Rigoberta Clinical

🚀 RigoBERTa Clinical

RigoBERTa Clinical is a cutting - edge clinical encoder language model for Spanish. It is developed through domain - adaptive pretraining on ClinText-SP, the largest publicly available Spanish clinical corpus. This model notably enhances performance on multiple clinical NLP benchmarks and offers robust language understanding in the clinical domain.

🚀 Quick Start

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✨ Features

• State - of - the - art Performance: Significantly improves performance on multiple clinical NLP benchmarks.
• Domain - Adaptive Pretraining: Trained on the largest open Spanish clinical corpus, ClinText-SP.
• Robust Language Understanding: Offers strong language understanding capabilities in the Spanish clinical domain.

📦 Installation

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💻 Usage Examples

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📚 Documentation

Model Details

Model Description

RigoBERTa Clinical is created by further pretraining the general - purpose RigoBERTa 2 on a carefully curated clinical corpus. The pretraining uses masked language modeling (MLM) to adapt the model’s linguistic knowledge to the Spanish clinical domain.

Model Sources

• Paper: ClinText-SP and RigoBERTa Clinical: a new set of open resources for Spanish Clinical NLP

Intended Use & Limitations

Intended Use

RigoBERTa Clinical is designed for:

• Understanding clinical text in Spanish.
• Healthcare NLP tasks such as clinical note classification, entity recognition in clinical texts, and related downstream tasks.
• Research and development, including benchmarking and further model adaptation.

Limitations & Caveats

⚠️ Important Note

• Domain Specificity: Although highly effective for Spanish clinical texts, the model may not generalize to other domains or languages.
• Data Biases: ClinText-SP, while the largest corpus available, may contain biases due to source selection and the inherent limitations of public clinical data.
• Operational Cost: Despite being an encoder - based model with relatively lower computational costs compared to generative LLMs, deployment in resource - constrained settings should be carefully evaluated.

Training Details

Training Data: ClinText-SP

ClinText-SP is the largest open Spanish clinical corpus and includes data from various open sources:

• Volume: ~26 million tokens, 35,996 samples
• Sample Details: Average of ~700 tokens per sample; contains both long - form clinical cases and shorter, schematic texts.
• Sources: Medical journals, clinical shared tasks, radiological reports, and Wikipedia extracts.
• Availability: ClinText-SP on Hugging Face Datasets

Training Procedure

Preprocessing

• Tokenizer: Uses the tokenizer from RigoBERTa 2 to ensure consistency with the base model.
• Handling Long Sequences: Clinical texts exceeding 512 tokens are segmented with a stride of 128 tokens; shorter sequences are padded as necessary.
• OOV Handling: Out - of - vocabulary words are managed using subword tokenization, maintaining robust handling of clinical terminology.

Training Details

• Objective: Masked Language Modeling (MLM)
• Epochs: 2 full epochs (with the best model selected after ~1.8 epochs, based on downstream performance)
• Hyperparameters Grid:
  • Batch Sizes: 32, 64, 128
  • Learning Rates: Ranges of {5e - 6, 1e - 5, 2e - 5} for batch size 32, {1e - 5, 2e - 5, 4e - 5} for 64, and {1e - 5, 4e - 5, 8e - 5} for 128
• Best Settings: Batch size = 32, Learning rate = 2e - 5, ~2800 training steps (~1.8 epochs)
• Optimizer: AdamW with weight decay of 0.1
• Hardware: Trained on a single NVIDIA A100 GPU (80GB memory)

Evaluation

RigoBERTa Clinical was evaluated on several Spanish clinical NLP tasks including Named Entity Recognition (NER) and multilabel classification. Evaluation metrics (F1 score and micro - averaged F1) indicate that the model outperforms previous clinical and general Spanish language models.

Key Results:

• Achieves top performance on datasets such as cantemist, meddocan, and livingner1, among others.
• Consistently surpasses the performance of models that were trained solely on clinical data, demonstrating the advantage of leveraging general domain knowledge during domain adaptation.
• Detailed benchmarking results and comparisons are provided in the associated publication.

For a full breakdown of results (including performance on multilingual baselines and other clinical - specific models), please refer to Table 1 and the Nemenyi plot in the original paper.

Citation

If you use RigoBERTa Clinical in your research, please cite the associated paper:

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

📄 License

This model is licensed under the rigoclinical - nc license.