# cardiffnlp/twitter-xlm-roberta-base-hate-spanish

This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base) using the [`HaterNet`](https://zenodo.org/record/2592149) dataset and the Spanish subset of 
[`SemEval-2019 Task 5`](https://aclanthology.org/S19-2007/).

## Following metrics are achieved 

* `on the test split of SemEval-2019 Task 5`

  - F1 (weighted):  0.7866 
  - F1 (macro):  0.7935 
  - Accuracy:  0.7937

* on custom test split of `Haternet`

  - F1 (weighted): 0.7815  
  - F1 (macro):  0.6981 
  - Accuracy: 0.7933 

* on `Haternet` & `SemEval-2019 Task 5`
  - F1 (weighted): 0.7908
  - F1 (macro):  0.7657 
  - Accuracy: 0.7936



### Usage
Install tweetnlp via pip.
```shell
pip install tweetnlp
```
Load the model in python.
```python
import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-xlm-roberta-base-hate-spanish")
model.predict('Ismael es egocentrico porque se vuelve loca si le dicen que tiene el pelo bonito😂😂😂😂 eso se define con otro objetivo #FirstDates251')
>> {'label': 'NOT-HATE'}

```



### Datasets
@inproceedings{basile-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
    author = "Basile, Valerio  and
      Bosco, Cristina  and
      Fersini, Elisabetta  and
      Nozza, Debora  and
      Patti, Viviana  and
      Rangel Pardo, Francisco Manuel  and
      Rosso, Paolo  and
      Sanguinetti, Manuela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S19-2007",
    doi = "10.18653/v1/S19-2007",
    pages = "54--63",
}

@article{quijano2019haternet,
  title={HaterNet a system for detecting and analyzing hate speech in Twitter (Version 1.0)[Data set]},
  author={Quijano-Sanchez, Lara and Kohatsu, Juan Carlos Pereira and Liberatore, Federico and Camacho-Collados, Miguel},
  journal={Zenodo},
  year={2019}
}

{}
transformers pytorch tf xlm-roberta text-classification autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 

# cardiffnlp/twitter-xlm-roberta-base-hate-spanish

This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base using the 'HaterNet' dataset and the Spanish subset of 
'SemEval-2019 Task 5'.

## Following metrics are achieved 

* 'on the test split of SemEval-2019 Task 5'

  - F1 (weighted):  0.7866 
  - F1 (macro):  0.7935 
  - Accuracy:  0.7937

* on custom test split of 'Haternet'

  - F1 (weighted): 0.7815  
  - F1 (macro):  0.6981 
  - Accuracy: 0.7933 

* on 'Haternet' & 'SemEval-2019 Task 5'
  - F1 (weighted): 0.7908
  - F1 (macro):  0.7657 
  - Accuracy: 0.7936



### Usage
Install tweetnlp via pip.

Load the model in python.




### Datasets
@inproceedings{basile-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
    author = "Basile, Valerio  and
      Bosco, Cristina  and
      Fersini, Elisabetta  and
      Nozza, Debora  and
      Patti, Viviana  and
      Rangel Pardo, Francisco Manuel  and
      Rosso, Paolo  and
      Sanguinetti, Manuela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "URL
    doi = "10.18653/v1/S19-2007",
    pages = "54--63",
}

@article{quijano2019haternet,
  title={HaterNet a system for detecting and analyzing hate speech in Twitter (Version 1.0)[Data set]},
  author={Quijano-Sanchez, Lara and Kohatsu, Juan Carlos Pereira and Liberatore, Federico and Camacho-Collados, Miguel},
  journal={Zenodo},
  year={2019}
}

# cardiffnlp/twitter-xlm-roberta-base-hate-spanish

This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base using the 'HaterNet' dataset and the Spanish subset of 
'SemEval-2019 Task 5'. ## Following metrics are achieved 

* 'on the test split of SemEval-2019 Task 5'

  - F1 (weighted):  0.7866 
  - F1 (macro):  0.7935 
  - Accuracy:  0.7937

* on custom test split of 'Haternet'

  - F1 (weighted): 0.7815  
  - F1 (macro):  0.6981 
  - Accuracy: 0.7933 

* on 'Haternet' & 'SemEval-2019 Task 5'
  - F1 (weighted): 0.7908
  - F1 (macro):  0.7657 
  - Accuracy: 0.7936 ### Usage
Install tweetnlp via pip.

Load the model in python. ### Datasets
@inproceedings{basile-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
    author = "Basile, Valerio  and
      Bosco, Cristina  and
      Fersini, Elisabetta  and
      Nozza, Debora  and
      Patti, Viviana  and
      Rangel Pardo, Francisco Manuel  and
      Rosso, Paolo  and
      Sanguinetti, Manuela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "URL
    doi = "10.18653/v1/S19-2007",
    pages = "54--63",
}

@article{quijano2019haternet,
  title={HaterNet a system for detecting and analyzing hate speech in Twitter (Version 1.0)[Data set]},
  author={Quijano-Sanchez, Lara and Kohatsu, Juan Carlos Pereira and Liberatore, Federico and Camacho-Collados, Miguel},
  journal={Zenodo},
  year={2019}
}
TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 
 # cardiffnlp/twitter-xlm-roberta-base-hate-spanish

This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base using the 'HaterNet' dataset and the Spanish subset of 
'SemEval-2019 Task 5'. ## Following metrics are achieved 

* 'on the test split of SemEval-2019 Task 5'

  - F1 (weighted):  0.7866 
  - F1 (macro):  0.7935 
  - Accuracy:  0.7937

* on custom test split of 'Haternet'

  - F1 (weighted): 0.7815  
  - F1 (macro):  0.6981 
  - Accuracy: 0.7933 

* on 'Haternet' & 'SemEval-2019 Task 5'
  - F1 (weighted): 0.7908
  - F1 (macro):  0.7657 
  - Accuracy: 0.7936 ### Usage
Install tweetnlp via pip.

Load the model in python. ### Datasets
@inproceedings{basile-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
    author = "Basile, Valerio  and
      Bosco, Cristina  and
      Fersini, Elisabetta  and
      Nozza, Debora  and
      Patti, Viviana  and
      Rangel Pardo, Francisco Manuel  and
      Rosso, Paolo  and
      Sanguinetti, Manuela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "URL
    doi = "10.18653/v1/S19-2007",
    pages = "54--63",
}

@article{quijano2019haternet,
  title={HaterNet a system for detecting and analyzing hate speech in Twitter (Version 1.0)[Data set]},
  author={Quijano-Sanchez, Lara and Kohatsu, Juan Carlos Pereira and Liberatore, Federico and Camacho-Collados, Miguel},
  journal={Zenodo},
  year={2019}
}
TAGS
#transformers #pytorch #tf #xlm-roberta #text-classification #autotrain_compatible #endpoints_compatible #region-us 
# cardiffnlp/twitter-xlm-roberta-base-hate-spanish

This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base using the 'HaterNet' dataset and the Spanish subset of 
'SemEval-2019 Task 5'.## Following metrics are achieved 

* 'on the test split of SemEval-2019 Task 5'

  - F1 (weighted):  0.7866 
  - F1 (macro):  0.7935 
  - Accuracy:  0.7937

* on custom test split of 'Haternet'

  - F1 (weighted): 0.7815  
  - F1 (macro):  0.6981 
  - Accuracy: 0.7933 

* on 'Haternet' & 'SemEval-2019 Task 5'
  - F1 (weighted): 0.7908
  - F1 (macro):  0.7657 
  - Accuracy: 0.7936### Usage
Install tweetnlp via pip.

Load the model in python.### Datasets
@inproceedings{basile-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in {T}witter",
    author = "Basile, Valerio  and
      Bosco, Cristina  and
      Fersini, Elisabetta  and
      Nozza, Debora  and
      Patti, Viviana  and
      Rangel Pardo, Francisco Manuel  and
      Rosso, Paolo  and
      Sanguinetti, Manuela",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "URL
    doi = "10.18653/v1/S19-2007",
    pages = "54--63",
}

@article{quijano2019haternet,
  title={HaterNet a system for detecting and analyzing hate speech in Twitter (Version 1.0)[Data set]},
  author={Quijano-Sanchez, Lara and Kohatsu, Juan Carlos Pereira and Liberatore, Federico and Camacho-Collados, Miguel},
  journal={Zenodo},
  year={2019}
}