Instructions to use Jeevesh8/bert_ft_cola-35 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_cola-35 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_cola-35")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_cola-35") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_cola-35") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad573e99ed0f7404d2abb45bb9434a6deb9b57bbaf63d8cb1b04a458024dff74
- Size of remote file:
- 438 MB
- SHA256:
- 67c136034e0489735c2258b5d77eb0c7534e73aba967ebedf4a8f7c4e32a7d03
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.