| --- |
| license: mit |
| language: |
| - en |
| - zh |
| tags: |
| - onnx |
| - text-classification |
| - intent-routing |
| - code-intelligence |
| library_name: onnxruntime |
| pipeline_tag: text-classification |
| --- |
| |
| # Intent Router (ONNX int8) |
|
|
| A 7-class intent classifier for code query routing. Classifies natural language queries (English and Chinese) into structured intents for code intelligence tools. |
|
|
| ## Intents |
|
|
| | Label | Description | |
| |-------|-------------| |
| | `locate_symbol` | Find symbol definitions | |
| | `find_references` | Trace reverse references / impact | |
| | `trace_dependencies` | Trace forward dependencies / call chains | |
| | `semantic_search` | Semantic search over code and docs | |
| | `browse_structure` | Browse package / module structure | |
| | `cross_layer_trace` | Map between code and business docs | |
| | `ambiguous` | Query cannot be classified | |
|
|
| ## Files |
|
|
| | File | Required | Description | |
| |------|----------|-------------| |
| | `onnx/model.onnx` | Yes | ONNX model graph | |
| | `onnx/model.onnx_data` | Yes | Model weights (int8 quantized) | |
| | `model_head.json` | Yes | Classification head (weights + bias) | |
| | `tokenizer.json` | Yes | Tokenizer | |
| | `tokenizer_config.json` | Yes | Tokenizer configuration | |
| | `labels.json` | Yes | Intent label list | |
| | `config.json` | Yes | Model configuration | |
|
|
| ## Inference |
|
|
| Requires [ONNX Runtime](https://onnxruntime.ai/). The model takes tokenized text input and outputs sentence embeddings. The classification head (`model_head.json`) maps embeddings to intent logits. |
|
|
| ``` |
| input text → tokenizer → ONNX model → embedding → classification head → intent + confidence |
| ``` |
|
|
| ## Benchmark |
|
|
| Evaluated on a held-out test set of 221 bilingual (Chinese + English) code queries. |
|
|
| | Metric | Value | |
| |--------|-------| |
| | Overall accuracy | 96.8% (214/221) | |
| | Inference latency (CPU, ONNX Runtime) | ~3ms p50 | |
|
|
| Per-intent performance: |
|
|
| | Intent | Precision | Recall | F1 | |
| |--------|-----------|--------|----| |
| | locate_symbol | 98.4% | 96.8% | 0.976 | |
| | find_references | 95.1% | 97.5% | 0.963 | |
| | trace_dependencies | 90.2% | 95.1% | 0.926 | |
| | semantic_search | 100.0% | 91.5% | 0.956 | |
| | browse_structure | 91.7% | 100.0% | 0.957 | |
| | cross_layer_trace | 100.0% | 100.0% | 1.000 | |
| | ambiguous | 100.0% | 100.0% | 1.000 | |
| |
| Training set: 284 samples. Test set: 221 samples. |
| |
| ## Quantization |
| |
| int8 (dynamic quantization). Total size ~559MB. |
| |
| ## Related Project |
| |
| This model is fine-tuned for [C4A (Context For AI)](https://github.com/context4ai/c4a), a knowledge modeling service that indexes code repositories and business documents for developer teams and AI agents. |
| |
| ## License |
| |
| MIT |
| |
