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")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("MatthewsO3/GraphCode-CErl-codesearch") model = AutoModel.from_pretrained("MatthewsO3/GraphCode-CErl-codesearch") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9dded836663d19228926d237722a0d0be71245f1c930b7fedc6ace3e4b7ffd40
- Size of remote file:
- 499 MB
- SHA256:
- dd9faba2e723ddbf1f57ffd4672487638f59cb2b0b1a46bcfca5fd8da0f1e22c
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