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README.md

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+# 🧠 Text Similarity Model using Sentence-BERT
+
+This project fine-tunes a Sentence-BERT model (`paraphrase-MiniLM-L6-v2`) on the **STS Benchmark** English dataset (`stsb_multi_mt`) to perform **semantic similarity scoring** between two text inputs.
+
+---
+
+## 🚀 Features
+
+- 🔁 Fine-tunes `sentence-transformers/paraphrase-MiniLM-L6-v2`
+- 🔧 Trained on the `stsb_multi_mt` dataset (English split)
+- 🧪 Predicts cosine similarity between sentence pairs (0 to 1)
+- ⚙️ Uses a custom PyTorch model and manual training loop
+- 💾 Model is saved as `similarity_model.pt`
+- 🧠 Supports inference on custom sentence pairs
+
+---
+
+## 📦 Dependencies
+
+Install required libraries:
+
+```python
+pip install -q transformers datasets sentence-transformers evaluate --upgrade
+```
+
+# 📊 Dataset
+ - Dataset: stsb_multi_mt
+ - Split: "en"
+ - Purpose: Provides sentence pairs with similarity scores ranging from 0 to 5, which are normalized to 0–1 for training.
+
+```python
+
+from datasets import load_dataset
+
+dataset = load_dataset("stsb_multi_mt", name="en", split="train")
+dataset = dataset.shuffle(seed=42).select(range(10000))  # Sample subset for faster training
+```
+
+## 🏗️ Model Architecture
+# ✅ Base Model
+ - sentence-transformers/paraphrase-MiniLM-L6-v2 (from Hugging Face)
+
+# ✅ Fine-Tuning
+ - Cosine similarity computed between the CLS token embeddings of two inputs
+
+ - Loss: Mean Squared Error (MSE) between predicted similarity and true score
+
+# 🧠 Training
+
+ - Epochs: 3
+
+ - Optimizer: Adam
+
+ - Loss: MSELoss
+
+ - Manual training loop using PyTorch
+
+# Files and Structure
+
+📦text-similarity-project
+ ┣ 📜similarity_model.pt          # Trained PyTorch model
+ ┣ 📜training_script.py           # Full training and inference script
+ ┣ 📜README.md                    # Documentation