Instructions to use neulab/codebert-cpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neulab/codebert-cpp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="neulab/codebert-cpp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("neulab/codebert-cpp") model = AutoModelForMaskedLM.from_pretrained("neulab/codebert-cpp", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from neulab/codebert-cpp: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/neulab/codebert-cpp/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://neulab/codebert-cpp/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/neulab/codebert-cpp/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- b779614298dbd0e9c1cd556973d2a3b5b53d8897c0d2e29f353521c40a762289
- Size of remote file:
- 499 MB
- SHA256:
- aa47ec1e6d3c2da77a45c1ff34d0f8a3a0076cadc95528eccb6d3ec3aaf1e559
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