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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30df69c0550b964b1f439fb9266995f7771c76bb948f92201282da6689e56c2a
- Size of remote file:
- 44.7 MB
- SHA256:
- d0e7a84733de8d716c906443103d2eebe6fdcfa99549930d68229417d2d0ef6d
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