mBERT fine-tuned on English semantic role labeling

Model description

This model is the bert-base-multilingual-cased fine-tuned on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data. This is part of a project from which resulted the following models:

• liaad/srl-pt_bertimbau-base
• liaad/srl-pt_bertimbau-large
• liaad/srl-pt_xlmr-base
• liaad/srl-pt_xlmr-large
• liaad/srl-pt_mbert-base
• liaad/srl-en_xlmr-base
• liaad/srl-en_xlmr-large
• liaad/srl-en_mbert-base
• liaad/srl-enpt_xlmr-base
• liaad/srl-enpt_xlmr-large
• liaad/srl-enpt_mbert-base
• liaad/ud_srl-pt_bertimbau-large
• liaad/ud_srl-pt_xlmr-large
• liaad/ud_srl-enpt_xlmr-large

For more information, please see the accompanying article (See BibTeX entry and citation info below) and the project's github.

Intended uses & limitations

How to use

To use the transformers portion of this model:

from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("liaad/srl-en_mbert-base")
model = AutoModel.from_pretrained("liaad/srl-en_mbert-base")

To use the full SRL model (transformers portion + a decoding layer), refer to the project's github.

Limitations and bias

• The models were trained only for 5 epochs.
• The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data.

Training procedure

The model was trained on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data. They were tested on the PropBank.Br data set as well as on a smaller opinion dataset "Buscapé". For more information, please see the accompanying article (See BibTeX entry and citation info below) and the project's github.

Eval results

BibTeX entry and citation info

@misc{oliveira2021transformers,
      title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling}, 
      author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge},
      year={2021},
      eprint={2101.01213},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}