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
| 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 | |