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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("neulab/codebert-java") model = AutoModelForMaskedLM.from_pretrained("neulab/codebert-java") - Inference
- Notebooks
- Google Colab
- Kaggle
| This is a `microsoft/codebert-base-mlm` model, trained for 1,000,000 steps (with `batch_size=32`) on **Java** code from the `codeparrot/github-code-clean` dataset, on the masked-language-modeling task. | |
| It is intended to be used in CodeBERTScore: [https://github.com/neulab/code-bert-score](https://github.com/neulab/code-bert-score), but can be used for any other model or task. | |
| For more information, see: [https://github.com/neulab/code-bert-score](https://github.com/neulab/code-bert-score) |