Datasets:

c3po-ai
/

edgar-corpus

---
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annotations_creators:
- no-annotation
language:
- en
language_creators:
- other
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: EDGAR-CORPUS (10-K Filings from 1999 to 2020)
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
tags:
- research papers
- edgar
- sec
- finance
- financial
- filings
- 10K
- 10-K
- nlp
- research
- econlp
- economics
- business
task_categories:
- other
task_ids: []
duplicated_from: eloukas/edgar-corpus
---










































# Dataset Card for [EDGAR-CORPUS]

## Table of Contents

- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks](#supported-tasks)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Licensing Information](#licensing-information)
  - [References](#references)
  - [Contributions](#contributions)

## Dataset Description

- **Point of Contact: Lefteris Loukas**

### Dataset Summary

This dataset card is based on the paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round** authored by _Lefteris Loukas et.al_, as published in the _ECONLP 2021_ workshop.

This dataset contains the annual reports of public companies from 1993-2020 from SEC EDGAR filings. 

There is supported functionality to load a specific year.

Care: since this is a corpus dataset, different `train/val/test` splits do not have any special meaning. It's the default HF card format to have train/val/test splits.

If you wish to load specific year(s) of specific companies, you probably want to use the open-source software which generated this dataset, EDGAR-CRAWLER: https://github.com/nlpaueb/edgar-crawler.


### Supported Tasks

This is a raw dataset/corpus for financial NLP. 
As such, there are no annotations or labels. 

### Languages

The EDGAR Filings are in English.

## Dataset Structure

### Data Instances

Refer to the dataset preview.

### Data Fields

**filename**: Name of file on EDGAR from which the report was extracted.<br>
**cik**: EDGAR identifier for a firm.<br>
**year**: Year of report.<br>
**section_1**: Corressponding section of the Annual Report.<br>
**section_1A**: Corressponding section of the Annual Report.<br>
**section_1B**: Corressponding section of the Annual Report.<br>
**section_2**: Corressponding section of the Annual Report.<br>
**section_3**: Corressponding section of the Annual Report.<br>
**section_4**: Corressponding section of the Annual Report.<br>
**section_5**: Corressponding section of the Annual Report.<br>
**section_6**: Corressponding section of the Annual Report.<br>
**section_7**: Corressponding section of the Annual Report.<br>
**section_7A**: Corressponding section of the Annual Report.<br>
**section_8**: Corressponding section of the Annual Report.<br>
**section_9**: Corressponding section of the Annual Report.<br>
**section_9A**: Corressponding section of the Annual Report.<br>
**section_9B**: Corressponding section of the Annual Report.<br>
**section_10**: Corressponding section of the Annual Report.<br>
**section_11**: Corressponding section of the Annual Report.<br>
**section_12**: Corressponding section of the Annual Report.<br>
**section_13**: Corressponding section of the Annual Report.<br>
**section_14**: Corressponding section of the Annual Report.<br>
**section_15**: Corressponding section of the Annual Report.<br>

```python
import datasets

# Load the entire dataset
raw_dataset = datasets.load_dataset("eloukas/edgar-corpus", "full")

# Load a specific year and split
year_1993_training_dataset = datasets.load_dataset("eloukas/edgar-corpus", "year_1993", split="train")
```

### Data Splits

| Config    | Training | Validation | Test   |
| --------- | -------- | ---------- | ------ |
| full      | 176,289  | 22,050     | 22,036 |
| year_1993 | 1,060    | 133        | 133    |
| year_1994 | 2,083    | 261        | 260    |
| year_1995 | 4,110    | 514        | 514    |
| year_1996 | 7,589    | 949        | 949    |
| year_1997 | 8,084    | 1,011      | 1,011  |
| year_1998 | 8,040    | 1,006      | 1,005  |
| year_1999 | 7,864    | 984        | 983    |
| year_2000 | 7,589    | 949        | 949    |
| year_2001 | 7,181    | 898        | 898    |
| year_2002 | 6,636    | 830        | 829    |
| year_2003 | 6,672    | 834        | 834    |
| year_2004 | 7,111    | 889        | 889    |
| year_2005 | 7,113    | 890        | 889    |
| year_2006 | 7,064    | 883        | 883    |
| year_2007 | 6,683    | 836        | 835    |
| year_2008 | 7,408    | 927        | 926    |
| year_2009 | 7,336    | 917        | 917    |
| year_2010 | 7,013    | 877        | 877    |
| year_2011 | 6,724    | 841        | 840    |
| year_2012 | 6,479    | 810        | 810    |
| year_2013 | 6,372    | 797        | 796    |
| year_2014 | 6,261    | 783        | 783    |
| year_2015 | 6,028    | 754        | 753    |
| year_2016 | 5,812    | 727        | 727    |
| year_2017 | 5,635    | 705        | 704    |
| year_2018 | 5,508    | 689        | 688    |
| year_2019 | 5,354    | 670        | 669    |
| year_2020 | 5,480    | 686        | 685    |

## Dataset Creation

### Source Data

#### Initial Data Collection and Normalization

Initial data was collected and processed by the authors of the research paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round**.

#### Who are the source language producers?

Public firms filing with the SEC.

### Annotations

#### Annotation process

NA

#### Who are the annotators?

NA

### Personal and Sensitive Information

The dataset contains public filings data from SEC.

## Considerations for Using the Data

### Social Impact of Dataset

Low to none.

### Discussion of Biases

The dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature.

### Other Known Limitations

The dataset needs further cleaning for improved performance.

## Additional Information

### Licensing Information

EDGAR data is publicly available.

### Shoutout 
Huge shoutout to [@JanosAudran](https://huggingface.co/JanosAudran) for the HF Card setup!

## Citation
If this work helps or inspires you in any way, please consider citing the relevant paper published at the [3rd Economics and Natural Language Processing (ECONLP) workshop](https://lt3.ugent.be/econlp/) at EMNLP 2021 (Punta Cana, Dominican Republic):
```
@inproceedings{loukas-etal-2021-edgar,
    title = "{EDGAR}-{CORPUS}: Billions of Tokens Make The World Go Round",
    author = "Loukas, Lefteris  and
      Fergadiotis, Manos  and
      Androutsopoulos, Ion  and
      Malakasiotis, Prodromos",
    booktitle = "Proceedings of the Third Workshop on Economics and Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.econlp-1.2",
    pages = "13--18",
}
```

### References

- [Research Paper] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CORPUS: Billions of Tokens Make The World Go Round. Third Workshop on Economics and Natural Language Processing (ECONLP). https://arxiv.org/abs/2109.14394 - Punta Cana, Dominican Republic, November 2021. 

- [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. https://github.com/nlpaueb/edgar-crawler (2021)

- [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. https://zenodo.org/record/5528490 (2021)

- [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. https://zenodo.org/record/5524358 (2021)

- [Applied Research paper where EDGAR-CORPUS is used] Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and, George Paliouras. FiNER: Financial Numeric Entity Recognition for XBRL Tagging. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/2022.acl-long.303 (2022)