feat: initial dataset release

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README.md

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+---
+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

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