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---
datasets:
- HiTZ/AbstRCT-ES
language:
- es
- en
pipeline_tag: token-classification
---
# Cross-lingual Argument Mining in the Medical Domain
This model is a fine-tuned version of mBERT for the argument mining task using AbstRCT data in English and Spanish.
The dataset consists of abstracts of 5 disease types for argument component detection and argument relation classification:
- `neoplasm`: 350 train, 100 dev and 50 test abstracts
- `glaucoma_test`: 100 abstracts
- `mixed_test`: 100 abstracts (20 on glaucoma, 20 on neoplasm, 20 on diabetes, 20 on hypertension, 20 on hepatitis)
The results achieved for each test set:
Test | F1-macro | F1-Claim | F1-Premise
--|-------|-------|-------
Neoplasm | 82.36 | 74.89 | 89.07
Glaucoma | 80.52 | 75.22 | 84.86
Mixed | 81.69 | 75.06 | 88.57
You can find more information:
- 📖 Paper: [Crosslingual Argument Mining in the Medical Domain](https://arxiv.org/abs/2301.10527)
- Code: [https://github.com/ragerri/abstrct-projections/tree/final](https://github.com/ragerri/abstrct-projections/tree/final)
You can load the model as follows:
```python
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained('HiTZ/mbert-argument-mining-es')
```
## Citation
````bibtex
@misc{yeginbergen2024crosslingual,
title={Cross-lingual Argument Mining in the Medical Domain},
author={Anar Yeginbergen and Rodrigo Agerri},
year={2024},
eprint={2301.10527},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
````
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