Instructions to use Jeevesh8/std_0pnt2_bert_ft_cola-21 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-21 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-21")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-21") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/std_0pnt2_bert_ft_cola-21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jeevesh8/std_0pnt2_bert_ft_cola-21: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-21/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jeevesh8/std_0pnt2_bert_ft_cola-21/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jeevesh8/std_0pnt2_bert_ft_cola-21/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- 05b427bc5980320f071d5532d0eaeb5fe4f75a961cc565bdeb0498bf67a28cae
- Size of remote file:
- 438 MB
- SHA256:
- 4b136d5975848b5080628e9f4c8ce2f5c6045abb3e7cb64f668cb70d730f1af2
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