metadata

dataset_info:
  - config_name: dedup
    features:
      - name: text
        dtype: string
      - name: source
        dtype: string
    splits:
      - name: train
        num_bytes: 85241511
        num_examples: 30844
    download_size: 48607995
    dataset_size: 85241511
  - config_name: original
    features:
      - name: text
        dtype: string
      - name: source
        dtype: string
    splits:
      - name: train
        num_bytes: 105400009
        num_examples: 35996
    download_size: 60150578
    dataset_size: 105400009
configs:
  - config_name: dedup
    data_files:
      - split: train
        path: dedup/train-*
  - config_name: original
    data_files:
      - split: train
        path: original/train-*
    default: true
license: mit
task_categories:
  - text-generation
  - mask-generation
language:
  - es
tags:
  - Clinical
  - Spanish
size_categories:
  - 10K<n<100K

ClinText-SP Dataset Card

Dataset Description

ClinText-SP is the largest publicly available Spanish clinical corpus designed to support research in clinical natural language processing. It aggregates a rich collection of clinical texts from diverse open sources, including medical journals, annotated corpora from shared tasks, and supplementary sources like Wikipedia and medical textbooks.

The dataset contains:

35,996 samples with an average of ~700 tokens per sample
Approximately 25.62M tokens in total

ClinText-SP offers a balanced mix of long, well-structured clinical case reports and shorter, schematic texts, making it ideal for a variety of clinical NLP tasks.

Data Sources

The corpus is built from three primary source types:

Medical Journals: Clinical case reports from specialized Spanish-language journals.
Annotated Corpora: Datasets from shared tasks.
Other Sources: Additional clinical knowledge extracted from Wikipedia and select medical textbooks to complement the dataset.

Data Preprocessing

Cleaning & Extraction: Texts were parsed and cleaned from PDFs, HTMLs, and other formats. Extraneous formatting, HTML artifacts, and non-essential metadata (e.g., author names) were removed.
Customized Strategies: Specific regex-based heuristics and LLM-assisted methods (using Qwen2.5) were employed to accurately extract clinical case information.
Deduplication & Language Filtering: Fuzzy deduplication (using MinHash) ensured unique entries, and non-Spanish texts were removed using Python Langdetect.

Intended Use

ClinText-SP is ideal for:

Training and Benchmarking: Facilitating the development of Spanish clinical NLP models, including encoder-based models such as RigoBERTa Clinical.
Domain-Adaptive Pretraining: Serving as a robust resource for adapting language models to the clinical domain.
Research and Application: Advancing clinical language understanding and supporting applications in healthcare AI.

Limitations and Biases

Biases: The dataset may reflect biases inherent to the selected sources and may not cover every clinical specialty.
Coverage: While comprehensive, the dataset might not fully encapsulate the entirety of clinical nuances across all medical fields.
Data Quality: Variations in data quality exist due to the diversity of sources and extraction methods.

For more detailed information, please check the original paper.

Citation

If you use ClinText-SP in your research, please cite the work as follows:

BibTeX:

@misc{subies2025clintextsprigobertaclinicalnew,
      title={ClinText-SP and RigoBERTa Clinical: a new set of open resources for Spanish Clinical NLP}, 
      author={Guillem García Subies and Álvaro Barbero Jiménez and Paloma Martínez Fernández},
      year={2025},
      eprint={2503.18594},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.18594}, 
}

APA:

Subies, G. G., Barbero Jiménez, Á., & Martínez Fernández, P. (2025). ClinText-SP and RigoBERTa Clinical: A new set of open resources for Spanish Clinical NLP. arXiv. https://arxiv.org/abs/2503.18594

Model Card Authors and Contact

Guillem García Subies: [email protected], [email protected]