---
language:
- en
- de
tags:
- financial
- bilingual
- pdf
pretty_name: FinCorpus-DE10k
annotations_creators:
- no-annotation
language_creators:
- found
size_categories:
- 10K<n<100K
license: cc-by-nc-nd-4.0
dataset_info:
- config_name: BBK_monthly
  features:
  - name: filename
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
  download_size: 271752073
  dataset_size: 0
- config_name: Law
  features:
  - name: filename
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 25707085
    num_examples: 134
  download_size: 271752073
  dataset_size: 25707085
- config_name: all
  features:
  - name: filename
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 946487016
    num_examples: 10402
  download_size: 271752073
  dataset_size: 946487016
- config_name: Annual_reports
  features:
  - name: filename
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 54268688
    num_examples: 87
  download_size: 271752073
  dataset_size: 54268688
- config_name: Final_terms
  features:
  - name: filename
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 601219720
    num_examples: 9591
  download_size: 271752073
  dataset_size: 601219720
- config_name: Base_prospectuses
  features:
  - name: filename
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 265291523
    num_examples: 590
  download_size: 271752073
  dataset_size: 265291523
---


# Dataset Card for FinCorpus-DE10k

<!-- Provide a quick summary of the dataset. -->

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

FinCorpus-DE10k is a corpus containing 12,235 PDF files of financial documents, mostly security prospectuses, along with plaintext files for approximately 10,500 of these documents. The documents are primarily in German (71%), with the rest being bilingual (German and English). This dataset aims to facilitate tasks like text analysis, language modeling, and document understanding in the financial domain.

This dataset is a subset of the above dataset, with the collections we felt comfortable releasing under permissive CC licenses. It omits the IFRS (containing 7 documents) and Informational_materials (127/129 txt/pdf files) collections. To get access to the full corpus, get in touch with us.

- **Curated by:** Nata Kozaeva, Serhii Hamotskyi, Christian Hänig
- **Language(s) (NLP):** German (DE), Bilingual (German and English)
- **License:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) except the monthly and annual reports, which are [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/).

It's composed of multiple collections, with the text content available as dataset configs as:
- Annual_reports
- BBK_monthly
- Base_prospectuses
- Final_terms
- Law

(By default, all collections are downloaded).

The entire corpus, pdf and txt files, can be downloaded here: [https://huggingface.co/datasets/anhaltai/fincorpus-de-10k/resolve/main/data/corpus_safe.zip?download=true](https://huggingface.co/datasets/anhaltai/fincorpus-de-10k/resolve/main/data/corpus_safe.zip?download=true)

### Dataset Sources
The FinCorpus-DE10k dataset is composed of financial documents from various collections, each with its unique characteristics and source of origin. The documents were primarily sourced from the websites of financial institutions, regulatory bodies, and publicly available databases, with significant contributions from the Deutsche Bundesbank. The dataset includes:

- **Final Terms Prospectuses**: These documents detail the terms and conditions of the issuance of financial securities, predominantly collected by the Deutsche Bundesbank. They form the largest part of the dataset, with documents ranging from 1 to 719 pages, but mainly under 100 pages.
- **Base Prospectuses**: Containing information about the issuer, description of the security, and the summary of the prospectus. These documents are longer and fewer compared to the Final Terms but hold comprehensive information required for investors.
- **Bundesbank Monthly Reports**: Consisting of 838 monthly reports from the German Bundesbank, spanning from 1949 to 2022. These documents offer a historical perspective on the German financial language. We didn't extract text from these documents.  **Licensed [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/)**
- **Annual Reports**: This collection includes annual (and some quarterly) reports from the Bundesbank and other institutions, covering economic and financial issues, monetary policy, and financial stability risks. **Licensed [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/)**
- **Law**: Featuring German laws in the financial and related domains, including some English translations. This collection reflects the regulations applicable to the financial sector in Germany and EU Directives implemented into German law.

The collection as a whole is licensed [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) except where stated otherwise.

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/AnhaltAI/fincorpus-de-10k-scripts/ 
- **Paper:** **TODO**


## Uses

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

By providing a rich collection of financial documents in PDF format, the dataset facilitates the development of algorithms that can navigate the complex layouts typically found in financial documents.
FinCorpus-DE10k is also suited for developing and testing NLP models specialized in the financial domain, including but not limited to information extraction, named entity recognition, and specialized language models.

<!--
### Out-of-Scope Use

// This section addresses misuse, malicious use, and uses that the dataset will not work well for.

The dataset is not designed for non-NLP tasks or NLP tasks outside the financial domain.
-->

## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
When used through `load_dataset()`, the dataset has two features: `filename` and `text`, one instance per .txt document.

The complete dataset, pdf and txt, can be found in [corpus.zip](https://huggingface.co/datasets/anhaltai/fincorpus-de-10k/resolve/main/data/corpus_safe.zip?download=true). In the archive, `metadata.csv` contains the path for the PDF and its extracted .txt (if available), as well as collection name, presence of extracted text, paths to PDF and .txt files, document language(s), and financial identifiers like ISIN and country for relevant documents.

The pdf and txt subfolders contain the same mirrored directory structure, sorted by collection.

## Dataset Creation

<!--
### Curation Rationale

// Motivation for the creation of this dataset.

Created to support research in financial document analysis, facilitating advancements in financial technology, regulatory compliance, and economic research.
-->

### Source Data

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

Extensive preprocessing was applied to ensure the quality and uniformity of the dataset. It's described in our paper: **TODO**

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

The documents were produced by various financial institutions, regulatory bodies, companies, and the Deutsche Bundesbank.

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

The dataset contains financial documents that are publicly available. 

#### Licensing

We diligently adhered to the licensing guidelines to the best of our understanding. However, the responsibility for the use of the documents and compliance with applicable laws rests with you.

Get in touch with us if any of the documents need to be removed from the collection.

Relevant links are:
- Bundesbank monthly+annual allows using its documents if they are unchanged, hence the CC BY-NC-ND license: [Nutzungsbedingungen - Für den allgemeinen Gebrauch der Website \| Deutsche Bundesbank](https://www.bundesbank.de/de/startseite/benutzerhinweise/nutzungsbedingungen-fuer-den-allgemeinen-gebrauch-der-website-763554#tar-4)
- German laws are public domain: [Act on Copyright and Related Rights (Urheberrechtsgesetz – UrhG)](https://www.gesetze-im-internet.de/englisch_urhg/englisch_urhg.html#p0037)
- Final terms documents (and their Basisprospekte) can be considered public domain (at least their textual content), since the relevant EU regulation _mandates_ they are published and freely accessible: [L_2017168EN.01001201.xml](https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX%3A32017R1129)


## Citation 

Temporary citation until paper is published:
```
@inproceedings{hamotskyi-etal-2024-fincorpus,
  title = {{{FinCorpus-DE10k}}: {{A}} Corpus for the German Financial Domain},
  booktitle = {The 2024 {{Joint International Conference}} on {{Computational Linguistics}}, {{Language Resources}} and {{Evaluation}} ({{LREC-COLING}} 2024)},
  author = {Hamotskyi, Serhii and Kozaeva, Nata and H{\"a}nig, Christian},
  year = {2024},
  month = may,
  publisher = {European Language Resources Association},
  address = {Torino, Italy},
  abstract = {We introduce a predominantly German corpus comprising 12.5k PDF documents sourced from the financial domain. The corresponding extracted textual data encompasses more than 165 million tokens derived predominantly from German, and to a lesser extent, bilingual documents.  We provide detailed information about the document types included in the corpus, such as final terms, base prospectuses, annual reports, information materials, law documents, international financial reporting standards, and monthly reports from the Bundesbank, accompanied by comprehensive statistical analysis. To our knowledge, it is the first non-email German financial corpus available, and we hope it will fill this gap and foster further research in the financial domain both in the German language and in multilingual contexts.}
}
```