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--- |
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language: |
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- multilingual |
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- pt |
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tags: |
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- bert-large-portuguese-cased |
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- semantic role labeling |
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- finetuned |
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- dependency parsing |
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license: Apache 2.0 |
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datasets: |
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- PropBank.Br |
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- CoNLL-2012 |
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- Universal Dependencies |
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metrics: |
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- F1 Measure |
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--- |
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# BERTimbau large fine-tune in Portuguese Universal Dependencies and semantic role labeling |
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## Model description |
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This model is the [`neuralmind/bert-large-portuguese-cased`](https://huggingface.co/neuralmind/bert-large-portuguese-cased) fine-tuned first on the Universal Dependencies Portuguese dataset and then fine-tuned on the PropBank.Br data. This is part of a project from which resulted the following models: |
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* [liaad/srl-pt_bertimbau-base](https://huggingface.co/liaad/srl-pt_bertimbau-base) |
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* [liaad/srl-pt_bertimbau-large](https://huggingface.co/liaad/srl-pt_bertimbau-large) |
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* [liaad/srl-pt_xlmr-base](https://huggingface.co/liaad/srl-pt_xlmr-base) |
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* [liaad/srl-pt_xlmr-large](https://huggingface.co/liaad/srl-pt_xlmr-large) |
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* [liaad/srl-pt_mbert-base](https://huggingface.co/liaad/srl-pt_mbert-base) |
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* [liaad/srl-en_xlmr-base](https://huggingface.co/liaad/srl-en_xlmr-base) |
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* [liaad/srl-en_xlmr-large](https://huggingface.co/liaad/srl-en_xlmr-large) |
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* [liaad/srl-en_mbert-base](https://huggingface.co/liaad/srl-en_mbert-base) |
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* [liaad/srl-enpt_xlmr-base](https://huggingface.co/liaad/srl-enpt_xlmr-base) |
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* [liaad/srl-enpt_xlmr-large](https://huggingface.co/liaad/srl-enpt_xlmr-large) |
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* [liaad/srl-enpt_mbert-base](https://huggingface.co/liaad/srl-enpt_mbert-base) |
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* [liaad/ud_srl-pt_bertimbau-large](https://huggingface.co/liaad/ud_srl-pt_bertimbau-large) |
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* [liaad/ud_srl-pt_xlmr-large](https://huggingface.co/liaad/ud_srl-pt_xlmr-large) |
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* [liaad/ud_srl-enpt_xlmr-large](https://huggingface.co/liaad/ud_srl-enpt_xlmr-large) |
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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). |
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## Intended uses & limitations |
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#### How to use |
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To use the transformers portion of this model: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("liaad/ud_srl-pt_bertimbau-large") |
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model = AutoModel.from_pretrained("liaad/ud_srl-pt_bertimbau-large") |
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``` |
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To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). |
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#### Limitations and bias |
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- The model was trained only for 10 epochs in the Universal Dependencies dataset. |
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## Training procedure |
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The model was trained on the Universal Dependencies Portuguese dataset; then on the CoNLL formatted OntoNotes v5.0; then on Portuguese semantic role labeling data (PropBank.Br) 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). |
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## Eval results |
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| Model Name | F<sub>1</sub> CV PropBank.Br (in domain) | F<sub>1</sub> Buscapé (out of domain) | |
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| --------------- | ------ | ----- | |
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| `srl-pt_bertimbau-base` | 76.30 | 73.33 | |
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| `srl-pt_bertimbau-large` | 77.42 | 74.85 | |
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| `srl-pt_xlmr-base` | 75.22 | 72.82 | |
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| `srl-pt_xlmr-large` | 77.59 | 73.84 | |
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| `srl-pt_mbert-base` | 72.76 | 66.89 | |
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| `srl-en_xlmr-base` | 66.59 | 65.24 | |
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| `srl-en_xlmr-large` | 67.60 | 64.94 | |
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| `srl-en_mbert-base` | 63.07 | 58.56 | |
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| `srl-enpt_xlmr-base` | 76.50 | 73.74 | |
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| `srl-enpt_xlmr-large` | **78.22** | 74.55 | |
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| `srl-enpt_mbert-base` | 74.88 | 69.19 | |
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| `ud_srl-pt_bertimbau-large` | 77.53 | 74.49 | |
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| `ud_srl-pt_xlmr-large` | 77.69 | 74.91 | |
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| `ud_srl-enpt_xlmr-large` | 77.97 | **75.05** | |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{oliveira2021transformers, |
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title={Transformers and Transfer Learning for Improving Portuguese Semantic Role Labeling}, |
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author={Sofia Oliveira and Daniel Loureiro and Alípio Jorge}, |
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year={2021}, |
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eprint={2101.01213}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
