Dataset Card for BrainData

This dataset card provides detailed information about the BrainData dataset. It has been generated using this raw template.

Dataset Details

Dataset Description

BrainData is a comprehensive dataset designed for multiple NLP tasks including translation, summarization, and text-to-text generation. It encompasses a variety of domains such as legal, finance, and medical, with content available in both Arabic and English. This extensive dataset is ideal for training robust multilingual models.

Curated by: Dr. Mohamed El Fadil
Funded by: BRAINSAIT LTD
Shared by: Dr. Mohamed El Fadil
Language(s) (NLP): Arabic, English
License: Apache-2.0

Dataset Sources [optional]

Repository: BrainData GitHub Repository
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

BrainData can be used for training models in translation, summarization, and text generation. It is particularly useful for applications in the legal, finance, and medical sectors, where multilingual support is required.

Out-of-Scope Use

The dataset is not suitable for tasks unrelated to text processing, such as image recognition or speech-to-text. Additionally, it should not be used for generating inappropriate or harmful content.

Dataset Structure

The dataset includes text data in both Arabic and English, covering multiple domains. It is structured to support easy access and processing, with clear separations between different task categories and languages.

[More Information Needed]

Dataset Creation

Curation Rationale

BrainData was created to address the need for high-quality multilingual datasets in the fields of legal, finance, and medical text processing. It aims to facilitate the development of advanced NLP models that can operate across different languages and domains.

Source Data

The source data includes legal documents, financial reports, medical records, and web data, ensuring a diverse and representative sample of text for each domain.

Data Collection and Processing

Data was collected from reputable sources in the legal, finance, and medical fields. It underwent thorough filtering and normalization to ensure consistency and quality. Tools such as Python's NLTK and SpaCy libraries were used for text preprocessing.

Who are the source data producers?

The source data was produced by professionals and organizations in the legal, finance, and medical sectors, ensuring authoritative and accurate content.

Annotations [optional]

Annotation process

Annotations were performed by domain experts using tools like Prodigy and Labelbox. Guidelines were provided to ensure consistency, and inter-annotator agreement was regularly checked to maintain quality.

Who are the annotators?

The annotators were professionals with expertise in legal, finance, and medical fields, ensuring high-quality and accurate annotations.

Personal and Sensitive Information

The dataset contains sensitive information, particularly in the medical and financial domains. All personal identifiers have been removed or anonymized to protect privacy.

Bias, Risks, and Limitations

The dataset may contain biases inherent to the source data, such as regional or institutional biases. Users should be aware of these limitations and consider them when developing models.

Recommendations

Users should perform bias analysis and fairness checks when using the dataset, especially for critical applications. Regular updates and retraining with new data are recommended to mitigate biases.

Citation [optional]

BibTeX:

@misc{braindata2024dataset,
  title={BrainData: A Multilingual Dataset for Legal, Finance, and Medical Text Processing},
  author={El Fadil, Mohamed and BrainSAIT Team},
  year={2024},
  url={https://github.com/brainsait/dataset}
}