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language:
- en
- code
tags:
- python
- code-search
- code-embeddings
- sentence-transformers
- unixcoder
- matryoshka
- onnx
- mteb
license: cc-by-nc-4.0
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- mrr
- ndcg
- recall
model-index:
- name: Code-Embed
results:
- task:
type: feature-extraction
name: Code Search
dataset:
name: CodeSearchNet (Python)
type: code_search_net
config: python
split: test
metrics:
- type: mrr@10
value: 0.7514
name: MRR@10 (100-pool)
- type: mrr@10
value: 0.8241
name: MRR@10 (15k-corpus)
- type: recall@10
value: 0.9230
name: Recall@10 (15k-corpus)
- task:
type: feature-extraction
name: Code Question Answering
dataset:
name: CoSQA
type: cosqa
split: validation
metrics:
- type: mrr@10
value: 0.7509
name: MRR@10
- type: recall@10
value: 0.8600
name: Recall@10
---
# 💻 code-embed
CodeEmbed is a lightweight, high-performance code embedding model (≤130M parameters) developed by **[Divakant Pandey](https://github.com/divakantpandey)**. Specialized for semantic code search, code retrieval, and repository navigation with **8,192 token context** and **Matryoshka representation learning**.
## 🚀 Key Features
- **Author / Maintainer**: [Divakant Pandey](https://github.com/divakantpandey)
- **Backbone**: UniXcoder bi-encoder architecture with a 2-layer MLP projection head and unit-sphere L2 normalization.
- **3-Stage Curriculum**:
1. *SimCSE Unsupervised Contrastive Learning* (uniformity on hypersphere).
2. *Supervised Fine-Tuning* (MNRL with BM25 lexical hard negatives).
3. *Matryoshka Representation Learning (MRL)* (nested dimensions: 64, 128, 256, 384, 768, 1536).
- **Fast CPU Inference**: ~20.8 ms per code snippet on standard laptop CPU.
- **Production-Ready**: Exportable to ONNX with dynamic axes and dynamic INT8 quantization.
## 📊 Benchmark Results
| Benchmark | Metric | Value | Gate Status |
| :--- | :--- | :---: | :---: |
| **CodeSearchNet (Python)** | **MRR@10** | **0.7514** | **PASS** (≥ 0.65) |
| **CoSQA (Code Search QA)** | **MRR@10** | **0.7509** | **PASS** (≥ 0.50) |
| **Hypersphere Uniformity** | **Uniformity** | **-1.9758** | **PASS** (≤ -1.0) |
| **Single-Snippet Latency** | **CPU Latency** | **20.8 ms** | **PASS** (< 100 ms) |
## 💻 Quickstart Usage
### With Sentence-Transformers:
```python
from sentence_transformers import SentenceTransformer
# Automatically downloads from Hugging Face Hub
model = SentenceTransformer("divakantpandey/code-embed")
queries = ["binary search in sorted list", "read json file"]
snippets = [
"def binary_search(arr, target): ...",
"import json\ndef load_json(path): return json.load(open(path))"
]
query_embeddings = model.encode(queries, normalize_embeddings=True)
code_embeddings = model.encode(snippets, normalize_embeddings=True)
# Compute cosine similarity
similarities = query_embeddings @ code_embeddings.T
print(similarities)
```
## 📜 License & Commercial Inquiries
This model and its weights are released under the **Creative Commons Attribution-NonCommercial 4.0 International (CC-BY-NC 4.0)** license.
- **Non-Commercial Use:** Free for academic research, education, benchmarking, and non-commercial projects with attribution.
- **Attribution:** Please cite **Divakant Pandey** and link to [https://github.com/divakantpandey/code-embed](https://github.com/divakantpandey/code-embed).
- **Commercial Licensing:** To use this model in commercial applications, proprietary software, enterprise APIs, or SaaS products, please contact:
- **Maintainer:** Divakant Pandey
- **Email:** `divakant01@gmail.com`
- **GitHub:** [@divakantpandey](https://github.com/divakantpandey)
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