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| language: | |
| - multilingual | |
| - pt | |
| tags: | |
| - bert-base-multilingual-cased | |
| - semantic role labeling | |
| - finetuned | |
| license: apache-2.0 | |
| datasets: | |
| - PropBank.Br | |
| metrics: | |
| - F1 Measure | |
| # mBERT fine-tuned on Portuguese semantic role labeling | |
| ## Model description | |
| This model is the [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) 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](https://huggingface.co/liaad/srl-pt_bertimbau-base) | |
| * [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large) | |
| * [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base) | |
| * [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large) | |
| * [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base) | |
| * [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base) | |
| * [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large) | |
| * [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base) | |
| * [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base) | |
| * [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large) | |
| * [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base) | |
| * [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large) | |
| * [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large) | |
| * [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/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](https://github.com/asofiaoliveira/srl_bert_pt). | |
| ## Intended uses & limitations | |
| #### How to use | |
| To use the transformers portion of this model: | |
| ```python | |
| from transformers import AutoTokenizer, AutoModel | |
| tokenizer = AutoTokenizer.from_pretrained("liaad/srl-pt_mbert-base") | |
| model = AutoModel.from_pretrained("liaad/srl-pt_mbert-base") | |
| ``` | |
| To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). | |
| ## 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](https://github.com/asofiaoliveira/srl_bert_pt). | |
| ## 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 | |
| ```bibtex | |
| @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} | |
| } | |
| ``` |