Instructions to use nikchar/claim_detection_squeezebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nikchar/claim_detection_squeezebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nikchar/claim_detection_squeezebert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nikchar/claim_detection_squeezebert") model = AutoModelForSequenceClassification.from_pretrained("nikchar/claim_detection_squeezebert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e61b48bcacfc1690b919251651ba56adf8b080bbb42b0c86217f1c2d5704eef6
- Size of remote file:
- 204 MB
- SHA256:
- 3eddde75fd2d3d04c04fbc776d48a79eb8d3af57fb886b9b958908406a91d220
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