natural-language-processing-2
Hate Speech Detection
3260 papers • 126 benchmarks • 313 datasets
Hate speech detection is the task of detecting if communication such as text, audio, and so on contains hatred and or encourages violence towards a person or a group of people. This is usually based on prejudice against 'protected characteristics' such as their ethnicity, gender, sexual orientation, religion, age et al. Some example benchmarks are ETHOS and HateXplain. Models can be evaluated with metrics like the F-score or F-measure.
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Benchmarks
These leaderboards are used to track progress in hate-speech-detection-4
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Dataset
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Ethos Binary
Ethos Binary
HateXplain
HateXplain
Ethos MultiLabel
Ethos MultiLabel
Libraries
iUse these libraries to find hate-speech-detection-4 models and implementations
5 papers 90
Datasets
Subtasks
Most implemented papers
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Thomas Wolf, Victor Sanh, Julien Chaumond, Lysandre Debut•Tue Oct 01 2019
This work proposes a method to pre-train a smaller general-purpose language representation model, called DistilBERT, which can be fine-tuned with good performances on a wide range of tasks like its larger counterparts, and introduces a triple loss combining language modeling, distillation and cosine-distance losses.
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