bert-base-multilingual-cased semantic role labeling finetuned

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:

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

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

Model Name F<sub>1</sub> CV PropBank.Br (in domain) F<sub>1</sub> Buscapé (out of domain)
srl-pt_bertimbau-base 76.30 73.33
srl-pt_bertimbau-large 77.42 74.85
srl-pt_xlmr-base 75.22 72.82
srl-pt_xlmr-large 77.59 73.84
srl-pt_mbert-base 72.76 66.89
srl-en_xlmr-base 66.59 65.24
srl-en_xlmr-large 67.60 64.94
srl-en_mbert-base 63.07 58.56
srl-enpt_xlmr-base 76.50 73.74
srl-enpt_xlmr-large 78.22 74.55
srl-enpt_mbert-base 74.88 69.19
ud_srl-pt_bertimbau-large 77.53 74.49
ud_srl-pt_xlmr-large 77.69 74.91
ud_srl-enpt_xlmr-large 77.97 75.05

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}
}