Datasets:

AgentPublic
/

open_government

 ---
 license: apache-2.0
+task_categories:
+- text-generation
+language:
+- en
+- fr
+- bg
+- hr
+- cs
+- da
+- nl
+- et
+- fi
+- de
+- el
+- hu
+- ga
+- it
+- lt
+- mt
+- pl
+- pt
+- ro
+- sk
+- sl
+- es
+- sv
+tags:
+- legal
+- finance
+size_categories:
+- 100B<n<1T
 ---
+# Open Government Dataset
+Open Government is a set of financial, legal, and administrative data in the public domain. In total, the dataset contains approximately 390B tokens.
+The dataset comprises two main datasets: Finance Commons and Legal Commons.
+## Use Common Corpus
+from datasets import load_dataset
+data = load_dataset('PleIAs/open_government')
+## Dataset Structure
+<details >
+  <summary>Data Fields</summary>
+  *  identifier: unique text identifier
+  *  text: post-processed text
+  *  char_count: number of UTF-8 characters in text
+  *  file_name: original file path, organized by collection
+  *  set_id: set id (1-10)
+  *  subset_id: subset id (1-100)
+  *  license
+  * URL
+  * language
+</details >
+## Provenance
+### Finance Commons
+Finance Commons was released independently as a [collection](https://huggingface.co/collections/PleIAs/finance-commons-66925e1095c7fa6e6828e26c). It is a multimodal dataset and contains both text and PDF data. This release containing Finance Commons contains only text data.
+The dataset comprises several subsets:
+* **Securities and Exchange Commission (SEC)** This dataset comprises the SEC annual reports (Form 10-K) for the years 1993 to 2024. Entries up to 2020 were compiled by [Loukas et al. (2021)](https://aclanthology.org/2021.econlp-1.2/). We added the reports from 2021-2024, which come from the [EDGAR database](https://www.sec.gov/search-filings/edgar-search-assistance/accessing-edgar-data), compiled using the [EDGAR-Crawler toolkit](https://github.com/nlpaueb/edgar-crawler). The documents are primarily in English. This dataset is available individually: [https://huggingface.co/datasets/PleIAs/SEC](https://huggingface.co/datasets/PleIAs/SEC).
+* **World Trade Organization (WTO)** This dataset comprises documents from WTO’s official [Documents Online platform](https://docs.wto.org/dol2fe/Pages/FE_Search/FE_S_S005.aspx). The documents cover the years 1995 to 2024. Documents are available in three official languages: English, French, and Spanish. Some documents are available in other languages, e.g. Chinese, Korean, Arabic, German, and Portuguese. This dataset is available individually: [https://huggingface.co/datasets/PleIAs/WTO-Text](https://huggingface.co/datasets/PleIAs/WTO-Text).
+* **French Authority for Financial Market (AMF)** This is a dataset of documents from the French Authority for Financial Market, or the [Autorité des marchés financiers](https://www.amf-france.org/en/news-publications/publications/open-data) (AMF), which is an independent public authority that regulates the French market. The documents are primarily in French. This dataset is available individually: [https://huggingface.co/datasets/PleIAs/AMF-Text](https://huggingface.co/datasets/PleIAs/AMF-Text).
+* **Tenders Electronic Daily (TED) EU Tenders** This dataset is a collection of procurement notices published by the EU. The documents are published in the online version of the ['Supplement to the Official Journal' of the EU](https://ted.europa.eu/en/), dedicated to European public procurement. The documents are mostly in German, with French, Polish, and Spanish making up relatively large portions of the remaining documents. This dataset is available individually: [https://huggingface.co/datasets/PleIAs/TEDEUTenders](https://huggingface.co/datasets/PleIAs/TEDEUTenders).
+* **General Agreement on Tariffs and Trade (GATT) Library** This dataset comprises documents from GATT, which was an organization that promoted international commerce and the reduction of trade barriers among member states. Public documents were [made available by the General Council of the WTO in 2006](https://www.wto.org/english/docs_e/gattdocs_e.htm). The documents span from January 1, 1946, to September 6, 1996. Most of the documents are in English, but there are also documents in French, Spanish, and other languages. This dataset is available individually: [https://huggingface.co/datasets/PleIAs/GATT_library](https://huggingface.co/datasets/PleIAs/GATT_library).
+Total tokens by subset:
+| **Dataset**      | **Tokens**         |
+|--------------|----------------|
+| SEC          | 9,648,522,224  |
+| WTO          | 2,783,387,015  |
+| AMF          | 4,912,438,575  |
+| TEDEUTenders | 649,323,694    |
+| GATT Library | 215,338,931    |
+| **Total Tokens:** | **18,209,010,439** |
+### Legal Commons
+Legal Commons is a collection of legal and administrative datasets. The datasets come mostly from the EU and the US and cover a wide range of languages. These datasets are useful for developing language models with legal knowledge, as well as models that are ideal for document processing in official administrative applications.
+* **Europarl** This dataset is a multilingual parallel corpus, drawn from [the proceedings of the European Parliament](https://www.statmt.org/europarl/). It includes texts from 21 EU languages. It was originally compiled by [Koehn (2005)](https://aclanthology.org/2005.mtsummit-papers.11/).
+* **Caselaw Access Project** This dataset consists of 6,930,777 legal cases, digitized from [Harvard Law School Library's physical collection of American case law](https://case.law). The dataset spans the years 1658 to 2020. The dataset is primarily in English.
+* **CourtListener** This is a [dataset](https://www.courtlistener.com/help/api/bulk-data/) of opinions, oral arguments, judges, judicial financial records, and federal filings put together by the [Free Law Project](https://free.law/contact).
+* **EUR-lex.} This is a dataset of 57,000 [legislative documents from the EU](https://eur-lex.europa.eu/). It is based on the dataset by [Loza Mencía & Fürnkranz (2010)](https://link.springer.com/chapter/10.1007/978-3-642-12837-0_11) and developed by.
+[Chalkidis et al. (2019)](https://aclanthology.org/P19-1636/). The documents have also been annotated by the [Publications Office of EU](https://publications.europa.eu/en) with concepts from [EuroVoc](http://eurovoc.europa.eu/). The dataset covers all 24 EU languages.
+* **Eurovoc** Eurovoc is a dataset containing 3,700,000 documents in 39 languages with associated [EuroVoc](http://eurovoc.europa.eu/) labels. The documents come from [Cellar](https://op.europa.eu/en/web/cellar), which is a data repository for the Publications Office of the European Union. This dataset was originally compiled by Sébastien Campion and the original version is available: [https://huggingface.co/datasets/EuropeanParliament/Eurovoc](https://huggingface.co/datasets/EuropeanParliament/Eurovoc).
+* **law_parliament** This dataset comes from French Parliament via [Regards Citoyens](https://www.regardscitoyens.org/#&panel1-1)
+* **French Open Data** This dataset comes from French administrative bodies’ websites, for example the French Directorate of Legal and Administrative Information ([Direction de l'information légale et administrative](https://echanges.dila.gouv.fr/OPENDATA/); DILA)), which is a French public administrative entity that disseminates information about laws and their applications to the public.
+* **US PTO** This dataset comprises documents from the United States Patent and Trademark Office (USPTO), which is the federal agency that grants patents and registers trademarks. This dataset consists of actions from this agency from 2019-2022. It was originally published as part of the [Pile of Law](https://huggingface.co/datasets/pile-of-law/pile-of-law) [(Henderson et al. (2022)](https://openreview.net/forum?id=3HCT3xfNm9r).
+* **UN Digital Library** This dataset comes from the [UN Digital Library](https://digitallibrary.un.org/?ln=en).
+* **PleIAs European Legal Dataset** This comprises data from various EU websites, e.g. [Archives of the EU Institute](https://archives.eui.eu/) and the [Council of the EU](https://www.consilium.europa.eu/en/general-secretariat/corporate-policies/transparency/open-data/).
+* ** OECD** This data comes from the [Organisation for Economic Co-operation and Development (OECD)](https://www.oecd.org/en/data/datasets.html?orderBy=mostRelevant&page=0).
+Tokens by subset:
+| **Dataset**                | **Tokens**          |
+|------------------------|-----------------|
+| Caselaw Access Project | 13,823,526,194  |
+| CourtListener          | 22,463,960,458  |
+| EUR-lex                | 64,896,588,374  |
+| Eurovoc                | 31,613,548,606  |
+| law_parliament         | 275,623,650     |
+| French Open Data       | 24,480,289,170  |
+| USPTO                  | 200,115,310,846 |
+| UN Digital Library     | 1,764,113,826   |
+| European Open Data     | 3,627,192,797   |
+| OECD                   | 575,213,706     |
+| **Total Tokens:**           | **363,639,398,827** |
+## How to Use
+### Considerations for Using the Data
+All data in Common Corpus are permissibly licensed and may be used for both commercial and non-commercial purposes.
+The dataset is multilingual. The language text is included in the metadata, so data can be filtered by language. Additionally, some of the text data are historical. The year each text is written is included in the metadata, therefore it is possible to construct a dataset with a custom date cutoff if desired.
+### Personal and Sensitive Information
+We have attempted to remove personally identifiable information (PII). We primarily use [Microsoft Presidio](https://microsoft.github.io/presidio/), but make additional modifications to account for language- and country-specific considerations, such as European phone number formats.
+# Acknowledgements
+The corpus was stored and processed with the generous support of the AI Alliance, Jean Zay (Eviden, Idris), Nvidia Inception program, Nebius AI, Tracto AI, Mozilla. It was built up with the support and concerted efforts of the state start-up LANGU:IA (start-up d’Etat), supported by the French Ministry of Culture and DINUM, as part of the prefiguration of the service offering of the Alliance for Language technologies EDIC (ALT-EDIC). This dataset was also made in partnership with Wikimedia Enterprise for the Wikipedia part. The collection of the corpus has been largely facilitated thanks to the open science LLM community insights, cooperation and support (Eleuther AI, Allen AI, HuggingFace…).
+<div style="text-align: center;">
+  <img src="https://huggingface.co/datasets/PleIAs/common_corpus/resolve/main/logo/ai_alliance.png" style="width: 33%; margin: 0 auto; display: inline-block;"/>
+  <img src="https://huggingface.co/datasets/PleIAs/common_corpus/resolve/main/logo/logo-genci-header.svg" style="width: 33%; margin: 0 auto; display: inline-block;"/>
+  <img src="https://huggingface.co/datasets/PleIAs/common_corpus/resolve/main/logo/Nvidia_(logo).svg.png" style="width: 33%; margin: 0 auto; display: inline-block;"/>
+  <img src="https://huggingface.co/datasets/PleIAs/common_corpus/resolve/main/logo/tractoAI.png" style="width: 33%; margin: 0 auto; display: inline-block;"/>
+  <img src="https://huggingface.co/datasets/PleIAs/common_corpus/resolve/main/logo/mozilla.png" style="width: 33%; margin: 0 auto; display: inline-block;"/>
+  <img src="https://raw.githubusercontent.com/Pleias/logos/f117dee70b317bc664eac14ee70d7c0563101ed1/ministere_logo.png?token=GHSAT0AAAAAACZUTJMICO3MSWUJ43EQWG5QZZL3RFQ" style="width: 33%; margin: 0 auto; display: inline-block;"/>
+  <img src="https://raw.githubusercontent.com/Pleias/logos/f117dee70b317bc664eac14ee70d7c0563101ed1/wikimedia_logo.png?token=GHSAT0AAAAAACZUTJMIIPAP4J7MKP6RSSWCZZL3TFA" style="width: 33%; margin: 0 auto; display: inline-block;"/>
+</div>