Instructions to use buelfhood/GraphCodeBERT_BCB_ChaFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use buelfhood/GraphCodeBERT_BCB_ChaFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="buelfhood/GraphCodeBERT_BCB_ChaFT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("buelfhood/GraphCodeBERT_BCB_ChaFT") model = AutoModelForSequenceClassification.from_pretrained("buelfhood/GraphCodeBERT_BCB_ChaFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from buelfhood/GraphCodeBERT_BCB_ChaFT: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/buelfhood/GraphCodeBERT_BCB_ChaFT/resolve/main/model.safetensors
- Command line
-
hf download hf://buelfhood/GraphCodeBERT_BCB_ChaFT/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/buelfhood/GraphCodeBERT_BCB_ChaFT/resolve/main/model.safetensors
499 MB
- Xet hash:
- b861f18e967a628d52c925fdbaa0383a759c8a20b1e96d8f1d065ac1c0c31db2
- Size of remote file:
- 499 MB
- SHA256:
- 2c5e0ff17439da52729447fe99d52b2050c2ba54ae601543e6d77ef4c6fb2091
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.