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--- |
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model-index: |
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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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| **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 | 0.5848 | 0.5657 | 0.5514 | |
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| ucberkeley-dlab/measuring-hate-speech | 0.8706 | 0.8531 | 0.8701 | |
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| Detecting East Asian Prejudice on Social Media | 0.9276 | 0.8935 | 0.9273 | |
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| Call me sexist, but | 0.9033 | 0.6288 | 0.8852 | |
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| Predicting the Type and Target of Offensive Posts in Social Media | 0.9075 | 0.5984 | 0.8935 | |
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| HateXplain | 0.9594 | 0.8024 | 0.9600 | |
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| Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior | 0.6817 | 0.5939 | 0.6233 | |
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| Twitter Sentiment Analysis | 0.9808 | 0.9258 | 0.9807 | |
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| Overview of the HASOC track at FIRE 2019:Hate Speech and Offensive Content Identification in Indo-European Languages | 0.8665 | 0.5562 | 0.8343 | |
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| Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter | 0.9465 | 0.8557 | 0.9440 | |
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| Automated Hate Speech Detection and the Problem of Offensive Language | 0.9116 | 0.8797 | 0.9100 | |
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| Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter | 0.8378 | 0.8338 | 0.8385 | |
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| Multilingual and Multi-Aspect Hate Speech Analysis | 0.9655 | 0.4912 | 0.9824 | |
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| **Overall** | **0.8827** | **0.8383** | **0.8842** | |
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### Usage |
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Install tweetnlp via pip. |
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```shell |
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pip install tweetnlp |
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``` |
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Load the model in python. |
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```python |
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import tweetnlp |
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model = tweetnlp.Classifier("cardiffnlp/twitter-roberta-base-hate-latest") |
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model.predict('I love everybody :)') |
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>> {'label': 'NOT-HATE'} |
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``` |
