Instructions to use neulab/codebert-java with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use neulab/codebert-java with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="neulab/codebert-java")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("neulab/codebert-java") model = AutoModelForMaskedLM.from_pretrained("neulab/codebert-java", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from neulab/codebert-java: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/neulab/codebert-java/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://neulab/codebert-java/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/neulab/codebert-java/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 40379776f5d6d191453d7d029ae2b2e4b2d52622c3f418fdfc36ca4b42c614dc
- Size of remote file:
- 499 MB
- SHA256:
- 222d32f569601c144a746bc2b4781c868158b7c8f298ab1ef07e232bf881e421
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