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bert-hateXplain
tum-nlp/bert-hateXplain
bert-hateXplain is a text classification model from tum-nlp. Use it when you need a label for a piece of text. It is set up for transformers.
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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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Jun 15, 2023
- Updated
- Oct 8, 2023
- SHA
- 274ddee6
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Datasets
Model card
From the Hugging Face model README
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")
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Languages
- en
- Created
- Jun 15, 2023
- Updated
- Oct 8, 2023