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--- |
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license: apache-2.0 |
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base_model: bert-base-multilingual-cased |
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datasets: |
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- HiTZ/multilingual-abstrct |
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language: |
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- en |
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- es |
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- fr |
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- it |
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metrics: |
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- f1 |
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pipeline_tag: token-classification |
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library_name: transformers |
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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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- text: Dans la comparaison entre les répondeurs et les patients atteints de SD (6m) et de PD, les répondeurs ont indiqué un meilleur bien-être physique (P=.004) et une meilleure humeur (P=.02) au mois 3. |
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- text: Nel confronto tra i responder e i pazienti con SD (6m) e PD, i responder hanno indicato un migliore benessere fisico (P=.004) e umore (P=.02) al terzo mese. |
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--- |
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<p align="center"> |
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<br> |
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<img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="width: 45%;"> |
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<be> |
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# mBERT for multilingual Argument Detection in the Medical Domain |
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) for the argument component |
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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)). |
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## Performance |
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F1-macro scores (at sequence level) and their averages per test set from the argument component detection results of |
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monolingual, monolingual automatically post-processed, multilingual, multilingual automatically post-processed, and crosslingual experiments. |
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<img src="https://raw.githubusercontent.com/hitz-zentroa/multilingual-abstrct/main/resources/multilingual-abstrct-results.png" style="width: 75%;"> |
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### Label Dictionary |
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```` |
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"id2label": { |
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"0": "B-Claim", |
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"1": "B-Premise", |
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"2": "I-Claim", |
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"3": "I-Premise", |
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"4": "O" |
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} |
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```` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 3.0 |
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### Framework versions |
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- Transformers 4.40.0.dev0 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.2 |
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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 |