Instructions to use dilanjt/java-javascript_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dilanjt/java-javascript_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="dilanjt/java-javascript_model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("dilanjt/java-javascript_model") model = AutoModelForMaskedLM.from_pretrained("dilanjt/java-javascript_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8cbb0e7d97d9c1a593bca6bd48211cb8308063e6cd6faf58882a71ea4c999676
- Size of remote file:
- 499 MB
- SHA256:
- f488203bd8b0ece4300750be167aff7823b87f5107fa3082be41de7a22477068
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.