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 (at sequence level) 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.

Label Dictionary

"id2label": {
    "0": "B-Claim",
    "1": "B-Premise",
    "2": "I-Claim",
    "3": "I-Premise",
    "4": "O"
  }

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