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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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- xlm-roberta-large
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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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# XLM-R 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 [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) 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_xlmr-large")
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model = AutoModel.from_pretrained("liaad/ud_srl-pt_xlmr-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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- 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.
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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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``` |
