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model-index:
  - name: twitter-roberta-base-hate-latest
    results: []
pipeline_tag: text-classification

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.

Following metrics are achieved

Dataset Accuracy Macro-F1 Weighted-F1
hatEval, SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter 0.5848 0.5657 0.5514
ucberkeley-dlab/measuring-hate-speech 0.8706 0.8531 0.8701
Detecting East Asian Prejudice on Social Media 0.9276 0.8935 0.9273
Call me sexist, but 0.9033 0.6288 0.8852
Predicting the Type and Target of Offensive Posts in Social Media 0.9075 0.5984 0.8935
HateXplain 0.9594 0.8024 0.9600
Large Scale Crowdsourcing and Characterization of Twitter Abusive BehaviorLarge Scale Crowdsourcing and Characterization of Twitter Abusive Behavior 0.6817 0.5939 0.6233
Twitter Sentiment Analysis 0.9808 0.9258 0.9807
Overview of the HASOC track at FIRE 2019:Hate Speech and Offensive Content Identification in Indo-European Languages 0.8665 0.5562 0.8343
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter 0.9465 0.8557 0.9440
Automated Hate Speech Detection and the Problem of Offensive Language 0.9116 0.8797 0.9100
Hateful Symbols or Hateful People? Predictive Features for Hate Speech Detection on Twitter 0.8378 0.8338 0.8385
Multilingual and Multi-Aspect Hate Speech Analysis 0.9655 0.4912 0.9824
Overall 0.8827 0.8383 0.8842

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'}