Instructions to use Jeevesh8/std_0pnt2_bert_ft_cola-67 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/std_0pnt2_bert_ft_cola-67 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/std_0pnt2_bert_ft_cola-67")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-67") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-67") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 618879de39434653946a4fe597b6109925b37df7576db7c9b83f25691ae65209
- Size of remote file:
- 438 MB
- SHA256:
- 257595b680b1e7ceedd5756c6568ae3ad1b71228e6b8ce28f57d027ad844eef3
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