Instructions to use XQ112/OpCodeBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use XQ112/OpCodeBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="XQ112/OpCodeBERT")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("XQ112/OpCodeBERT") model = AutoModel.from_pretrained("XQ112/OpCodeBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 406ace2729ee44ecc5597218d52546d228c923bde30ba3b255e33c20256f3213
- Size of remote file:
- 504 MB
- SHA256:
- 6996928e4af6b0dd84f2ec6fc698c99f51617cac40e3b88694a730cc968eb65f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.