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--- |
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language: |
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- en |
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- code |
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license: apache-2.0 |
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library_name: transformers.js |
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tags: |
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- code |
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- embeddings |
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- onnx |
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- transformers.js |
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- semantic-search |
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- code-search |
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pipeline_tag: feature-extraction |
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base_model: microsoft/unixcoder-base |
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--- |
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# UniXcoder ONNX for Code Search |
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**Converted by [VibeAtlas](https://vibeatlas.dev)** - AI Context Optimization for Developers |
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This is [Microsoft's UniXcoder](https://huggingface.co/microsoft/unixcoder-base) converted to ONNX format for use with **Transformers.js** in browser and Node.js environments. |
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## Why UniXcoder? |
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UniXcoder understands code **semantically**, not just as text: |
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- Trained on 6 programming languages (Python, Java, JavaScript, PHP, Ruby, Go) |
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- Understands AST structure and data flow |
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- 20-30% better code search accuracy vs generic embedding models |
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## Quick Start |
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### Transformers.js (Browser/Node.js) |
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```javascript |
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import { pipeline } from '@huggingface/transformers'; |
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const embedder = await pipeline( |
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'feature-extraction', |
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'sailesh27/unixcoder-base-onnx' |
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); |
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const code = `function authenticate(user) { |
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return user.isValid && user.hasPermission; |
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}`; |
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const embedding = await embedder(code, { |
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pooling: 'mean', |
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normalize: true |
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}); |
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console.log(embedding.dims); // [1, 768] |
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``` |
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### Semantic Code Search |
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```javascript |
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import { pipeline, cos_sim } from '@huggingface/transformers'; |
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const embedder = await pipeline('feature-extraction', 'sailesh27/unixcoder-base-onnx'); |
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// Index your code |
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const codeSnippets = [ |
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'function login(user, pass) { ... }', |
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'function formatDate(date) { ... }', |
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'function validateEmail(email) { ... }' |
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]; |
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const codeEmbeddings = await embedder(codeSnippets, { pooling: 'mean', normalize: true }); |
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// Search with natural language |
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const query = 'user authentication'; |
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const queryEmbedding = await embedder(query, { pooling: 'mean', normalize: true }); |
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// Find most similar |
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const similarities = codeEmbeddings.tolist().map((emb, i) => ({ |
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code: codeSnippets[i], |
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score: cos_sim(queryEmbedding.tolist()[0], emb) |
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})); |
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``` |
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## Technical Details |
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- **Architecture**: RoBERTa-based encoder |
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- **Hidden Size**: 768 |
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- **Max Sequence Length**: 512 tokens |
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- **Output Dimensions**: 768 |
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- **ONNX Opset**: 14 |
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## About VibeAtlas |
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**VibeAtlas** is the reliability infrastructure for AI coding: |
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- Reduce AI token costs by 40-60% |
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- Improve code search accuracy with semantic understanding |
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- Add governance guardrails to AI workflows |
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**Links**: |
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- [Website](https://vibeatlas.dev) |
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- [VS Code Extension](https://marketplace.visualstudio.com/items?itemName=vibeatlas.vibeatlas) |
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- [GitHub](https://github.com/vibeatlas) |
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## Citation |
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```bibtex |
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@misc{unixcoder-onnx-2025, |
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title={UniXcoder ONNX: Code Embeddings for JavaScript}, |
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author={VibeAtlas Team}, |
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year={2025}, |
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publisher={Hugging Face}, |
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url={https://huggingface.co/sailesh27/unixcoder-base-onnx} |
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} |
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``` |
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### Original UniXcoder Paper |
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```bibtex |
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@inproceedings{guo2022unixcoder, |
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title={UniXcoder: Unified Cross-Modal Pre-training for Code Representation}, |
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author={Guo, Daya and Lu, Shuai and Duan, Nan and Wang, Yanlin and Zhou, Ming and Yin, Jian}, |
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booktitle={ACL}, |
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year={2022} |
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} |
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``` |
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## License |
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Apache 2.0 (same as original UniXcoder) |
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