Instructions to use Jeevesh8/bert_ft_cola-51 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_cola-51 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_cola-51")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_cola-51") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_cola-51") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ec50e0b4ed805d758b6f4ec88b3f7986670c0fe25155ff9fb2e22a430dbf761e
- Size of remote file:
- 438 MB
- SHA256:
- 6d6f2735dd14a062bd822cd0a276de352769245c70c677a0611f4684fe68d36c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.