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edgar-corpus

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---
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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: []
---
# 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.
## 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",
}
```
### 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!
### 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)