Instructions to use Kanit/bert-hateXplain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kanit/bert-hateXplain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kanit/bert-hateXplain")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kanit/bert-hateXplain") model = AutoModelForSequenceClassification.from_pretrained("Kanit/bert-hateXplain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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datasets:
- hatexplain
language:
- en
pipeline_tag: text-classification
---
# BERT for hate speech classification
The model is based on BERT and used for classifying a text as **toxic** and **non-toxic**.
The model was fine-tuned on the HateXplain dataset found here: https://huggingface.co/datasets/hatexplain
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