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 unixcoder_model_v1.pt from sujalgawas/code-bert-python: direct link, hf CLI and curl.
- Browser
- Download file 507 MB
-
https://huggingface.co/sujalgawas/code-bert-python/resolve/68700b5afe95a747b6148e08af19595e67d11f34/unixcoder_model_v1.pt
- Command line
-
hf download hf://sujalgawas/code-bert-python@68700b5afe95a747b6148e08af19595e67d11f34/unixcoder_model_v1.pt
-
curl -L -o unixcoder_model_v1.pt https://huggingface.co/sujalgawas/code-bert-python/resolve/68700b5afe95a747b6148e08af19595e67d11f34/unixcoder_model_v1.pt
507 MB
- Xet hash:
- 0f458371c6c1c43fedc32d5d8acc3337e3e10d007b9123ac5d69aa5a91e1e9fb
- Size of remote file:
- 507 MB
- SHA256:
- 116b9685867148a298d1abeed977191c522fe56061fcbfaed3cdba15ec201220
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