Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
code
modernbert
feature-extraction
code-search
code-retrieval
bug-localization
Eval Results (legacy)
text-embeddings-inference
Instructions to use vedevpatel/CodeIntel-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vedevpatel/CodeIntel-150M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vedevpatel/CodeIntel-150M") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Fold the headline result into the opening paragraph
Browse files
README.md
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# CodeIntel-150M
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(it leads its size class on CoIR StackOverflow QA and CodeSearchNet Python). It is a fine-tune of
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[granite-embedding-english-r2](https://huggingface.co/ibm-granite/granite-embedding-english-r2) (ModernBERT,
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Apache-2.0). It runs locally, including as a 325 MB INT8 ONNX model, and needs no remote code.
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# CodeIntel-150M
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CodeIntel-150M is a 149M-parameter embedding model for **finding the code a bug report is about**. Give it an issue
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written by a user and it retrieves the functions and classes in a Python or TypeScript repository that a fix will
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touch. When the issue only describes a symptom and names no code, it does this **about 55% better than any other open
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model its size**. It is also ahead when the issue does name code (+27%) and at matching tests to the code they cover
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(+23%).
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It also handles call site -> definition navigation and code search (it leads its size class on CoIR StackOverflow QA
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and CodeSearchNet Python). It is a fine-tune of
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[granite-embedding-english-r2](https://huggingface.co/ibm-granite/granite-embedding-english-r2) (ModernBERT,
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Apache-2.0). It runs locally, including as a 325 MB INT8 ONNX model, and needs no remote code.
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