Instructions to use Jeevesh8/std_0pnt2_bert_ft_cola-1 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-1 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-1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-1") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd21b8ad81546adf9fc25cbc8d5b481a8bcb28bd652a51a174964d3366ca1d53
- Size of remote file:
- 438 MB
- SHA256:
- ae5e7bf4181fe1c1cfd26883421161cee3dec8b50c84a50047a6b783e1564ada
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.