Datasets:

eloukas
/

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: []
	---










































	# 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)