Instructions to use fasterinnerlooper/codeBERTa-csharp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fasterinnerlooper/codeBERTa-csharp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="fasterinnerlooper/codeBERTa-csharp")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("fasterinnerlooper/codeBERTa-csharp") model = AutoModelForMaskedLM.from_pretrained("fasterinnerlooper/codeBERTa-csharp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 033617553297dd7cb626cc41c6b3094314136518c5e04f5e613626c29ac53782
- Size of remote file:
- 334 MB
- SHA256:
- 6e10d223fd4409c40fbeb91ed5ba1ea391edbf67038ba945db273a3e7868a3dd
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