Instructions to use Jeevesh8/bert_ft_cola-65 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_cola-65 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_cola-65")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_cola-65") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_cola-65") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83925ca8582731ffd967923277a948a1a91c3f4591c4a4e6d2a8b2964716dc47
- Size of remote file:
- 438 MB
- SHA256:
- e319d2a4423bd56912a1b2cbd3e502991b53b682159c0ea538ece52e412dbf44
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