metadata
language:
- multilingual
- pt
tags:
- xlm-roberta-base
- semantic role labeling
- finetuned
license: Apache 2.0
datasets:
- PropBank.Br
metrics:
- F1 Measure
XLM-R base fine-tuned on Portuguese semantic role labeling
Model description
This model is the xlm-roberta-base
fine-tuned on Portuguese 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-pt_xlmr-base")
model = AutoModel.from_pretrained("liaad/srl-pt_xlmr-base")
To use the full SRL model (transformers portion + a decoding layer), refer to the project's github.
Limitations and bias
- This model does not include a Tensorflow version. This is because the "type_vocab_size" in this model was changed (from 1 to 2) and, therefore, it cannot be easily converted to Tensorflow.
Training procedure
The model was trained on the PropBank.Br datasets, using 10-fold Cross-Validation. The 10 resulting models were tested on the folds 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 | F1 CV PropBank.Br (in domain) | F1 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}
}