bge-small-code-search-v1

A BGE-small-en-v1.5 model fine-tuned on CodeSearchNet (Python) for semantic code search.

🔍 What It Does

Maps natural language queries and code snippets into the same 384-dimensional vector space. Search your codebase by describing what a function does.

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Matthieufromparis/bge-small-code-search-v1")

query = "parse JSON config file and return a dictionary"
code_snippets = [...]  # your codebase
query_emb = model.encode(query)
code_embs = model.encode(code_snippets)
similarities = model.similarity(query_emb, code_embs)

📊 Performance

Metric	Base (BGE-small)	Fine-Tuned	Improvement
NDCG@10	0.9761	0.9849	+0.9%
Accuracy@1	0.948	0.960	+1.3%
MRR@10	0.975	0.978	+0.3%

Evaluated on 500 held-out Python code-comment pairs from CodeSearchNet.

🏗️ Training

Base Model: BAAI/bge-small-en-v1.5 (33M params, 384 dims)
Dataset: CodeSearchNet — Python subset, 6,000 pairs
Loss: MultipleNegativesRankingLoss
Epochs: 3 | Batch Size: 4 | LR: 2e-5
Hardware: Apple M4 (MPS), ~33 min

🚀 Quick Start

pip install sentence-transformers

from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Matthieufromparis/bge-small-code-search-v1")
query_embedding = model.encode("function that sorts a list of dictionaries by a key")
code_embedding = model.encode("def sort_dicts_by_key(dicts, key): return sorted(dicts, key=lambda x: x.get(key, ''))")
similarity = model.similarity(query_embedding, code_embedding)
print(f"Similarity: {similarity.item():.4f}")

📦 Use Cases

Semantic Code Search — Find functions by describing what they do
Code Documentation Lookup — Match docs to relevant code
Code Deduplication — Find similar implementations across repos
RAG for Coding Assistants — Retrieve relevant code for LLM context

🎯 Intended Use

Designed for asymmetric search — queries are natural language, documents are code.

⚠️ Limitations

Trained on Python only — may not generalize to other languages
384 dimensions — trades quality for speed vs larger models
Training data from CodeSearchNet (2019 vintage)

📚 Resources

📘 Fine-Tune Your Own Code Embeddings — Complete Guide by Matthieu.AI
📄 BGE Paper (C-Pack)
📄 CodeSearchNet Paper
🏆 MTEB Leaderboard

Author: Matthieu.AI (Matthieufromparis) — License: Apache 2.0

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🔍 What It Does

Maps natural language queries and code snippets into the same 384-dimensional vector space. Search your codebase by describing what a function does.

from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matthieufromparis/bge-small-code-search-v1") query = "parse JSON config file and return a dictionary" code_snippets = [...] # your codebase query_emb = model.encode(query) code_embs = model.encode(code_snippets) similarities = model.similarity(query_emb, code_embs)

Metric

Base (BGE-small)

Fine-Tuned

Improvement

NDCG@10

0.9761

0.9849

+0.9%

Accuracy@1

0.948

0.960

+1.3%

MRR@10

0.975

0.978

+0.3%

🚀 Quick Start

pip install sentence-transformers

from sentence_transformers import SentenceTransformer model = SentenceTransformer("Matthieufromparis/bge-small-code-search-v1") query_embedding = model.encode("function that sorts a list of dictionaries by a key") code_embedding = model.encode("def sort_dicts_by_key(dicts, key): return sorted(dicts, key=lambda x: x.get(key, ''))") similarity = model.similarity(query_embedding, code_embedding) print(f"Similarity: {similarity.item():.4f}")