Instructions to use AllenGeng/CodeBert14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AllenGeng/CodeBert14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="AllenGeng/CodeBert14")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("AllenGeng/CodeBert14") model = AutoModelForTokenClassification.from_pretrained("AllenGeng/CodeBert14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from AllenGeng/CodeBert14: direct link, hf CLI and curl.
- Browser
- Download file 496 MB
-
https://huggingface.co/AllenGeng/CodeBert14/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://AllenGeng/CodeBert14@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/AllenGeng/CodeBert14/resolve/refs%2Fpr%2F1/pytorch_model.bin
496 MB
- Xet hash:
- 138cb8506770eadee8d74ba79725a78fac23d085fd1ee50124932c5e391f5439
- Size of remote file:
- 496 MB
- SHA256:
- 7a24c6c806967690e3b3e7af901341f48798b9ffbf043cd7ed5716768f7d4dd6
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