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--- |
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datasets: |
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- hatexplain |
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language: |
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- en |
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pipeline_tag: text-classification |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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--- |
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# BERT for hate speech classification |
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The model is based on BERT and used for classifying a text as **toxic** and **non-toxic**. It achieved an **F1** score of **0.81** and an **Accuracy** of **0.77**. |
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The model was fine-tuned on the HateXplain dataset found here: https://huggingface.co/datasets/hatexplain |
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## How to use |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline |
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# Load model and tokenizer |
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tokenizer = AutoTokenizer.from_pretrained('tum-nlp/bert-hateXplain') |
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model = AutoModelForSequenceClassification.from_pretrained('tum-nlp/bert-hateXplain') |
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# Create the pipeline for classification |
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hate_classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) |
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# Predict |
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hate_classifier("Girls like attention and they get desperate") |
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``` |
