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")# pip install -U transformers accelerate # 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
Download model_v1.pt from sujalgawas/code-bert-python: direct link, hf CLI and curl.
- Browser
- Download file 500 MB
-
https://huggingface.co/sujalgawas/code-bert-python/resolve/9566c106be1a6eafcc90cc7bdccefc5c522abf6f/model_v1.pt
- Command line
-
hf download hf://sujalgawas/code-bert-python@9566c106be1a6eafcc90cc7bdccefc5c522abf6f/model_v1.pt
-
curl -L -o model_v1.pt https://huggingface.co/sujalgawas/code-bert-python/resolve/9566c106be1a6eafcc90cc7bdccefc5c522abf6f/model_v1.pt
500 MB
- Xet hash:
- 728366be1d7515987583a04b20f8a9fdccab05845598fce6b6b2a57bda48d744
- Size of remote file:
- 500 MB
- SHA256:
- eb7de7c5eae3d940b72e7e2d55e3e6534f361d4e4342b4c6c03c97dd2cf7c43c
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