readme: ---
  language:
  - multilingual
  - pt
  tags:
  - bert-large-portuguese-cased
  - semantic role labeling
  - finetuned
  - dependency parsing
  license: apache-2.0
  datasets:
  - PropBank.Br
  - CoNLL-2012
  - Universal Dependencies
  metrics:
  - F1 Measure
  ---
  
  # BERTimbau large fine-tune in Portuguese Universal Dependencies and semantic role labeling
  
  ## Model description
  
  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:
  
  * [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/ud_srl-pt_bertimbau-large")
  model = AutoModel.from_pretrained("liaad/ud_srl-pt_bertimbau-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
  
  - The model was trained only for 10 epochs in the Universal Dependencies dataset.
  
  ## Training procedure
  
  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).
  
  ## 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}
  }
  ```