Instructions to use Jeevesh8/bert_ft_cola-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_cola-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_cola-5")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_cola-5") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_cola-5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a589ee6fbd7fbe4c5d9e3668dc8c76cf68fddd1d7ddeca8d3fbbae0da4afecbb
- Size of remote file:
- 438 MB
- SHA256:
- 586af0187047dd87b1c8d7a55d01137280d2194315279ffffd0c24a437787d5a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.