: Documents were kept if they their average unigram log probability was higher than -20. To calculate the average log word probability, we use word frequencies extracted from the", A("1T Web-gram corpus", href= "https://catalog.ldc.upenn.edu/LDC2006T13"),". Specifically, we use the list available created by". A("Rachel Tatman", href="https://www.kaggle.com/datasets/rtatman/english-word-frequency"),"."), | |
| Li("Note: The Frequency Filter was calculated but not applied. 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."), | |
| ), | |
| H4("Local Deduplication Process"), | |
| Ol( | |
| Li("Local dedup was done with all papers combined."), | |
| ), | |
| table_div_arx, | |
| ), | |
| Section( | |
| H3("S2ORC - NEED TO MAKE S2ORC ABSTRACT AND UPDATE THIS FILTERING SECTION"), | |
| H4("Download and Extraction"), | |
| Ol( | |
| Li("This was downloaded directly in zip format using S2ORC api key and normal get request. code: response = urllib.request.urlopen(url)"), | |
| Li("There were two kind of datasets that was downloaded S2ORC and S2ORC abstract"), | |
| ), | |
| H4("Filtering - S2ORC"), | |
| P("1. Multiple filters are used here after manually verifying output of all the filters as suggested by peS2o dataset"), | |
| Ol( | |
| Li("title_abstract: must have title and abstract"), | |
| 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."), | |
| Li("word_count: less than 500 words (not inclusive) are discarded"), | |
| Li("paragraph_count: The paper must have at least 5 paragraphs after removing paragraphs with less than -20 average log world probability"), | |
| 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."), | |
| ), | |
| H4("Filtering - S2ORC Abstract"), | |
| 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"), | |
| Ol( | |
| Li("title_abstract: must have title and abstract"), | |
| Li("language: abstract must be in English"), | |
| Li("word_count: less than 20 (not inclusive) are discarded"), | |
| Li("Unigram log probablity"), | |
| 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."), | |
| ), | |
| 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_s2o, | |
| ), | |
| Section( | |
| H3("PubMed - need to update with abstract vs central"), | |
| H4("Download and Extraction"), | |
| Ol( | |
| 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/"), | |
| Li("All the urls are downloaded and the downloaded data is in xml.tar format"), | |
| 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"), | |
| Li("All the markdown files are combined to create jsonl files. In jsonl files, 1 line correspond to 1 markdown file."), | |
| 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"), | |
| ), | |
| H4("Filtering"), | |
| P("1. Multiple filters are used here after manually verifying output of all the filters as suggested by peS2o dataset."), | |
| Ol( | |
| Li("min_word: less than 100 words (not inclusive) are discarded, less than 20 words for pubmed abstract"), | |
| Li("Language: any language other than English are discarded"), | |
| 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"), | |
| 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."), | |
| Li("need to add the hyperlinks for the section above"), | |
| ), | |
| 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_med, | |
| ), | |
| Section( | |
| H3("Phil Papers"), | |
| H4("Download and Extraction"), | |
| P("Original PDF files download from", A("https://philarchive.org/oai.pl", href="https://philarchive.org/oai.pl"), ". All available PDF's were downloaded. Each PDF was converted to text using java", D_code("-jar ../philpapers_resources/src/pdfbox-app-2.0.21.jar ExtractText {f0} {FOUT.name}", language="java"), ". After converting to text formatting, a language was detected and added using the langdetect (citation needed) library."), | |
| 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."), | |
| ), | |
| table_div_phil, | |
| ), | |
| Section( | |
| H3("Europarl"), | |
| H4("Download and Extraction"), | |
| P("Original dataset was downloaded from", A("http://www.statmt.org/europarl/v7/europarl.tgz", href="http://www.statmt.org/europarl/v7/europarl.tgz"),". The files were converted to jsonl lines for filtering."), | |
| H4("Filtering"), | |
| P("EuroParl was initially filtered during the download process. Documents with fewer than 200 characters were removed. The documents also contained 'TAGS' which were removed."), | |
| H4("Local Deduplication Process"), | |
| Ol( | |
| Li("Local dedup was done within europarl itself"), | |
| ), | |
| table_div_up, | |
| ), | |
| Section( | |
| H3("HackerNews"), | |
| H4("Download and Extraction"), | |
| P("The dataset was downloaded from the HackerNews repo here:", A("https://hacker-news.firebaseio.com/v0/item/", href="https://hacker-news.firebaseio.com/v0/item/"), ". The dataset was parsed using the Story ID. In this dataset each post is a story, and each reply is considered subsequent story. Story IDs were considered between ID 1 to 37500000. The URL for all Story IDs was pinged. If that ID returned an error, the ID was removed. Each request was given a 2 second wait to account for network time."), | |
| P("The HackerNews dataset contains a vast amount of stories and is known for lively discussions. Due to the number of replies a story may contain, only longest threads included stories from the 3rd level onwards. All stories included the title (1st level) and all direct replies (2nd level). Replies to the replies (3rd level) are only included for X STORIES."), | |
| H4("Filtering"), | |
| Ol( | |
| Li("Language Filter: English"), | |
| Li("Minimum Word Count Filter: 10"), | |
| Li("Unigram Log Probability"), | |
| ), | |
| H4("Local Deduplication Process"), | |
| Ol( | |
| Li("Local dedup was done within hackernews itself"), | |
| ), | |
| table_div_hn, | |
| ), | |
| Section( | |
| H3("USPTO"), | |
| H4("Download and Extraction"), | |
| P("Data was downloaded and extracted using tags from", A("https://bulkdata.uspto.gov/data/patent/grant/redbook/fulltext/", href="https://bulkdata.uspto.gov/data/patent/grant/redbook/fulltext/"),". There were three different formats that needed three different functions to download and extract the data based on year: I(Pre_2002), 2002_to_2004, and post_2004."), | |
| H4("Filtering"), | |
| Ol( | |
| Li("Language Filter: English"), | |
| Li("Minimum Word Count Filter: 50"), | |
| Li("Unigram Log Probability"), | |
| ), | |
| H4("Local Deduplication Process"), | |
| Ol( | |
| Li("Local dedup was done within USPTO itself"), | |
| ), | |
| table_div_uspto, | |
| ), | |
| Section( | |
| H3("FreeLaw"), | |
| H4("Download and Extraction"), | |
| P("The dataset was downloaded from:" A("https://storage.courtlistener.com/bulk-data/", href="https://storage.courtlistener.com/bulk-data/"),". There are 19 CSV files which contain overlapping content. CSV files can contain content in multiple columns requiring a holistic extraction approach. Text was extracted from the following using html2text function.", | |
| D_code(""" | |
| ("html", html2text), | |
| ("html_lawbox", html2text), | |
| ("html_columbia", html2text), | |
| ("html_anon_2020", html2text), | |
| ("html_with_citations", html2text), | |
| ("xml_harvard", html2text), | |
| plain_text | |
| """, language ="SQL")," All content was downloaded leading to high number of documents filtered during local deduplication. Following The Pile, priorty was given to plain_text first, followed by the columns in the table in reverse order."), | |
| H4("Filtering"), | |
| Ol( | |
| Li("Language Filter: English"), | |
| Li("Minimum Word Count Filter: 50"), | |
| Li("Unigram Log Probability"), | |
| ), | |
| H4("Local Deduplication Process"), | |
| Ol( | |
| Li("Local dedup was done within freelaw itself which removed 90%+ duplicates"), | |
| ), | |
| table_div_freelaw, | |
| ), | |
| Section( | |
| H3("StackExchange"), | |
| H4("Download and Extraction"), | |
| P("The archive dataset was used to download all data from StackExchange and StackExchange's sub URLs including: ", A("math.stackexchange.com", href"math.stackexchange.com"),". Raw data was extracted an XML format and only two files Posts.xml and Comments.xml were considered. To match the StackExchange hierarchy, each file was parsed using post_id to connect questions to answers and then to comments."), | |
| P(""" | |
| 1. Questions: | |
| 2. Comment1: | |
| 3. Comment2: | |
| 4. Answer1: | |
| 5. Comment1: | |
| 6. Comment2: | |
| 7. Answer2: | |
| 8. Comment1: | |
| 9. Comment2: | |
| """), | |
| H4("Filtering"), | |
| Ol( | |
| Li("Minimum Word Count Filter: 10"), | |
| ), | |
| H4("Local Deduplication Process"), | |
| Ol( | |
| Li("Local dedup was done within stackexchange itself"), | |
| ), | |
| table_div_se, | |
| ), | |
| Section( | |
| H3("Ubuntu IRC"), | |
| H4("Download and Extraction"), | |
| P("The dataset was downloaded from:", A("https://irclogs.ubuntu.com/{date.year}/{date.month:02d}/{date.day:02d}/", href"https://irclogs.ubuntu.com/{date.year}/{date.month:02d}/{date.day:02d}/"), " based on the year."), | |
| P("During extraction, the logs were cleaned using following functions:"), | |
| D_code(""" | |
| def exclude_system(x): | |
| return '\n'.join(line for line in x.split('\n') if not line.startswith('===')) | |
| def exclude_select_system(x): | |
| return '\n'.join(line for line in x.split('\n') if not (line.startswith('===') and any(term in line for term in ['has joined #', 'has left #', 'Topic for #', "Topic (#", "is now known as"]) )) | |
| def clean(x): | |
| return '\n'.join('* ' + line[4:] if line.startswith('===') else line[8:] for line in x.split('\n')) | |
| """, block="block", language="python" ), | |
| H4("Filtering"), | |
| Ol( | |
| Li("Language Filter: English"), | |
| Li("Minimum Word Count Filter: 10"), | |
| Li("Unigram Log Probability"), | |
| ), | |
| H4("Local Deduplication Process"), | |
| Ol( | |
| Li("Local dedup was done within Ubuntu IRC itself"), | |
| ), | |
| table_div_uirc, | |
| ), | |
| Section( | |
| H3("DM Maths"), | |
| H4("Download and Extraction"), | |
| P("The dataset was downloaded rirectly downloaded from the Huggingface repo:", A("https://huggingface.co/datasets/deepmind/math_dataset",href="https://huggingface.co/datasets/deepmind/math_dataset"), ". The data was converted to the jsonl format where lines is represented as:"), | |
| D_code(""" | |
| Question: TEXT | |
| Answer: TEXT""", block="block", language="python"), | |
| H4("Filtering"), | |
| Ol( | |
| Li("No filtering was applied to DM Math"), | |
| ), | |
| H4("Local Deduplication Process"), | |
| Ol( | |
| Li("None"), | |
| ), | |
| table_div_dmm, | |
| ), | |
| Section( | |
| H3("PG19"), | |
| H4("Download and Extraction"), | |
| Ol( | |
| Li("The dataset was downloaded directly from Huggingface:", A("https://huggingface.co/datasets/deepmind/pg19", href="https://huggingface.co/datasets/deepmind/pg19"), "."), | |
| ), | |
| H4("Filtering"), | |
| Ol( | |
| Li("Language Filter: ???"), | |
| Li("Minimum Word Count Filter: 20"), | |
| Li("Unigram Log Probability"), | |
| ), | |
| H4("Local Deduplication Process"), | |
| Ol( | |
| Li("Local dedup was done within PG19 itself"), | |
| ), | |
| 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 for the stacked bar chart | |
| 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] | |
| } | |
| # Creating a dataframe | |
| df = pd.DataFrame(data) | |
| # Creating the stacked bar chart | |
| fig = go.Figure() | |
| # Add trace for each dataset | |
| for dataset in df.columns[1:]: | |
| fig.add_trace(go.Bar( | |
| name=dataset, | |
| x=df['Filter'], | |
| y=df[dataset] | |
| )) | |
| # Update the layout | |
| 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 | |
| ) | |
| # Show the plot | |
| diff2_stacked_bar = fig | |
| def curated(request): | |
| # Partial Updates | |
| 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"), | |
| H3("TALK ABOUT THE DIFFERENT FILTERS BEFORE HAND"), | |
| filtering_process, | |
| data_preparation_div, | |
| H2("Local Deduplication"), | |
| local_dedup_text, | |
| table_div_data_pipe, | |
| id="inner-text", | |
| ) | |