20
stars
17
commits
10
repos using this model
2
linked in READMEs
Feb 24, 2025
updated
endpoints_compatible
pytorch
roberta
text-classification
text-embeddings-inference
tf
transformers
README
cardiffnlp/twitter-roberta-base-hate-latest
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2022-154m for binary hate-speech classification. A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model. More details in the reference paper.
| Dataset | Accuracy | Macro-F1 | Weighted-F1 |
|---|---|---|---|
| hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter | 0.5831 | 0.5646 | 0.548 |
| ucberkeley-dlab/measuring-hate-speech | 0.9273 | 0.9193 | 0.928 |
| Detecting East Asian Prejudice on Social Media | 0.9231 | 0.6623 | 0.9428 |
| Call me sexist, but | 0.9686 | 0.9203 | 0.9696 |
| Predicting the Type and Target of Offensive Posts in Social Media | 0.9164 | 0.6847 | 0.9098 |
| HateXplain | 0.8653 | 0.845 | 0.8662 |
| Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior | 0.7801 | 0.7446 | 0.7614 |
| Multilingual and Multi-Aspect Hate Speech Analysis | 0.9944 | 0.4986 | 0.9972 |
| Hate speech and offensive content identification in indo-european languages | 0.8779 | 0.6904 | 0.8706 |
| Are You a Racist or Am I Seeing Things? | 0.921 | 0.8935 | 0.9216 |
| Automated Hate Speech Detection | 0.9423 | 0.9249 | 0.9429 |
| Hate Towards the Political Opponent | 0.8783 | 0.6595 | 0.8788 |
| Hateful Symbols or Hateful People? | 0.8187 | 0.7833 | 0.8323 |
| Overall | 0.8766 | 0.7531 | 0.8745 |
Usage
Install tweetnlp via pip.
pip install tweetnlp
Load the model in python.
import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-hate-latest")
model.predict('I love everybody :)')
>> {'label': 'NOT-HATE'}
Reference paper - Model based on:
@inproceedings{antypas-camacho-collados-2023-robust,
title = "Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation",
author = "Antypas, Dimosthenis and
Camacho-Collados, Jose",
booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.woah-1.25",
pages = "231--242"
}
Similar repos
Used in code
20
stars
17
commits
10
repos using this model
2
linked in READMEs
Feb 24, 2025
updated
endpoints_compatible
pytorch
roberta
text-classification
text-embeddings-inference
tf
transformers
README
cardiffnlp/twitter-roberta-base-hate-latest
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2022-154m for binary hate-speech classification. A combination of 13 different hate-speech datasets in the English language were used to fine-tune the model. More details in the reference paper.
| Dataset | Accuracy | Macro-F1 | Weighted-F1 |
|---|---|---|---|
| hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter | 0.5831 | 0.5646 | 0.548 |
| ucberkeley-dlab/measuring-hate-speech | 0.9273 | 0.9193 | 0.928 |
| Detecting East Asian Prejudice on Social Media | 0.9231 | 0.6623 | 0.9428 |
| Call me sexist, but | 0.9686 | 0.9203 | 0.9696 |
| Predicting the Type and Target of Offensive Posts in Social Media | 0.9164 | 0.6847 | 0.9098 |
| HateXplain | 0.8653 | 0.845 | 0.8662 |
| Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior | 0.7801 | 0.7446 | 0.7614 |
| Multilingual and Multi-Aspect Hate Speech Analysis | 0.9944 | 0.4986 | 0.9972 |
| Hate speech and offensive content identification in indo-european languages | 0.8779 | 0.6904 | 0.8706 |
| Are You a Racist or Am I Seeing Things? | 0.921 | 0.8935 | 0.9216 |
| Automated Hate Speech Detection | 0.9423 | 0.9249 | 0.9429 |
| Hate Towards the Political Opponent | 0.8783 | 0.6595 | 0.8788 |
| Hateful Symbols or Hateful People? | 0.8187 | 0.7833 | 0.8323 |
| Overall | 0.8766 | 0.7531 | 0.8745 |
Usage
Install tweetnlp via pip.
pip install tweetnlp
Load the model in python.
import tweetnlp
model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-hate-latest")
model.predict('I love everybody :)')
>> {'label': 'NOT-HATE'}
Reference paper - Model based on:
@inproceedings{antypas-camacho-collados-2023-robust,
title = "Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation",
author = "Antypas, Dimosthenis and
Camacho-Collados, Jose",
booktitle = "The 7th Workshop on Online Abuse and Harms (WOAH)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.woah-1.25",
pages = "231--242"
}