| --- |
| 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) |
|
|
