| --- |
| datasets: |
| - hatexplain |
| language: |
| - en |
| pipeline_tag: text-classification |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| --- |
| # BERT for hate speech classification |
| 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**. |
|
|
| The model was fine-tuned on the HateXplain dataset found here: https://huggingface.co/datasets/hatexplain |
|
|
| ## How to use |
|
|
| ```python |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline |
| |
| # Load model and tokenizer |
| tokenizer = AutoTokenizer.from_pretrained('tum-nlp/bert-hateXplain') |
| model = AutoModelForSequenceClassification.from_pretrained('tum-nlp/bert-hateXplain') |
| |
| # Create the pipeline for classification |
| hate_classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) |
| |
| # Predict |
| hate_classifier("I like you. I love you") |
| |
| ``` |
