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--- |
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license: apache-2.0 |
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task_categories: |
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- translation |
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- summarization |
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- text2text-generation |
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language: |
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- ar |
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- en |
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tags: |
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- legal |
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- finance |
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- medical |
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- webdataset |
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pretty_name: BrainData |
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size_categories: |
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- n>1T |
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--- |
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# Dataset Card for BrainData |
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<!-- Provide a quick summary of the dataset. --> |
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This dataset card provides detailed information about the BrainData dataset. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). |
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## Dataset Details |
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### Dataset Description |
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<!-- Provide a longer summary of what this dataset is. --> |
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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. |
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- **Curated by:** Dr. Mohamed El Fadil |
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- **Funded by:** BRAINSAIT LTD |
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- **Shared by:** Dr. Mohamed El Fadil |
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- **Language(s) (NLP):** Arabic, English |
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- **License:** Apache-2.0 |
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### Dataset Sources [optional] |
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<!-- Provide the basic links for the dataset. --> |
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- **Repository:** [BrainData GitHub Repository](https://github.com/brainsait/dataset) |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the dataset is intended to be used. --> |
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### Direct Use |
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<!-- This section describes suitable use cases for the dataset. --> |
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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. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> |
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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. |
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## Dataset Structure |
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<!-- 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. --> |
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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. |
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[More Information Needed] |
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## Dataset Creation |
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### Curation Rationale |
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<!-- Motivation for the creation of this dataset. --> |
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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. |
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### Source Data |
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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The source data includes legal documents, financial reports, medical records, and web data, ensuring a diverse and representative sample of text for each domain. |
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#### Data Collection and Processing |
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
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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. |
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#### Who are the source data producers? |
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<!-- 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. --> |
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The source data was produced by professionals and organizations in the legal, finance, and medical sectors, ensuring authoritative and accurate content. |
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### Annotations [optional] |
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<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> |
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#### Annotation process |
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<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> |
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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. |
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#### Who are the annotators? |
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<!-- This section describes the people or systems who created the annotations. --> |
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The annotators were professionals with expertise in legal, finance, and medical fields, ensuring high-quality and accurate annotations. |
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#### Personal and Sensitive Information |
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<!-- 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. --> |
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The dataset contains sensitive information, particularly in the medical and financial domains. All personal identifiers have been removed or anonymized to protect privacy. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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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. |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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```bibtex |
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@misc{braindata2024dataset, |
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title={BrainData: A Multilingual Dataset for Legal, Finance, and Medical Text Processing}, |
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author={El Fadil, Mohamed and BrainSAIT Team}, |
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year={2024}, |
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url={https://github.com/brainsait/dataset} |
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} |
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