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## 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)


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"section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1433409319, "num_examples": 5508}, {"name": "validation", "num_bytes": 181466460, "num_examples": 689}, {"name": "test", "num_bytes": 182594965, "num_examples": 688}], "download_size": 1815810567, "dataset_size": 1797470744}, {"config_name": "year_2019", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1421232269, "num_examples": 5354}, {"name": "validation", "num_bytes": 175603562, "num_examples": 670}, {"name": "test", "num_bytes": 176336174, "num_examples": 669}], "download_size": 1791237155, "dataset_size": 1773172005}, {"config_name": "year_2020", "features": [{"name": "filename", "dtype": "string"}, {"name": "cik", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "section_1", "dtype": "string"}, {"name": "section_1A", "dtype": "string"}, {"name": "section_1B", "dtype": "string"}, {"name": "section_2", "dtype": "string"}, {"name": "section_3", "dtype": "string"}, {"name": "section_4", "dtype": "string"}, {"name": "section_5", "dtype": "string"}, {"name": "section_6", "dtype": "string"}, {"name": "section_7", "dtype": "string"}, {"name": "section_7A", "dtype": "string"}, {"name": "section_8", "dtype": "string"}, {"name": "section_9", "dtype": "string"}, {"name": "section_9A", "dtype": "string"}, {"name": "section_9B", "dtype": "string"}, {"name": "section_10", "dtype": "string"}, {"name": "section_11", "dtype": "string"}, {"name": "section_12", "dtype": "string"}, {"name": "section_13", "dtype": "string"}, {"name": "section_14", "dtype": "string"}, {"name": "section_15", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1541847387, "num_examples": 5480}, {"name": "validation", "num_bytes": 193498658, "num_examples": 686}, {"name": "test", "num_bytes": 192600298, "num_examples": 685}], "download_size": 1946916132, "dataset_size": 1927946343}], "tags": ["research papers", "edgar", "sec", "finance", "financial", "filings", "10K", "10-K", "nlp", "research", "econlp", "economics", "business"], "duplicated_from": "eloukas/edgar-corpus"}
TAGS
#task_categories-other #annotations_creators-no-annotation #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|other #language-English #license-apache-2.0 #research papers #edgar #sec #finance #financial #filings #10K #10-K #nlp #research #econlp #economics #business #arxiv-2109.14394 #region-us 

Dataset Card for [EDGAR-CORPUS]
===============================


Table of Contents
-----------------


* Table of Contents
* Dataset Description
	+ Dataset Summary
	+ Supported Tasks
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Licensing Information
	+ References
	+ 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: URL


### 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.  

cik: EDGAR identifier for a firm.  

year: Year of report.  

section\_1: Corressponding section of the Annual Report.  

section\_1A: Corressponding section of the Annual Report.  

section\_1B: Corressponding section of the Annual Report.  

section\_2: Corressponding section of the Annual Report.  

section\_3: Corressponding section of the Annual Report.  

section\_4: Corressponding section of the Annual Report.  

section\_5: Corressponding section of the Annual Report.  

section\_6: Corressponding section of the Annual Report.  

section\_7: Corressponding section of the Annual Report.  

section\_7A: Corressponding section of the Annual Report.  

section\_8: Corressponding section of the Annual Report.  

section\_9: Corressponding section of the Annual Report.  

section\_9A: Corressponding section of the Annual Report.  

section\_9B: Corressponding section of the Annual Report.  

section\_10: Corressponding section of the Annual Report.  

section\_11: Corressponding section of the Annual Report.  

section\_12: Corressponding section of the Annual Report.  

section\_13: Corressponding section of the Annual Report.  

section\_14: Corressponding section of the Annual Report.  

section\_15: Corressponding section of the Annual Report.  



### Data Splits



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 for the HF Card setup!


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 at EMNLP 2021 (Punta Cana, Dominican Republic):


### 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). URL - Punta Cana, Dominican Republic, November 2021.
* [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. URL (2021)
* [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. URL (2021)
* [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. URL (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). URL (2022)



### 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: URL ### 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.  

cik: EDGAR identifier for a firm.  

year: Year of report.  

section\_1: Corressponding section of the Annual Report.  

section\_1A: Corressponding section of the Annual Report.  

section\_1B: Corressponding section of the Annual Report.  

section\_2: Corressponding section of the Annual Report.  

section\_3: Corressponding section of the Annual Report.  

section\_4: Corressponding section of the Annual Report.  

section\_5: Corressponding section of the Annual Report.  

section\_6: Corressponding section of the Annual Report.  

section\_7: Corressponding section of the Annual Report.  

section\_7A: Corressponding section of the Annual Report.  

section\_8: Corressponding section of the Annual Report.  

section\_9: Corressponding section of the Annual Report.  

section\_9A: Corressponding section of the Annual Report.  

section\_9B: Corressponding section of the Annual Report.  

section\_10: Corressponding section of the Annual Report.  

section\_11: Corressponding section of the Annual Report.  

section\_12: Corressponding section of the Annual Report.  

section\_13: Corressponding section of the Annual Report.  

section\_14: Corressponding section of the Annual Report.  

section\_15: Corressponding section of the Annual Report. ### Data Splits



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 for the HF Card setup!


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 at EMNLP 2021 (Punta Cana, Dominican Republic): ### 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). URL - Punta Cana, Dominican Republic, November 2021.
* [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. URL (2021)
* [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. URL (2021)
* [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. URL (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). URL (2022)
TAGS
#task_categories-other #annotations_creators-no-annotation #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|other #language-English #license-apache-2.0 #research papers #edgar #sec #finance #financial #filings #10K #10-K #nlp #research #econlp #economics #business #arxiv-2109.14394 #region-us 
 ### 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: URL ### 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.  

cik: EDGAR identifier for a firm.  

year: Year of report.  

section\_1: Corressponding section of the Annual Report.  

section\_1A: Corressponding section of the Annual Report.  

section\_1B: Corressponding section of the Annual Report.  

section\_2: Corressponding section of the Annual Report.  

section\_3: Corressponding section of the Annual Report.  

section\_4: Corressponding section of the Annual Report.  

section\_5: Corressponding section of the Annual Report.  

section\_6: Corressponding section of the Annual Report.  

section\_7: Corressponding section of the Annual Report.  

section\_7A: Corressponding section of the Annual Report.  

section\_8: Corressponding section of the Annual Report.  

section\_9: Corressponding section of the Annual Report.  

section\_9A: Corressponding section of the Annual Report.  

section\_9B: Corressponding section of the Annual Report.  

section\_10: Corressponding section of the Annual Report.  

section\_11: Corressponding section of the Annual Report.  

section\_12: Corressponding section of the Annual Report.  

section\_13: Corressponding section of the Annual Report.  

section\_14: Corressponding section of the Annual Report.  

section\_15: Corressponding section of the Annual Report. ### Data Splits



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 for the HF Card setup!


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 at EMNLP 2021 (Punta Cana, Dominican Republic): ### 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). URL - Punta Cana, Dominican Republic, November 2021.
* [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. URL (2021)
* [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. URL (2021)
* [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. URL (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). URL (2022)
passage: TAGS
#task_categories-other #annotations_creators-no-annotation #language_creators-other #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-extended|other #language-English #license-apache-2.0 #research papers #edgar #sec #finance #financial #filings #10K #10-K #nlp #research #econlp #economics #business #arxiv-2109.14394 #region-us 
### 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: URL### 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. passage: ### Data Fields


filename: Name of file on EDGAR from which the report was extracted.  

cik: EDGAR identifier for a firm.  

year: Year of report.  

section\_1: Corressponding section of the Annual Report.  

section\_1A: Corressponding section of the Annual Report.  

section\_1B: Corressponding section of the Annual Report.  

section\_2: Corressponding section of the Annual Report.  

section\_3: Corressponding section of the Annual Report.  

section\_4: Corressponding section of the Annual Report.  

section\_5: Corressponding section of the Annual Report.  

section\_6: Corressponding section of the Annual Report.  

section\_7: Corressponding section of the Annual Report.  

section\_7A: Corressponding section of the Annual Report.  

section\_8: Corressponding section of the Annual Report.  

section\_9: Corressponding section of the Annual Report.  

section\_9A: Corressponding section of the Annual Report.  

section\_9B: Corressponding section of the Annual Report.  

section\_10: Corressponding section of the Annual Report.  

section\_11: Corressponding section of the Annual Report.  

section\_12: Corressponding section of the Annual Report.  

section\_13: Corressponding section of the Annual Report.  

section\_14: Corressponding section of the Annual Report.  

section\_15: Corressponding section of the Annual Report.### Data Splits



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 for the HF Card setup!


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 at EMNLP 2021 (Punta Cana, Dominican Republic):