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
Summarise benchmark wins at the top of the results
Browse files
README.md
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## Results
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All numbers come from held-out test data that no training or model-selection step used. Each model was scored once,
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with the same protocol. The comparison models are open models of similar size, evaluated zero-shot.
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## Results
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CodeIntel-150M is the best open model of its size on **7 of 15 benchmarks**, including all three issue-localization
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benchmarks, test-to-implementation retrieval, TypeScript call-site navigation, CoIR StackOverflow QA and
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CodeSearchNet Python. The full per-model table is in
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[docs/results/benchmarks.md](https://github.com/vedevpatel/codeintel-150m/blob/main/docs/results/benchmarks.md).
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All numbers come from held-out test data that no training or model-selection step used. Each model was scored once,
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with the same protocol. The comparison models are open models of similar size, evaluated zero-shot.
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