---
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](https://huggingface.co/IIC/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](https://arxiv.org/abs/2503.18594).

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

**BibTeX:**

```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: guillem.garcia@iic.uam.es, 100500844@alumnos.uc3m.es