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README.md

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+---
+language:
+- en
+- hi
+license: mit
+library_name: transformers
+tags:
+- distilbert
+- onnx
+- int8
+- query-classification
+- generic-semantic
+- multilingual
+- text-classification
+datasets:
+- addyo07/query-classification-dataset
+metrics:
+- accuracy
+pipeline_tag: text-classification
+model-index:
+- name: distilbert-query-classifier
+  results:
+  - task:
+      type: text-classification
+      name: Generic vs Semantic Classification
+    dataset:
+      name: query-classification-dataset
+      type: addyo07/query-classification-dataset
+      split: test
+    metrics:
+    - type: accuracy
+      value: 0.9839
+      name: Accuracy
+    - type: precision
+      value: 0.9844
+      name: Precision
+    - type: recall
+      value: 0.9834
+      name: Recall
+    - type: f1
+      value: 0.9839
+      name: F1
+widget:
+- text: "my name is John"
+- text: "hello"
+- text: "मेरा नाम रवि है"
+- text: "नमस्ते"
+- text: "I love spicy food"
+- text: "stop"
+---
+
+# Query Sieve Classifier
+
+**DistilBERT multilingual** fine-tuned to classify user queries as **GENERIC** or **SEMANTIC** — filtering chit-chat from durable knowledge worth storing.
+
+## Model Description
+
+- **Architecture**: `distilbert-base-multilingual-cased` (134M params)
+- **Quantization**: INT8 dynamic (ONNX Runtime)
+- **Input**: Short text queries in English or Hindi (≤10 words recommended for model path; longer sentences bypass to SEMANTIC)
+- **Output**: Binary — GENERIC (0) or SEMANTIC (1)
+- **Inference**: ONNX Runtime CPU (Intel/AMD), single-thread P99 = **16.87ms**
+
+## Intended Use
+
+This model is designed as a **memory relevance gate** in voice AI pipelines. Before storing a user's utterance in long-term memory (episodic + semantic), run it through this classifier:
+
+- **SEMANTIC** → contains facts, preferences, name, location, relationships → store in memory
+- **GENERIC** → greeting, command, chit-chat, filler → skip memory, pass directly to LLM
+
+Sentences longer than 10 words bypass the model entirely and are treated as SEMANTIC, since they almost always contain durable information.
+
+## Performance
+
+| Split | Accuracy |
+|-------|----------|
+| Test (15% holdout) | **98.39%** |
+
+### Latency
+
+| Mode | P50 | P99 |
+|------|-----|-----|
+| Multi-thread CPU | 8.39 ms | 11.81 ms |
+| Single-thread CPU (intra_op_threads=1) | 14.81 ms | 16.87 ms |
+
+## Usage
+
+### Python
+
+```python
+from transformers import AutoTokenizer
+import onnxruntime as ort
+
+tokenizer = AutoTokenizer.from_pretrained("addyo07/distilbert-query-classifier")
+session = ort.InferenceSession("model_quantized.onnx")
+
+def classify(text: str) -> str:
+    inputs = tokenizer(text, return_tensors="np", max_length=64, truncation=True, padding="max_length")
+    logits = session.run(None, {
+        "input_ids": inputs["input_ids"].astype(np.int64),
+        "attention_mask": inputs["attention_mask"].astype(np.int64),
+    })[0]
+    return "SEMANTIC" if logits[0][1] > logits[0][0] else "GENERIC"
+```
+
+### Rust
+
+```toml
+[dependencies]
+query-sieve = { git = "https://github.com/your-org/query-sieve" }
+```
+
+```rust
+use query_sieve::GenericSemanticClassifier;
+
+let classifier = GenericSemanticClassifier::load(
+    "models/model_quantized.onnx",
+    "models/tokenizer.json",
+)?;
+let result = classifier.classify("my name is John")?;
+```
+
+## Training Data
+
+The dataset contains **12,044 synthetic examples** generated by `llama3.1:8b`:
+
+| Category | English | Hindi |
+|----------|---------|-------|
+| GENERIC | 3,003 | 3,019 |
+| SEMANTIC | 3,017 | 3,005 |
+
+The SEMANTIC category is balanced to contain ~40% short standalone statements (3-7 words) to prevent the model from learning "semantic = long sentence."
+
+## Files
+
+| File | Size | Description |
+|------|------|-------------|
+| `model_quantized.onnx` | 130 MB | INT8 quantized ONNX model |
+| `tokenizer.json` | 2.8 MB | HuggingFace tokenizer |
+| `config.json` | 0.7 KB | Model configuration |
+
+## License
+
+MIT