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language: | |
- multilingual | |
- pt | |
tags: | |
- xlm-roberta-large | |
- semantic role labeling | |
- finetuned | |
license: apache-2.0 | |
datasets: | |
- PropBank.Br | |
metrics: | |
- F1 Measure | |
# XLM-R large fine-tuned on Portuguese semantic role labeling | |
## Model description | |
This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) 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_xlmr-large") | |
model = AutoModel.from_pretrained("liaad/srl-pt_xlmr-large") | |
``` | |
To use the full SRL model (transformers portion + a decoding layer), refer to the [project's github](https://github.com/asofiaoliveira/srl_bert_pt). | |
#### 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](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} | |
} | |
``` |