Instructions to use Jeevesh8/std_0pnt2_bert_ft_cola-36 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-36 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-36")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-36") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-36") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 748ae12f7eeb4ed54731d9f9e07291287585c824c090650ddb21ee90ba8c5eea
- Size of remote file:
- 438 MB
- SHA256:
- bd3cafed5d73b1a09588144591f03c261c16abc48cb61f40655b92408c24ba43
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.