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-1700/optimizer.pt from dzungpham/graphcodebert-code-classification: direct link, hf CLI and curl.
- Browser
- Download file 4.74 MB
-
https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-best/checkpoint-1700/optimizer.pt
- Command line
-
hf download hf://dzungpham/graphcodebert-code-classification@34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-best/checkpoint-1700/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dzungpham/graphcodebert-code-classification/resolve/34959f8150aa72974f031232b8d5f8b74b50e9fe/graphcodebert-best/checkpoint-1700/optimizer.pt
4.74 MB
- Xet hash:
- c2a6b4ab914595ad17b10cdddf7285ed8f49ebf24ea89edb8211c68f4f2f9e98
- Size of remote file:
- 4.74 MB
- SHA256:
- 6a851f6c3b1be06e302a82dee43a3086a19a82c085cdba9ac3ee2e49b5b4ea15
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.