---
license: apache-2.0
base_model: bert-base-multilingual-cased
datasets:
- HiTZ/multilingual-abstrct
language:
- en
- es
- fr
- it
metrics:
- f1
pipeline_tag: token-classification
library_name: transformers
widget:
- text: The dysuria resolved faster in patients implanted with 103Pd but was unaffected by the use of supplemental radiotherapy and/or androgen deprivation therapy.
- text: La disuria se resolvió más rápidamente en los pacientes implantados con 103Pd, pero no se vio afectada por el uso de radioterapia suplementaria y/o terapia de privación de andrógenos.
- text: La dysurie s'est résorbée plus rapidement chez les patients implantés avec du 103Pd, mais n'a pas été affectée par l'utilisation d'une radiothérapie complémentaire et/ou d'une thérapie de privation d'androgènes.
- text: La disuria si è risolta più rapidamente nei pazienti impiantati con 103Pd, ma non è stata influenzata dall'uso della radioterapia supplementare e/o della terapia di deprivazione androgenica.
---

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    <br>
    <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="width: 45%;">
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# mBERT for multilingual Argument Detection in the Medical Domain


This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) for the argument component 
detection task on AbstRCT data in English, Spanish, French and Italian ([https://huggingface.co/datasets/HiTZ/multilingual-abstrct](https://huggingface.co/datasets/HiTZ/multilingual-abstrct)).


## Performance

F1-macro scores (at sequence level) and their averages per test set from the argument component detection results of
monolingual, monolingual automatically post-processed, multilingual, multilingual automatically post-processed, and crosslingual experiments.

<img src="https://raw.githubusercontent.com/hitz-zentroa/multilingual-abstrct/main/resources/multilingual-abstrct-results.png" style="width: 75%;">

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2

**Contact**: [Anar Yeginbergen](https://ixa.ehu.eus/node/13807?language=en) and [Rodrigo Agerri](https://ragerri.github.io/)
HiTZ Center - Ixa, University of the Basque Country UPV/EHU