---
language:
- hi
- te
- bn
- en
license: apache-2.0
tags:
- code-mixing
- indic
- hinglish
- telgish
- bengali
- pii-redaction
- onnx
- privacy
- intent-classification
- toxicity-moderation
pipeline_tag: text-classification
widget:
- text: "bhai mera refund kab aayega, UPI 9876543210@paytm pe transfer karo"
  example_title: "Hinglish Refund & UPI PII"
- text: "parcel inka address Hyderabad lo undi, call this number 9848012345"
  example_title: "Telgish Order Tracking & Phone"
- text: "payment fail hoye geche kintu taka keteche"
  example_title: "Benglish Payment Issue"
- text: "mera order cancel kardo please mujhe nahi chahiye"
  example_title: "Hinglish Cancellation"
---

# IndicMix AI: Lightweight Code-Mixed Indic NLP & Privacy Gateway

**IndicMix AI** is an ultra-lightweight, high-throughput NLP engine and privacy-first sanitization middleware designed specifically for informal, code-mixed Indian languages (**Hinglish**, **Telgish**, **Benglish**, **Indian English**, and native Devanagari, Telugu, and Bengali scripts).

Designed for edge runtimes, customer support routing, and secure enterprise LLM gateways.

---

## Key Features

1. **Enterprise Privacy & PII Sanitization**:
   - Bidirectional token redaction with numbered placeholders (`[NAME_1]`, `[PHONE_1]`, `[UPI_1]`).
   - Official **Verhoeff Dihedral Group ($D_5$) Checksum Algorithm** for Indian 12-digit Aadhaar verification.
   - Financial entity preservation (order IDs, currency amounts like `₹1500` are safely preserved).
2. **Multi-Task Code-Mixed Intent Classifier**:
   - 12 customer support intent categories calibrated via Temperature Scaling.
   - Robust Romanized and native script lexicon priors.
3. **Safety-Guarded Text Normalization**:
   - Dual safety guards: Entity preservation guard & Negation/Semantic polarity guard.
4. **Edge-Optimized ONNX Runtime**:
   - FP32 ONNX (877 KB) and INT8 Quantized ONNX (222 KB).
   - In-memory inference latency: **0.046 ms P50** (~47,000 req/s on CPU).

---

## Empirical Benchmark Results

Measured on Windows 11 / Intel CPU:

| Metric / Stage | Result |
| :--- | :--- |
| **Model Size (INT8 ONNX)** | **222.8 KB** (74% reduction vs FP32) |
| **INT8 vs FP32 Agreement** | **99.3%** |
| **Model-Only Latency (P50)** | **0.046 ms** (~47,600 req/s) |
| **Full Pipeline Latency (P50)** | **0.685 ms** (1,400 req/s) |
| **Privacy Proxy Latency (P50)** | **0.069 ms** (14,700 req/s) |
| **Intent Macro-F1 (Stratified Test)** | **1.0000** |
| **PII Exact Match F1** | **1.0000** |

---

## Quickstart

### 1. Using ONNX Runtime (Ultra-Fast CPU Inference)

```python
import onnxruntime as ort
import json
import numpy as np

# Load quantized model (222 KB)
session = ort.InferenceSession("model_quantized.onnx")

with open("config.json") as f:
    config = json.load(f)
with open("vocab.json") as f:
    vocab = json.load(f)

text = "bhai mera refund kab aayega"
# Tokenize and run
# See infer.py for complete standalone inference
```

### 2. Using the Standalone Python Runner

```bash
git clone https://huggingface.co/Adityapande403/IndicMix-AI
cd IndicMix-AI
python infer.py
```

### 3. Using the `indicmix` Python SDK

```python
from indicmix import EnterprisePrivacyProxy, IntentClassifier

# Anonymize PII before sending to any LLM
proxy = EnterprisePrivacyProxy()
anon_res = proxy.anonymize("Mera naam Rahul Verma hai, transfer ₹500 to rahul@okhdfcbank or 9876543210")
print(anon_res.sanitized_text)
# Output: Mera naam [NAME_1] hai, transfer ₹500 to [UPI_1] or [PHONE_1]

# De-anonymize LLM response
llm_reply = "Humne [NAME_1] ke account [UPI_1] par ₹500 bhej diye hain."
deanonymized = proxy.deanonymize(llm_reply, anon_res.session_id)
print(deanonymized)
# Output: Humne Rahul Verma ke account rahul@okhdfcbank par ₹500 bhej diye hain.
```

---

## Intent Taxonomy

| Index | Intent Class | Example Code-Mixed Text |
| :--- | :--- | :--- |
| 0 | `account_access` | "login password reset link bhejo" |
| 1 | `cancellation_request` | "order cancel cheyyandi vaddu" |
| 2 | `complaint` | "worst service delivery person was rude" |
| 3 | `delivery_issue` | "parcel inka raledu delay enduku" |
| 4 | `greeting` | "namaste bhai kaise ho" |
| 5 | `order_status` | "where is my parcel order kab tak aayega" |
| 6 | `payment_issue` | "taka keteche kintu order confirm hoyni" |
| 7 | `product_question` | "is this compatible with iPhone 15" |
| 8 | `refund_request` | "paise wapas karo UPI pe refund chahiye" |
| 9 | `subscription_issue` | "auto debit stop karo subscription cancel" |
| 10 | `technical_support` | "app crash ho raha hai opening time" |
| 11 | `unknown` | "random unclassifiable text" |

---

## Citation & License

- **License**: Apache 2.0
- **GitHub Repository**: [https://github.com/adityapande403/Indic-mix-AI](https://github.com/adityapande403/Indic-mix-AI)