Instructions to use raicrits/DistilFEVERit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raicrits/DistilFEVERit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raicrits/DistilFEVERit")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raicrits/DistilFEVERit") model = AutoModelForSequenceClassification.from_pretrained("raicrits/DistilFEVERit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0e4fe8585de458211faebda4978293e745365b3ae0f7811b72aaade5d11c1289
- Size of remote file:
- 541 MB
- SHA256:
- 15b8f743805cbcea5ab4854968b908e278f0de381c6b7c5105ec71b2f0770941
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