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- name: twitter-roberta-base-hate-latest
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results: []
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pipeline_tag: text-classification
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---
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# cardiffnlp/twitter-roberta-base-hate-latest
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/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.
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## Following metrics are achieved
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### Usage
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model.predict('I love everybody :)')
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>> {'label': 'NOT-HATE'}
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```
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- name: twitter-roberta-base-hate-latest
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results: []
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pipeline_tag: text-classification
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language:
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- en
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---
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# cardiffnlp/twitter-roberta-base-hate-latest
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This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-2022-154m](https://huggingface.co/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.
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## Following metrics are achieved
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| **Dataset** | **Accuracy** | **Macro-F1** | **Weighted-F1** |
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|:----------|-----------:|-----------:|--------------:|
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| hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter | 0.5831 | 0.5646 | 0.548 |
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| ucberkeley-dlab/measuring-hate-speech | 0.9273 | 0.9193 | 0.928 |
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| Detecting East Asian Prejudice on Social Media | 0.9231 | 0.6623 | 0.9428 |
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| Call me sexist, but | 0.9686 | 0.9203 | 0.9696 |
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| Predicting the Type and Target of Offensive Posts in Social Media | 0.9164 | 0.6847 | 0.9098 |
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| HateXplain | 0.8653 | 0.845 | 0.8662 |
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| Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior | 0.7801 | 0.7446 | 0.7614 |
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| Multilingual and Multi-Aspect Hate Speech Analysis | 0.9944 | 0.4986 | 0.9972 |
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| Hate speech and offensive content identification in indo-european languages | 0.8779 | 0.6904 | 0.8706 |
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| Are You a Racist or Am I Seeing Things? | 0.921 | 0.8935 | 0.9216 |
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| Automated Hate Speech Detection | 0.9423 | 0.9249 | 0.9429 |
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| Hate Towards the Political Opponent | 0.8783 | 0.6595 | 0.8788 |
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| Hateful Symbols or Hateful People? | 0.8187 | 0.7833 | 0.8323 |
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| **Overall** | **0.8766** | **0.7531** | **0.8745** |
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### Usage
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model.predict('I love everybody :)')
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>> {'label': 'NOT-HATE'}
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```
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### Model based on:
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```
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@misc{antypas2023robust,
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title={Robust Hate Speech Detection in Social Media: A Cross-Dataset Empirical Evaluation},
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author={Dimosthenis Antypas and Jose Camacho-Collados},
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year={2023},
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eprint={2307.01680},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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