Sexism and Hate Speech Mitigation
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# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("tum-nlp/bert-hateXplain")
model = AutoModelForSequenceClassification.from_pretrained("tum-nlp/bert-hateXplain")Quick Links
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
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")
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tum-nlp/bert-hateXplain")