Feature Extraction
Transformers
Safetensors
code
roberta
code-search
semantic-search
graphcodebert
erlang
cpp
text-embeddings-inference
Instructions to use MatthewsO3/GraphCode-CErl-codesearch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatthewsO3/GraphCode-CErl-codesearch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MatthewsO3/GraphCode-CErl-codesearch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MatthewsO3/GraphCode-CErl-codesearch") model = AutoModel.from_pretrained("MatthewsO3/GraphCode-CErl-codesearch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from MatthewsO3/GraphCode-CErl-codesearch: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/MatthewsO3/GraphCode-CErl-codesearch/resolve/main/model.safetensors
- Command line
-
hf download hf://MatthewsO3/GraphCode-CErl-codesearch/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/MatthewsO3/GraphCode-CErl-codesearch/resolve/main/model.safetensors
499 MB
- Xet hash:
- 9dded836663d19228926d237722a0d0be71245f1c930b7fedc6ace3e4b7ffd40
- Size of remote file:
- 499 MB
- SHA256:
- dd9faba2e723ddbf1f57ffd4672487638f59cb2b0b1a46bcfca5fd8da0f1e22c
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