Search Intent Classifier
Binary text classifier that decides whether a student's search query needs an AI-generated answer (trigger_ai) or can be satisfied with search results alone (skip_ai).
Model Details
| Property | Value |
|---|---|
| Base model | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 |
| Architecture | BERT (12 layers, 384 hidden size) |
| Task | Binary text classification |
| Quantization | Dynamic INT8 (weights QInt8, activations QUInt8) |
| Format | ONNX Runtime |
| Languages | English, German (multilingual) |
Labels
| Label | ID | Meaning |
|---|---|---|
skip_ai |
0 | Query can be answered with search results (e.g. "Lecture 4", "Exercise Sheet 2") |
trigger_ai |
1 | Query needs an LLM-generated explanation (e.g. "explain recursion", "wie funktioniert hashing") |
Usage
import numpy as np
import onnxruntime as ort
import joblib
from transformers import AutoTokenizer
# Load model components
tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
session = ort.InferenceSession("model_quantized.onnx")
head = joblib.load("model_head.joblib")
def predict(query: str) -> str:
inputs = tokenizer(query, return_tensors="np", truncation=True, max_length=512, padding=True)
outputs = session.run(None, {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"token_type_ids": inputs["token_type_ids"],
})
embedding = outputs[0].mean(axis=1)
label_id = head.predict(embedding)[0]
return "trigger_ai" if label_id == 1 else "skip_ai"
print(predict("explain recursion")) # trigger_ai
print(predict("how does quicksort work step by step")) # trigger_ai
print(predict("was ist ein Deadlock")) # trigger_ai
print(predict("Lecture 4")) # skip_ai
print(predict("Exercise Sheet 2")) # skip_ai
print(predict("Vorlesung 7 Folien")) # skip_ai
Training Data
Trained on a synthesized dataset of student search queries generated by Claude (Anthropic) in English and German.