Instructions to use sujalgawas/code-bert-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sujalgawas/code-bert-python with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sujalgawas/code-bert-python")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("sujalgawas/code-bert-python") model = AutoModel.from_pretrained("sujalgawas/code-bert-python", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f458371c6c1c43fedc32d5d8acc3337e3e10d007b9123ac5d69aa5a91e1e9fb
- Size of remote file:
- 507 MB
- SHA256:
- 116b9685867148a298d1abeed977191c522fe56061fcbfaed3cdba15ec201220
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.