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README.md
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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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tags:
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- generated_from_trainer
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model-index:
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- name: multi_rebuttal_neoplasm_mbert
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results: []
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datasets:
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- HiTZ/multilingual-abstrct
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language:
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metrics:
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- f1
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pipeline_tag: token-classification
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---
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# multi_rebuttal_neoplasm_mbert
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## Model description
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## Training and evaluation data
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More information needed
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## Training procedure
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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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- 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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---
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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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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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<h2 align="center">mBERT for multilingual Argument Detection in the Medical Domain</h2>
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<be>
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# Model Card: mBERT fine-tuned for multilingual (EN,ES,FR,IT) Argument Component Detection
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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]).
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## Performance
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<img src="https://github.com/hitz-zentroa/multilingual-abstrct/blob/65ca5c7452d83bb8d9d534aa401110e570a7ef83/resources/multilingual-abstrct-results.png" style="width: 50%;">
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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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- 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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