
# Toxic language detection

## Model description

A toxic language detection model trained on tweets. The base model is Roberta-large. For more information, 
including the **training data**, **limitations and bias**, please refer to the [paper](https://arxiv.org/pdf/2102.00086.pdf) and
Github [repo](https://github.com/XuhuiZhou/Toxic_Debias) for more details.

#### How to use
Note that LABEL_1 means toxic and LABEL_0 means non-toxic in the output.

```python
from transformers import pipeline
classifier = pipeline("text-classification",model='Xuhui/ToxDect-roberta-large', return_all_scores=True)
prediction = classifier("You are f**king stupid!", )
print(prediction)

"""
Output:
[[{'label': 'LABEL_0', 'score': 0.002632011892274022}, {'label': 'LABEL_1', 'score': 0.9973680377006531}]]
"""
```

## Training procedure
The random seed for this model is 22. For other details, please refer to the Github [repo](https://github.com/XuhuiZhou/Toxic_Debias) for more details.

### BibTeX entry and citation info

```bibtex
@inproceedings{zhou-etal-2020-debiasing,
  title = {Challenges in Automated Debiasing for Toxic Language Detection},
  author = {Zhou, Xuhui and Sap, Maarten and Swayamdipta, Swabha and Choi, Yejin and Smith, Noah A.},
  booktitle = {EACL},
  abbr = {EACL},
  html = {https://www.aclweb.org/anthology/2021.eacl-main.274.pdf},
  code = {https://github.com/XuhuiZhou/Toxic_Debias},
  year = {2021},
  bibtex_show = {true},
  selected = {true}
}
```
{"language": [], "tags": [], "datasets": [], "metrics": []}
transformers pytorch roberta text-classification arxiv:2102.00086 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #text-classification #arxiv-2102.00086 #autotrain_compatible #endpoints_compatible #region-us 


# Toxic language detection

## Model description

A toxic language detection model trained on tweets. The base model is Roberta-large. For more information, 
including the training data, limitations and bias, please refer to the paper and
Github repo for more details.

#### How to use
Note that LABEL_1 means toxic and LABEL_0 means non-toxic in the output.



## Training procedure
The random seed for this model is 22. For other details, please refer to the Github repo for more details.

### BibTeX entry and citation info


# Toxic language detection ## Model description

A toxic language detection model trained on tweets. The base model is Roberta-large. For more information, 
including the training data, limitations and bias, please refer to the paper and
Github repo for more details. #### How to use
Note that LABEL_1 means toxic and LABEL_0 means non-toxic in the output. ## Training procedure
The random seed for this model is 22. For other details, please refer to the Github repo for more details. ### BibTeX entry and citation info
TAGS
#transformers #pytorch #roberta #text-classification #arxiv-2102.00086 #autotrain_compatible #endpoints_compatible #region-us 
 # Toxic language detection ## Model description

A toxic language detection model trained on tweets. The base model is Roberta-large. For more information, 
including the training data, limitations and bias, please refer to the paper and
Github repo for more details. #### How to use
Note that LABEL_1 means toxic and LABEL_0 means non-toxic in the output. ## Training procedure
The random seed for this model is 22. For other details, please refer to the Github repo for more details. ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #roberta #text-classification #arxiv-2102.00086 #autotrain_compatible #endpoints_compatible #region-us 
# Toxic language detection## Model description

A toxic language detection model trained on tweets. The base model is Roberta-large. For more information, 
including the training data, limitations and bias, please refer to the paper and
Github repo for more details.#### How to use
Note that LABEL_1 means toxic and LABEL_0 means non-toxic in the output.## Training procedure
The random seed for this model is 22. For other details, please refer to the Github repo for more details.### BibTeX entry and citation info