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--- |
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datasets: |
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- HiTZ/AbstRCT-ES |
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language: |
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- es |
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- en |
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pipeline_tag: token-classification |
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widget: |
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- text: In the comparison of responders versus patients with both SD (6m) and PD, responders indicated better physical well-being (P=.004) and mood (P=.02) at month 3. |
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- text: En la comparación de los que respondieron frente a los pacientes tanto con SD (6m) como con EP, los que respondieron indicaron un mejor bienestar físico (P=.004) y estado de ánimo (P=.02) en el mes 3 |
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--- |
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# Cross-lingual Argument Mining in the Medical Domain |
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This model is a fine-tuned version of mBERT for the argument mining task using AbstRCT data in English and Spanish. |
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The dataset consists of abstracts of 5 disease types for argument component detection and argument relation classification: |
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- `neoplasm`: 350 train, 100 dev and 50 test abstracts |
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- `glaucoma_test`: 100 abstracts |
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- `mixed_test`: 100 abstracts (20 on glaucoma, 20 on neoplasm, 20 on diabetes, 20 on hypertension, 20 on hepatitis) |
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The results (F1 macro averaged at token level) achieved for each test set: |
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Test | F1-macro | F1-Claim | F1-Premise |
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--|-------|-------|------- |
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Neoplasm | 82.36 | 74.89 | 89.07 |
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Glaucoma | 80.52 | 75.22 | 84.86 |
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Mixed | 81.69 | 75.06 | 88.57 |
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You can find more information: |
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- 📖 Paper: [Crosslingual Argument Mining in the Medical Domain](https://arxiv.org/abs/2301.10527) |
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- Code: [https://github.com/ragerri/abstrct-projections](https://github.com/ragerri/abstrct-projections) |
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You can load the model as follows: |
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```python |
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from transformers import AutoModelForSequenceClassification |
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model = AutoModelForSequenceClassification.from_pretrained('HiTZ/mbert-argument-mining-es') |
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```` |
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## Citation |
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````bibtex |
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@misc{yeginbergen2024crosslingual, |
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title={Cross-lingual Argument Mining in the Medical Domain}, |
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author={Anar Yeginbergen and Rodrigo Agerri}, |
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year={2024}, |
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eprint={2301.10527}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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```` |
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**Contact**: [Anar Yeginbergen](https://ixa.ehu.eus/node/13807?language=en) and [Rodrigo Agerri](https://ragerri.github.io/) |
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HiTZ Center - Ixa, University of the Basque Country UPV/EHU |
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