Instructions to use Moo/hs_detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Moo/hs_detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Moo/hs_detector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Moo/hs_detector") model = AutoModelForSequenceClassification.from_pretrained("Moo/hs_detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fcd53cfc3cf8dec3e073d27e95691e7a67b6f86fff4b9c928d39b858183b390d
- Size of remote file:
- 511 MB
- SHA256:
- a35c22d8ac662fe34d3179c4a89e24dce04915dd3ec295258a88b7ec42b172a7
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