Upload READ_ME.md

READ_ME.md

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+# DistilBERT Quantized Model for Sentiment Analysis on Yelp Polarity Dataset
+
+This repository hosts a quantized version of the DistilBERT model, fine-tuned for sentiment analysis tasks on the Yelp Polarity dataset. The model has been optimized using post-training quantization to make it suitable for resource-constrained environments while maintaining high accuracy.
+
+## Model Details
+
+- **Model Architecture:** DistilBERT Base Uncased  
+- **Task:** Sentiment Analysis  
+- **Dataset:** Yelp Polarity  
+- **Quantization:** Dynamic Quantization (INT8 on Linear layers)  
+- **Fine-tuning Framework:** Hugging Face Transformers  
+
+---
+
+### Installation
+
+```sh
+pip install transformers datasets evaluate scikit-learn torch
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load trained model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained("./results")
+tokenizer = AutoTokenizer.from_pretrained("./results")
+
+# Set model to eval mode
+model.eval()
+
+# 10 Sample review texts
+sample_texts = [
+    "The food was absolutely wonderful!",
+    "Terrible experience. I will never come back.",
+    "Average service, but the food was decent.",
+    "I loved the ambiance and the staff was super friendly!",
+    "Worst food I've had in a long time.",
+    "Highly recommend this place for a date night.",
+    "The waiter was rude and the food was cold.",
+    "Amazing pizza, will order again!",
+    "They took too long to serve and it was overpriced.",
+    "Best customer service and delicious desserts!"
+]
+
+# Predict and print results
+for text in sample_texts:
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        prediction = torch.argmax(outputs.logits, dim=-1).item()
+        sentiment = "Positive" if prediction == 1 else "Negative"
+    print(f"Text: {text}\\nPredicted Sentiment: {sentiment}\\n")
+
+Quantization
+
+import os
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+# Load the fine-tuned model
+model = AutoModelForSequenceClassification.from_pretrained("./results")
+
+# Apply dynamic quantization
+quantized_model = torch.quantization.quantize_dynamic(
+    model,
+    {torch.nn.Linear},
+    dtype=torch.qint8
+)
+
+# Define path
+quantized_model_path = "./results/quantized_model"
+
+# Create directory if it doesn't exist
+os.makedirs(quantized_model_path, exist_ok=True)
+
+# Save the quantized model weights
+torch.save(quantized_model.state_dict(), f"{quantized_model_path}/pytorch_model.bin")
+
+# Save config and tokenizer
+model.config.save_pretrained(quantized_model_path)
+tokenizer = AutoTokenizer.from_pretrained("./results")
+tokenizer.save_pretrained(quantized_model_path)
+
+print("✅ Quantized model saved at:", quantized_model_path)
+
+Performance Metrics
+Accuracy: Approx. 95% on Yelp Polarity Test Subset
+
+Precision, Recall, F1-score: Computed during evaluation using scikit-learn
+
+Fine-Tuning Details
+Dataset
+Source: Yelp Polarity (via Hugging Face Datasets)
+
+Train samples used: 50,000
+
+Test samples used: 10,000
+
+Training
+Number of epochs: 3
+
+Batch size: 16
+
+Evaluation strategy: Per epoch
+
+Learning rate: 2e-5
+
+Weight decay: 0.01
+
+Repository-Structure
+
+.
+├── results/                     # Contains fine-tuned and quantized model files
+│   ├── pytorch_model.bin        # Quantized model weights
+│   ├── config.json              # Model config
+│   └── tokenizer/               # Tokenizer files
+├── logs/                        # Training logs
+├── README.md                    # Model documentation
+ 
+
+Limitations
+
+The model is trained only on Yelp reviews and may not generalize to other domains.
+
+Post-training quantization may cause minor accuracy degradation compared to full-precision models.
+