Instructions to use Kanit/distilbert-base-uncased-hateXplain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kanit/distilbert-base-uncased-hateXplain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Kanit/distilbert-base-uncased-hateXplain")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Kanit/distilbert-base-uncased-hateXplain") model = AutoModelForSequenceClassification.from_pretrained("Kanit/distilbert-base-uncased-hateXplain", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b1fc71991621e31dfef2892c8c4d4fc06326b627cd278f1d0d2d09557480073
- Size of remote file:
- 268 MB
- SHA256:
- af01945a359053d97b032e17f549fa9eca8579553b5dcb97f3edaa6fa553a1f4
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