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
Commit History
9 days ago
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Summarise benchmark wins at the top of the results
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Add Codex MCP setup
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Link the GitHub repo and add MCP setup for coding agents
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Add the codeintel adapter spec
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Frame the secondary results as percentage gains
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Tighten the opening paragraph
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Fold the headline result into the opening paragraph
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Use the official name CodeIntel-150M throughout the model card
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Lead the model card with the issue-localization result
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Release CodeIntel-150M
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initial commit
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