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mbert-argmining-abstrct-multilingual

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metadata
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: >-
      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.
  - 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.
  - 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.
  - 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.


mBERT for multilingual Argument Detection in the Medical Domain

This model is a fine-tuned version of 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).

Performance

F1-macro scores 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.

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 and Rodrigo Agerri HiTZ Center - Ixa, University of the Basque Country UPV/EHU