Instructions to use Jeevesh8/bert_ft_cola-76 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_cola-76 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_cola-76")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_cola-76") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_cola-76") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1556b7d3a408f785d88cdfbbf1cf65b7a0cba434988085049454c9e12f70e603
- Size of remote file:
- 438 MB
- SHA256:
- be7767d13c04ab19ea2da4a961093df95b292ef09b2b0efc6c61fd0f6b6c0614
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.