Instructions to use dzungpham/graphcodebert-code-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dzungpham/graphcodebert-code-classification with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dzungpham/graphcodebert-code-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download graphcodebert-best/checkpoint-1200/training_args.bin from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-best/checkpoint-1200/training_args.bin
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-best/checkpoint-1200/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-best/checkpoint-1200/training_args.bin
5.91 kB
- Xet hash:
- bd3c26b6a1efffc262e1e345f8360bce4bbfaf82ad88bb8cbf0f0e1ad3e14602
- Size of remote file:
- 5.91 kB
- SHA256:
- 2442ad24c09a6289958ec89eeef865760137153dc7f53d04b13f7b0926f8f307
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.