Instructions to use Jeevesh8/std_0pnt2_bert_ft_cola-12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/std_0pnt2_bert_ft_cola-12 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/std_0pnt2_bert_ft_cola-12")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-12") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-12") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dc3b438b4a4b763e9ad718ae13c49393edd101e0f4c6c55b734da80bedfe4fd2
- Size of remote file:
- 438 MB
- SHA256:
- 9fc1062170b2140b5dda526af7f9cb22688329178c11b0a4297a7727650183d4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.