hindi-embedding-foundational-model

Hindi Sentence Embeddings Model

This is a custom state-of-the-art sentence embedding model trained specifically for Hindi text. It leverages an advanced transformer architecture with specialized pooling strategies to create high-quality semantic representations of Hindi sentences.

Features

• Specialized for Hindi language text
• Advanced transformer architecture with optimized attention mechanism
• Multiple pooling strategies for enhanced semantic representations
• Creates normalized vector representations for semantic similarity
• Supports semantic search and text similarity applications

Usage

Installation

pip install torch sentencepiece scikit-learn matplotlib
git lfs install 
git clone https://huggingface.co/convaiinnovations/hindi-embedding-foundational-model
cd hindi-embedding-foundational-model

Enhanced RAG System

This model now includes an enhanced RAG (Retrieval Augmented Generation) system that integrates Unsloth's optimized Llama-3.2-1B-Instruct model for question answering on top of Hindi document retrieval.

Setup and Installation

1. Install additional dependencies:

pip install unsloth transformers bitsandbytes accelerate langchain langchain-community faiss-cpu

1. Index your documents:

python hindi-rag-system.py --model_dir /path/to/your/model --tokenizer_dir /path/to/tokenizer --data_dir ./data --output_dir ./output --index

1. Run in QA mode with LLM:

python hindi-rag-system.py --model_dir /path/to/your/model --tokenizer_dir /path/to/tokenizer --output_dir ./output --interactive --qa

Basic Embedding Usage

from hindi_embeddings import HindiEmbedder
# Initialize the embedder
model = HindiEmbedder("path/to/hindi-embedding-foundational-model")
# Encode sentences to embeddings
sentences = [
    "मुझे हिंदी भाषा बहुत पसंद है।",
    "मैं हिंदी भाषा सीख रहा हूँ।"
]
embeddings = model.encode(sentences)
print(f"Embedding shape: {embeddings.shape}")
# Compute similarity between sentences
similarity = model.compute_similarity(sentences[0], sentences[1])
print(f"Similarity: {similarity:.4f}")
# Perform semantic search
query = "भारत की राजधानी"
documents = [
    "दिल्ली भारत की राजधानी है।",
    "मुंबई भारत का सबसे बड़ा शहर है।",
    "हिमालय पर्वत भारत के उत्तर में स्थित है।"
]
results = model.search(query, documents)
for i, result in enumerate(results):
    print(f"{i+1}. Score: {result['score']:.4f}")
    print(f"   Document: {result['document']}")
# Visualize embeddings
example_sentences = [
    "मुझे हिंदी में पढ़ना बहुत पसंद है।",
    "आज मौसम बहुत अच्छा है।",
    "भारत एक विशाल देश है।"
]
model.visualize_embeddings(example_sentences)

Model Details

This model uses an advanced transformer-based architecture with the following enhancements:

• Pre-layer normalization for stable training
• Specialized attention mechanism with relative positional encoding
• Multiple pooling strategies (weighted, mean, attention-based)
• L2-normalized vectors for cosine similarity Technical specifications:
• Embedding dimension: 768
• Hidden dimension: 768
• Layers: 12
• Attention heads: 12
• Vocabulary size: 50,000
• Context length: 128 tokens

Applications

• Semantic search and information retrieval
• Text clustering and categorization
• Recommendation systems
• Question answering
• Document similarity comparison
• Content-based filtering
• RAG systems for Hindi language content

License

This model is released under the MIT License.