

# XLM-R large fine-tuned in English and Portuguese semantic role labeling

## Model description

This model is the [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data 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/srl-enpt_xlmr-large")
model = AutoModel.from_pretrained("liaad/srl-enpt_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.
- The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data.


## Training procedure

The model was first fine-tuned on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data; then it was fine-tuned in the PropBank.Br dataset using 10-fold Cross-Validation. The 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}
}
```
{"language": ["multilingual", "pt", "en"], "license": "apache-2.0", "tags": ["xlm-roberta-large", "semantic role labeling", "finetuned"], "datasets": ["PropBank.Br", "CoNLL-2012"], "metrics": ["F1 Measure"]}
transformers pytorch xlm-roberta feature-extraction xlm-roberta-large semantic role labeling finetuned multilingual pt en dataset:PropBank.Br dataset:CoNLL-2012 arxiv:2101.01213 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #multilingual #pt #en #dataset-PropBank.Br #dataset-CoNLL-2012 #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us 

XLM-R large fine-tuned in English and Portuguese semantic role labeling
=======================================================================


Model description
-----------------


This model is the 'xlm-roberta-large' fine-tuned first on the English CoNLL formatted OntoNotes v5.0 semantic role labeling data 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
* liaad/srl-pt\_bertimbau-large
* liaad/srl-pt\_xlmr-base
* liaad/srl-pt\_xlmr-large
* liaad/srl-pt\_mbert-base
* liaad/srl-en\_xlmr-base
* liaad/srl-en\_xlmr-large
* liaad/srl-en\_mbert-base
* liaad/srl-enpt\_xlmr-base
* liaad/srl-enpt\_xlmr-large
* liaad/srl-enpt\_mbert-base
* liaad/ud\_srl-pt\_bertimbau-large
* liaad/ud\_srl-pt\_xlmr-large
* 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.


Intended uses & limitations
---------------------------


#### How to use


To use the transformers portion of this model:


To use the full SRL model (transformers portion + a decoding layer), refer to the project's github.


#### 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.
* The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data.


Training procedure
------------------


The model was first fine-tuned on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data; then it was fine-tuned in the PropBank.Br dataset using 10-fold Cross-Validation. The 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.


Eval results
------------


Model Name: 'srl-pt\_bertimbau-base', F1 CV PropBank.Br (in domain): 76.30, F1 Buscapé (out of domain): 73.33
Model Name: 'srl-pt\_bertimbau-large', F1 CV PropBank.Br (in domain): 77.42, F1 Buscapé (out of domain): 74.85
Model Name: 'srl-pt\_xlmr-base', F1 CV PropBank.Br (in domain): 75.22, F1 Buscapé (out of domain): 72.82
Model Name: 'srl-pt\_xlmr-large', F1 CV PropBank.Br (in domain): 77.59, F1 Buscapé (out of domain): 73.84
Model Name: 'srl-pt\_mbert-base', F1 CV PropBank.Br (in domain): 72.76, F1 Buscapé (out of domain): 66.89
Model Name: 'srl-en\_xlmr-base', F1 CV PropBank.Br (in domain): 66.59, F1 Buscapé (out of domain): 65.24
Model Name: 'srl-en\_xlmr-large', F1 CV PropBank.Br (in domain): 67.60, F1 Buscapé (out of domain): 64.94
Model Name: 'srl-en\_mbert-base', F1 CV PropBank.Br (in domain): 63.07, F1 Buscapé (out of domain): 58.56
Model Name: 'srl-enpt\_xlmr-base', F1 CV PropBank.Br (in domain): 76.50, F1 Buscapé (out of domain): 73.74
Model Name: 'srl-enpt\_xlmr-large', F1 CV PropBank.Br (in domain): 78.22, F1 Buscapé (out of domain): 74.55
Model Name: 'srl-enpt\_mbert-base', F1 CV PropBank.Br (in domain): 74.88, F1 Buscapé (out of domain): 69.19
Model Name: 'ud\_srl-pt\_bertimbau-large', F1 CV PropBank.Br (in domain): 77.53, F1 Buscapé (out of domain): 74.49
Model Name: 'ud\_srl-pt\_xlmr-large', F1 CV PropBank.Br (in domain): 77.69, F1 Buscapé (out of domain): 74.91
Model Name: 'ud\_srl-enpt\_xlmr-large', F1 CV PropBank.Br (in domain): 77.97, F1 Buscapé (out of domain): 75.05


### BibTeX entry and citation info



#### How to use


To use the transformers portion of this model:


To use the full SRL model (transformers portion + a decoding layer), refer to the project's github. #### 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.
* The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data.


Training procedure
------------------


The model was first fine-tuned on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data; then it was fine-tuned in the PropBank.Br dataset using 10-fold Cross-Validation. The 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.


Eval results
------------


Model Name: 'srl-pt\_bertimbau-base', F1 CV PropBank.Br (in domain): 76.30, F1 Buscapé (out of domain): 73.33
Model Name: 'srl-pt\_bertimbau-large', F1 CV PropBank.Br (in domain): 77.42, F1 Buscapé (out of domain): 74.85
Model Name: 'srl-pt\_xlmr-base', F1 CV PropBank.Br (in domain): 75.22, F1 Buscapé (out of domain): 72.82
Model Name: 'srl-pt\_xlmr-large', F1 CV PropBank.Br (in domain): 77.59, F1 Buscapé (out of domain): 73.84
Model Name: 'srl-pt\_mbert-base', F1 CV PropBank.Br (in domain): 72.76, F1 Buscapé (out of domain): 66.89
Model Name: 'srl-en\_xlmr-base', F1 CV PropBank.Br (in domain): 66.59, F1 Buscapé (out of domain): 65.24
Model Name: 'srl-en\_xlmr-large', F1 CV PropBank.Br (in domain): 67.60, F1 Buscapé (out of domain): 64.94
Model Name: 'srl-en\_mbert-base', F1 CV PropBank.Br (in domain): 63.07, F1 Buscapé (out of domain): 58.56
Model Name: 'srl-enpt\_xlmr-base', F1 CV PropBank.Br (in domain): 76.50, F1 Buscapé (out of domain): 73.74
Model Name: 'srl-enpt\_xlmr-large', F1 CV PropBank.Br (in domain): 78.22, F1 Buscapé (out of domain): 74.55
Model Name: 'srl-enpt\_mbert-base', F1 CV PropBank.Br (in domain): 74.88, F1 Buscapé (out of domain): 69.19
Model Name: 'ud\_srl-pt\_bertimbau-large', F1 CV PropBank.Br (in domain): 77.53, F1 Buscapé (out of domain): 74.49
Model Name: 'ud\_srl-pt\_xlmr-large', F1 CV PropBank.Br (in domain): 77.69, F1 Buscapé (out of domain): 74.91
Model Name: 'ud\_srl-enpt\_xlmr-large', F1 CV PropBank.Br (in domain): 77.97, F1 Buscapé (out of domain): 75.05 ### BibTeX entry and citation info
TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #multilingual #pt #en #dataset-PropBank.Br #dataset-CoNLL-2012 #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us 
 #### How to use


To use the transformers portion of this model:


To use the full SRL model (transformers portion + a decoding layer), refer to the project's github. #### 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.
* The English data was preprocessed to match the Portuguese data, so there are some differences in role attributions and some roles were removed from the data.


Training procedure
------------------


The model was first fine-tuned on the CoNLL-2012 dataset, preprocessed to match the Portuguese PropBank.Br data; then it was fine-tuned in the PropBank.Br dataset using 10-fold Cross-Validation. The 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.


Eval results
------------


Model Name: 'srl-pt\_bertimbau-base', F1 CV PropBank.Br (in domain): 76.30, F1 Buscapé (out of domain): 73.33
Model Name: 'srl-pt\_bertimbau-large', F1 CV PropBank.Br (in domain): 77.42, F1 Buscapé (out of domain): 74.85
Model Name: 'srl-pt\_xlmr-base', F1 CV PropBank.Br (in domain): 75.22, F1 Buscapé (out of domain): 72.82
Model Name: 'srl-pt\_xlmr-large', F1 CV PropBank.Br (in domain): 77.59, F1 Buscapé (out of domain): 73.84
Model Name: 'srl-pt\_mbert-base', F1 CV PropBank.Br (in domain): 72.76, F1 Buscapé (out of domain): 66.89
Model Name: 'srl-en\_xlmr-base', F1 CV PropBank.Br (in domain): 66.59, F1 Buscapé (out of domain): 65.24
Model Name: 'srl-en\_xlmr-large', F1 CV PropBank.Br (in domain): 67.60, F1 Buscapé (out of domain): 64.94
Model Name: 'srl-en\_mbert-base', F1 CV PropBank.Br (in domain): 63.07, F1 Buscapé (out of domain): 58.56
Model Name: 'srl-enpt\_xlmr-base', F1 CV PropBank.Br (in domain): 76.50, F1 Buscapé (out of domain): 73.74
Model Name: 'srl-enpt\_xlmr-large', F1 CV PropBank.Br (in domain): 78.22, F1 Buscapé (out of domain): 74.55
Model Name: 'srl-enpt\_mbert-base', F1 CV PropBank.Br (in domain): 74.88, F1 Buscapé (out of domain): 69.19
Model Name: 'ud\_srl-pt\_bertimbau-large', F1 CV PropBank.Br (in domain): 77.53, F1 Buscapé (out of domain): 74.49
Model Name: 'ud\_srl-pt\_xlmr-large', F1 CV PropBank.Br (in domain): 77.69, F1 Buscapé (out of domain): 74.91
Model Name: 'ud\_srl-enpt\_xlmr-large', F1 CV PropBank.Br (in domain): 77.97, F1 Buscapé (out of domain): 75.05 ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #xlm-roberta #feature-extraction #xlm-roberta-large #semantic role labeling #finetuned #multilingual #pt #en #dataset-PropBank.Br #dataset-CoNLL-2012 #arxiv-2101.01213 #license-apache-2.0 #endpoints_compatible #region-us 
#### How to use


To use the transformers portion of this model:


To use the full SRL model (transformers portion + a decoding layer), refer to the project's github.