Intent-classifier-v1

Intent classifier v1

blue-machines/Intent-classifier-v1 is a sentence-level 7-class intent classifier.

• Base: microsoft/Multilingual-MiniLM-L12-H384
• Deploy artifact: model.onnx — dynamic INT8 including embeddings (~113 MB)
• Input: a single bare utterance (no [user] / [assistant] tags)
• Max length: 96

Intent labels

Files

Metrics (approximate)

Hard-fail fine-tunes after this checkpoint degraded accuracy — do not use hardft weights.

Inference

import json
from pathlib import Path

import numpy as np
import onnxruntime as ort
from transformers import AutoTokenizer

MODEL_DIR = Path(".")  # or snapshot_download("blue-machines/Intent-classifier-v1")
MAX_LEN = 96
LABELS = [
    "provide_info", "affirm", "deny", "correction",
    "question", "clarify_request", "unclear",
]

tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR)
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.unk_token

session = ort.InferenceSession(
    str(MODEL_DIR / "model.onnx"),
    providers=["CPUExecutionProvider"],
)

def predict_intent(sentence: str) -> dict:
    enc = tokenizer(
        sentence,
        return_tensors="np",
        truncation=True,
        max_length=MAX_LEN,
        padding=True,
    )
    feed = {
        "input_ids": enc["input_ids"].astype(np.int64),
        "attention_mask": enc["attention_mask"].astype(np.int64),
    }
    logits = session.run(None, feed)[0][0]
    x = logits.astype(np.float64)
    x = x - x.max()
    probs = np.exp(x); probs = probs / probs.sum()
    pred_id = int(probs.argmax())
    return {
        "intent": LABELS[pred_id],
        "confidence": float(probs.max()),
        "probs": {LABELS[i]: float(probs[i]) for i in range(7)},
    }

print(predict_intent("Haan, ye sahi hai. Proceed karo."))

Notes

• Intent-only (no LID / LSD).
• Trained on expanded sentence-direct data (synthetic + Muthoot), with Muthoot-upweighted continue-FT.
• Prefer this INT8 deploy over the earlier MatMul-only INT8 export (~389 MB).