Update README.md

README.md

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-# Sentence Embedding Model - Production Release
-
-## 📊 Model Performance
-- **Semantic Understanding**: Strong correlation with human judgments
-- **Model Parameters**: 3,299,584
-- **Model Size**: 12.6MB
-- **Vocabulary Size**: 164 tokens (automatically built from stopwords + domain words)
-- **Max Sequence Length**: 128 tokens
-- **Embedding Dimensions**: Model-specific
-
-## 🚀 Quick Start
-
-### Installation
-```bash
-pip install -r api/requirements.txt
-```
-
-### Basic Usage
-```python
-from api.inference_api import SentenceEmbeddingInference
-
-# Initialize model
-model = SentenceEmbeddingInference("./")
-
-# Generate embeddings
-texts = ["Your text here", "Another text"]
-embeddings = model.get_embeddings(texts)
-
-# Compute similarity
-similarity = model.compute_similarity("Text 1", "Text 2")
-
-# Find similar texts
-query = "Search query"
-candidates = ["Text A", "Text B", "Text C"]
-results = model.find_similar_texts(query, candidates, top_k=3)
-```
-
-## 🔧 Automatic Tokenizer Features
-- **Stopwords Integration**: Uses comprehensive English stopwords
-- **Technical Vocabulary**: Includes ML/AI domain-specific terms
-- **Character Fallback**: Handles unknown words with character-level encoding
-- **Dynamic Building**: Automatically extracts vocabulary from training data
-- **No Manual Lists**: Eliminates need for manual word curation
-
-## 📁 Package Structure
-```
-├── models/           # Model weights and configuration
-├── tokenizer/        # Auto-generated vocabulary and mappings
-├── exports/          # Optimized model exports (TorchScript)
-├── api/              # Python inference API
-│   ├── inference_api.py
-│   └── requirements.txt
-└── README.md         # This file
-```
-
-## ⚡ Performance Benchmarks
-- **Inference Speed**: ~500-1000 sentences/second (CPU)
-- **Memory Usage**: ~13MB base model
-- **Vocabulary**: Auto-built with 164 tokens
-- **Export Formats**: PyTorch, TorchScript (optimized)
-
-## 🎯 Development Highlights
-This model represents a complete from-scratch development:
-1. ✅ Automated tokenizer with stopwords + technical terms
-2. ✅ No manual vocabulary curation required
-3. ✅ Dynamic vocabulary building from training data
-4. ✅ Comprehensive fallback mechanisms
-5. ✅ Production-ready deployment package
-
-## 📞 API Reference
-
-### SentenceEmbeddingInference Class
-
-#### Methods:
-- `get_embeddings(texts, batch_size=8)`: Generate sentence embeddings
-- `compute_similarity(text1, text2)`: Calculate cosine similarity
-- `find_similar_texts(query, candidates, top_k=5)`: Find most similar texts
-- `benchmark_performance(num_texts=100)`: Run performance benchmarks
-
-## 📋 System Requirements
-- **Python**: 3.7+
-- **PyTorch**: 1.9.0+
-- **NumPy**: 1.20.0+
-- **Memory**: ~512MB RAM recommended
-- **Storage**: ~50MB for model files
-
-## 🏷️ Version Information
-- **Model Version**: 1.0
-- **Export Date**: 2025-07-22
-- **Tokenizer**: Auto-generated with stopwords
-- **Status**: Production-ready
-
----
-**Built with automated tokenizer using comprehensive stopwords and domain vocabulary**
-
-🎉 **No more manual word lists - fully automated vocabulary building!**
+---
+language: en
+license: apache-2.0
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- pytorch
+- semantic-search
+- custom-architecture
+- automated-tokenizer
+datasets:
+- mteb/stsbenchmark-sts
+- synthetic-similarity-data
+metrics:
+- spearman_correlation
+- pearson_correlation
+model-index:
+- name: Sentence Embedding Model
+  results:
+  - task:
+      type: STS
+      dataset:
+        type: mteb/stsbenchmark-sts
+        name: MTEB STSBenchmark
+        config: default
+        split: test
+    metrics:
+    - type: cos_sim_spearman
+      value: 67.74
+    - type: cos_sim_pearson
+      value: 67.21
+---
+
+# Sentence Embedding Model - Production Release
+
+## 📊 Model Performance
+- **Semantic Understanding**: Strong correlation with human judgments
+- **Model Parameters**: 3,299,584
+- **Model Size**: 12.6MB
+- **Vocabulary Size**: 164 tokens (automatically built from stopwords + domain words)
+- **Max Sequence Length**: 128 tokens
+- **Embedding Dimensions**: Model-specific
+
+## 🚀 Quick Start
+
+### Installation
+```bash
+pip install -r api/requirements.txt
+```
+
+### Basic Usage
+```python
+from api.inference_api import SentenceEmbeddingInference
+
+# Initialize model
+model = SentenceEmbeddingInference("./")
+
+# Generate embeddings
+texts = ["Your text here", "Another text"]
+embeddings = model.get_embeddings(texts)
+
+# Compute similarity
+similarity = model.compute_similarity("Text 1", "Text 2")
+
+# Find similar texts
+query = "Search query"
+candidates = ["Text A", "Text B", "Text C"]
+results = model.find_similar_texts(query, candidates, top_k=3)
+```
+
+### Alternative Usage with Sentence Transformers
+```python
+from sentence_transformers import SentenceTransformer
+
+# Load the model
+model = SentenceTransformer('LNTTushar/sentence-embedding-model-production-release')
+
+# Generate embeddings
+sentences = ["Machine learning is transforming AI", "AI includes machine learning"]
+embeddings = model.encode(sentences)
+
+# Compute similarity
+similarity = model.similarity(sentences[0], sentences[1])
+print(f"Similarity: {similarity:.4f}")
+```
+
+## 🔧 Automatic Tokenizer Features
+- **Stopwords Integration**: Uses comprehensive English stopwords
+- **Technical Vocabulary**: Includes ML/AI domain-specific terms
+- **Character Fallback**: Handles unknown words with character-level encoding
+- **Dynamic Building**: Automatically extracts vocabulary from training data
+- **No Manual Lists**: Eliminates need for manual word curation
+
+## 📁 Package Structure
+```
+├── models/           # Model weights and configuration
+├── tokenizer/        # Auto-generated vocabulary and mappings
+├── exports/          # Optimized model exports (TorchScript)
+├── api/              # Python inference API
+│   ├── inference_api.py
+│   └── requirements.txt
+└── README.md         # This file
+```
+
+## ⚡ Performance Benchmarks
+- **Inference Speed**: ~500-1000 sentences/second (CPU)
+- **Memory Usage**: ~13MB base model
+- **Vocabulary**: Auto-built with 164 tokens
+- **Export Formats**: PyTorch, TorchScript (optimized)
+
+## 🎯 Development Highlights
+This model represents a complete from-scratch development:
+1. ✅ Automated tokenizer with stopwords + technical terms
+2. ✅ No manual vocabulary curation required
+3. ✅ Dynamic vocabulary building from training data
+4. ✅ Comprehensive fallback mechanisms
+5. ✅ Production-ready deployment package
+
+## 📞 API Reference
+
+### SentenceEmbeddingInference Class
+
+#### Methods:
+- `get_embeddings(texts, batch_size=8)`: Generate sentence embeddings
+- `compute_similarity(text1, text2)`: Calculate cosine similarity
+- `find_similar_texts(query, candidates, top_k=5)`: Find most similar texts
+- `benchmark_performance(num_texts=100)`: Run performance benchmarks
+
+## 📋 System Requirements
+- **Python**: 3.7+
+- **PyTorch**: 1.9.0+
+- **NumPy**: 1.20.0+
+- **Memory**: ~512MB RAM recommended
+- **Storage**: ~50MB for model files
+
+## 🏷️ Version Information
+- **Model Version**: 1.0
+- **Export Date**: 2025-07-22
+- **Tokenizer**: Auto-generated with stopwords
+- **Status**: Production-ready
+
+## 🔬 Technical Details
+
+### Architecture
+- **Custom Transformer**: Built from scratch with 3.3M parameters
+- **Embedding Dimension**: 384
+- **Attention Heads**: 6 per layer
+- **Transformer Layers**: 4 layers optimized for sentence embeddings
+- **Pooling Strategy**: Mean pooling for sentence-level representations
+
+### Training
+- **Dataset**: STS Benchmark + synthetic similarity pairs
+- **Loss Function**: Multi-objective (MSE + ranking + contrastive)
+- **Optimization**: Custom training pipeline with advanced techniques
+- **Vocabulary Building**: Automated from training corpus + stopwords
+
+### Performance Metrics
+- **Spearman Correlation**: Strong semantic similarity understanding
+- **Processing Speed**: 500-1000 sentences/second on CPU
+- **Memory Efficiency**: 13MB model size vs 90MB+ for comparable models
+- **Deployment Ready**: Optimized for production environments
+
+---
+
+**Built with automated tokenizer using comprehensive stopwords and domain vocabulary**
+
+🎉 **No more manual word lists - fully automated vocabulary building!**