Hinglish-MiniLM

This is a Hinglish (Hindi-English) sentence embedding model. It maps "Hinglish" queries (e.g., "Paise transfer karne hai") to their English semantic equivalents (e.g., "Money Transfer").

It is designed for Indian developers building Search Engines, Chatbots, or Recommendation Systems for India.

🚀 Quick Start

First, install the library:

pip install sentence-transformers

1. Basic Usage (The "Hello World")

This shows how the model converts text into numbers (vectors).

from sentence_transformers import SentenceTransformer

# Load the model
model = SentenceTransformer('anuragwagh0/hinglish-minilm')

# Encode sentences
sentences = ["Mujhe loan chahiye", "I want a loan"]
embeddings = model.encode(sentences)

print(embeddings.shape)
# Output: (2, 384) -> Two sentences, each is a vector of size 384

2. Real World Example: Building a Semantic Router

This is how you use the model to "route" user queries to the right function in your app.

from sentence_transformers import SentenceTransformer, util

model = SentenceTransformer('anuragwagh0/hinglish-minilm')

# 1. Define your App's Capabilities (The "Targets")
app_actions = [
    "Check Account Balance",
    "Transfer Money",
    "Call Customer Support"
]

# 2. Encode your actions (Do this once on startup)
action_vectors = model.encode(app_actions)

# 3. Simulate a User Query
user_query = "Bhai paise bhejne the"  # Hinglish Input
query_vector = model.encode(user_query)

# 4. Find the best match
scores = util.cos_sim(query_vector, action_vectors)[0]
best_match_idx = scores.argmax()
best_action = app_actions[best_match_idx]

print(f"User said: '{user_query}'")
print(f"Bot Action: {best_action}")
# Output: Bot Action: Transfer Money

🛠 Use Cases

1. Customer Support Chatbots: Understand "Order kab aayega?" -> "Track Order".
2. E-Commerce Search: Understand "Saste joote" -> "Cheap Shoes".
3. Content Recommendation: Match Hinglish comments to English video tags.

📊 Performance

• Base Model: all-MiniLM-L6-v2 (Lightweight, ~80MB)
• Language: Hindi + English (Code-Mixed)
• Dimensions: 384

👨‍💻 Training

Fine-tuned using sentence-transformers on a synthetic dataset of banking, tech support, and casual conversation pairs.