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--- |
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license: mit |
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datasets: |
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- IEETA/SPACCC-Spanish-NER |
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language: |
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- es |
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metrics: |
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- f1 |
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--- |
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# Model Card for Biomedical Named Entity Recognition in Spanish Clinical Texts |
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Our model focuses on Biomedical Named Entity Recognition (NER) in Spanish clinical texts, crucial for automated information extraction in medical research and treatment improvements. It proposes a novel approach using a Multi-Head Conditional Random Field (CRF) classifier to tackle multi-class NER tasks, overcoming challenges of overlapping entity instances. The classes it recognizes include symptoms, procedures, diseases, chemicals, and proteins. |
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We provide 4 different models, available as branches of this repository. |
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## Model Details |
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### Model Description |
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- **Developed by:** IEETA |
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- **Model type:** Multi-Head-CRF, Roberta Base |
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- **Language(s) (NLP):** Spanish |
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- **License:** MIT |
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- **Finetuned from model:** lcampillos/roberta-es-clinical-trials-ner |
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### Model Sources |
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- **Repository:** [IEETA Multi-Head-CRF GitHub](https://github.com/ieeta-pt/Multi-Head-CRF) |
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- **Paper:** Multi-head CRF classifier for biomedical multi-class Named Entity Recognition on Spanish clinical notes [Awaiting Publication] |
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**Authors:** |
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- Richard A A Jonker ([ORCID: 0000-0002-3806-6940](https://orcid.org/0000-0002-3806-6940)) |
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- Tiago Almeida ([ORCID: 0000-0002-4258-3350](https://orcid.org/0000-0002-4258-3350)) |
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- Rui Antunes ([ORCID: 0000-0003-3533-8872](https://orcid.org/0000-0003-3533-8872)) |
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- João R Almeida ([ORCID: 0000-0003-0729-2264](https://orcid.org/0000-0003-0729-2264)) |
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- Sérgio Matos ([ORCID: 0000-0003-1941-3983](https://orcid.org/0000-0003-1941-3983)) |
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## Uses |
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Note we do not take any liability for the use of the model in any professional/medical domain. The model is intended for academic purposes only. It performs Named Entity Recognition over 5 classes namely: SYMPTOM PROCEDURE DISEASE PROTEIN CHEMICAL |
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## How to Get Started with the Model |
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Please refer to our GitHub repository for more information on how to train the model and run inference: [IEETA Multi-Head-CRF GitHub](https://github.com/ieeta-pt/Multi-Head-CRF) |
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## Training Details |
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### Training Data |
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The training data can be found on IEETA/SPACCC-Spanish-NER, which is further described on the dataset card. |
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The dataset used consists of 4 seperate datasets: |
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- [SympTEMIST](https://zenodo.org/records/10635215) |
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- [MedProcNER](https://zenodo.org/records/8224056) |
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- [DisTEMIST](https://zenodo.org/records/7614764) |
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- [PharmaCoNER](https://zenodo.org/records/4270158) |
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### Speeds, Sizes, Times |
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The models were trained using an Nvidia Quadro RTX 8000. The models for 5 classes took approximately 1 hour to train and occupy around 1GB of disk space. Additionally, this model shows linear complexity (+8 minutes) per entity class to classify. |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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The testing data can be found on IEETA/SPACCC-Spanish-NER, which is further described on the dataset card. |
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#### Metrics |
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The models were evaluated using the micro-averaged F1-score metric, the standard for entity recognition tasks. |
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### Results |
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We provide 4 separate models with various hyperparameter changes: |
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| HLs per head | Augmentation | Percentage Tags | Augmentation Probability | F1 | |
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|--------------|--------------|-----------------|--------------------------|--------| |
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| 3 | Random | 0.25 | 0.50 | 78.73 | |
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| 3 | Unknown | 0.50 | 0.25 | 78.50 | |
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| 3 | None | - | - | **78.89** | |
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| 1 | Random | 0.25 | 0.50 | **78.89** | |
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All models are trained with a context size of 32 tokens for 60 epochs. |
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## Citation |
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**BibTeX:** |
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[Awaiting Publication] |
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