Instructions to use Jeevesh8/bert_ft_cola-61 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_cola-61 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_cola-61")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_cola-61") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_cola-61") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5962e71a0ee3d64a486dfe1a4b63cdae35e78a62076a1c2a45076170e6f5f72c
- Size of remote file:
- 438 MB
- SHA256:
- bb7ca516ccc9cd43c07cc48f4cb7f560980aac87e86013a3d0aace2359c75a31
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.