Instructions to use tiya1012/hatespeech_hack with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiya1012/hatespeech_hack with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tiya1012/hatespeech_hack")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tiya1012/hatespeech_hack") model = AutoModelForSequenceClassification.from_pretrained("tiya1012/hatespeech_hack") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fdac2298b05af6275b1fdca938819021ea8d3831b7c650318dbbc8a97375b49b
- Size of remote file:
- 669 MB
- SHA256:
- fe894642cd4ba34a8dd90cd0216dd5983816b2ce9173a54f5e50ba5b7769bf10
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