Datasets:
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README.md
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path: data/train-*
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# Material Contracts (Exhibit 10) from SEC
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WIP. I started the crawling on Jan 14, 2024, and expect to finish in a month or two. I will upload the data in batches.
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path: data/train-*
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---
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# Material Contracts (Exhibit 10) from SEC
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## Table of Contents
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- [Dataset Card Creation Guide](#dataset-card-creation-guide)
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
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- [Who are the source language producers?](#who-are-the-source-language-producers)
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- [Annotations](#annotations)
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- [Annotation process](#annotation-process)
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- [Who are the annotators?](#who-are-the-annotators)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** NA
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- **Repository:** [Crawler](https://github.com/ChenghaoMou/edgar-crawler/tree/main)
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- **Paper:** NA
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- **Leaderboard:** NA
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- **Point of Contact:** mouchenghao at gmail dot com
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### Dataset Summary
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827437 material contracts (Exhibit 10) collected from sec.gov, specifically from 10-k, 10-Q, and 8-K, ranging from 1994 to Jan 2024. Exhibit 10 consists of a large collection of legal agreements and contracts.
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### Supported Tasks and Leaderboards
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- `language-modeling` or `text-generation`: The dataset can be used to build language model in legal/finance domain.
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Currently, there is no leaderboard for this dataset.
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### Languages
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The primary language is US English with the rare instances of other languages.
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## Dataset Structure
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### Data Instances
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Please refer to the data viewer for examples.
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### Data Fields
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Please refer to the data viewer for examples.
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### Data Splits
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The dataset does not have a split by design. Everything is under train by convention.
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## Dataset Creation
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### Curation Rationale
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SEC EDGAR is a public available service from which one can query and access corporate filings. This collection of one specific exhibit files enables researchers to learn and model from contracts and agreements directly.
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### Source Data
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https://www.sec.gov/
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#### Initial Data Collection and Normalization
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The data is collected by year from all filings that are 10-K, 8-K, or 10-Q. Each exhibit is individually downloaded with all metadata information for maximum completeness.
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#### Who are the source language producers?
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The Securities and Exchange Commission (SEC) requires public companies, certain company insiders, and broker-dealers to file periodic financial statements and other disclosures. Finance professionals and investors rely on SEC filings to make informed decisions when evaluating whether to invest in a company. SEC filings can be accessed for free at EDGAR, the commission's online database.
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### Annotations
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NA
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#### Annotation process
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NA
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#### Who are the annotators?
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NA
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### Personal and Sensitive Information
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It might contain PII information (names, emails, job titles, companies) that are already public.
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## Considerations for Using the Data
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### Social Impact of Dataset
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This dataset could enable a better legal and finance language modeling capability.
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### Discussion of Biases
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Considering the source and language, it might produce overly US-centric legalese.
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### Other Known Limitations
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NA
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## Additional Information
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### Dataset Curators
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@chenghao
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### Licensing Information
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[Attribution-NonCommercial-ShareAlike 4.0 International](https://creativecommons.org/licenses/by-nc-sa/4.0/)
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### Citation Information
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NA
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### Contributions
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NA
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