Edit model card

Cross-lingual Argument Mining in the Medical Domain

This model is a fine-tuned version of mBERT for the argument mining task using AbstRCT data in English and Spanish.
The dataset consists of abstracts of 5 disease types for argument component detection and argument relation classification:

  • neoplasm: 350 train, 100 dev and 50 test abstracts
  • glaucoma_test: 100 abstracts
  • mixed_test: 100 abstracts (20 on glaucoma, 20 on neoplasm, 20 on diabetes, 20 on hypertension, 20 on hepatitis)

The results (F1 macro averaged at token level) achieved for each test set:

Test F1-macro F1-Claim F1-Premise
Neoplasm 82.36 74.89 89.07
Glaucoma 80.52 75.22 84.86
Mixed 81.69 75.06 88.57

You can find more information:

You can load the model as follows:

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained('HiTZ/mbert-argument-mining-es')

Citation

@misc{yeginbergen2024crosslingual,
      title={Cross-lingual Argument Mining in the Medical Domain}, 
      author={Anar Yeginbergen and Rodrigo Agerri},
      year={2024},
      eprint={2301.10527},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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

Downloads last month
22
Safetensors
Model size
177M params
Tensor type
F32
·
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Dataset used to train HiTZ/mbert-argmining-abstrct-en-es

Collection including HiTZ/mbert-argmining-abstrct-en-es