Instructions to use TomData/deberta-HA-FT-hateXplain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TomData/deberta-HA-FT-hateXplain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TomData/deberta-HA-FT-hateXplain")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TomData/deberta-HA-FT-hateXplain") model = AutoModelForSequenceClassification.from_pretrained("TomData/deberta-HA-FT-hateXplain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fbb62d933ec89481e8ed45de316641e73f365d23db24fc710e62a0b607948a6b
- Size of remote file:
- 738 MB
- SHA256:
- 105e01b4aad8b69dedff39bc179fcf6ba1590b10f5a5dd7b9e8c441abfd83cfc
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