Create README.md

README.md

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+# 📄 Contract Sentiment Classifier (BERT)
+
+A fine-tuned BERT model for contract sentiment analysis, classifying legal or contractual text into positive, negative, or neutral sentiments.
+
+
+## 🧠 Model Details
+
+- 📌**Base Model**: bert-base-uncased
+- 🔧**Task**: Sentiment Classification (Contractual Text)
+- 🔍 **Labels**: `Negative (0)`, `Neutral (1)`, `Positive (2)`
+- 💾 **Quantized version available**: for faster inference
+- 🧠 **Framework**: PyTorch, Transformers (🤗 Hugging Face)
+
+
+
+
+## 🧠 Intended Uses
+
+- ✅ Classifying product feedback and user reviews
+- ✅ Sentiment analysis for e-commerce platforms
+- ✅ Social media monitoring and customer opinion mining
+
+---
+
+## 🚫 Limitations
+
+- ❌ Designed for English texts only
+- ❌Needs further tuning and evaluation on larger, diverse contract.
+- ❌ Not suitable for production use without robustness checks.
+
+---
+
+## 🏋️‍♂️ Training Details
+
+- **Base Model**: `bert-base-uncased`
+- **Dataset**: Custom labeled Contract Sentiment dataset
+- **Epochs**: 3
+- **Batch Size**: 5
+- **Learning rate**: AdamW
+- **Hardware**: Trained on NVIDIA GPU (CUDA-enabled)
+
+---
+
+## 📊 Evaluation Metrics
+
+| Metric     | Score |
+|------------|-------|
+| Accuracy   | 0.98  |
+| F1         | 0.99  |
+| Precision  | 0.99  |
+| Recall     | 0.97  |
+
+---
+
+## 🔎 Label Mapping
+
+| Label ID | Sentiment |
+|----------|-----------|
+| 0        | Negative  |
+| 1        | Neutral   |
+| 2        | Positive  |
+
+---
+
+## 🚀 Usage Example
+
+```python
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+from sklearn.metrics import accuracy_score, precision_recall_fscore_support
+import torch
+from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments
+from datasets import Dataset
+import torch.nn.functional as F
+
+# Load model and tokenizer
+model_name = "AventIQ-AI/Sentiment-Analysis-for-Contract-Sentiment"
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=3)
+model.eval()
+
+def tokenize_function(examples):
+    return tokenizer(examples['text'], padding='max_length', truncation=True)
+
+
+# Inference
+def predict_sentiment(user_text):
+    # Ensure input is a list for batch processing
+    if isinstance(user_text, str):
+        user_text = [user_text]
+
+    # Tokenize input text
+    inputs = tokenizer(user_text, return_tensors="pt", padding=True, truncation=True)
+
+    # Predict using the model
+    with torch.no_grad():
+        outputs = model(**inputs)
+    preds = torch.argmax(outputs.logits, dim=1)
+
+    # Decode predictions back to original sentiment labels
+    decoded_preds = label_encoder.inverse_transform(preds.numpy())
+
+    # Print each prediction
+    for text, sentiment in zip(user_text, decoded_preds):
+        print(f"Text: '{text}' => Sentiment: {sentiment}")
+
+
+# Example
+predict_sentiment("The delivery scheduled")
+```
+
+---
+
+## 🧪 Quantization
+
+- Applied **post-training dynamic quantization** using PyTorch to reduce model size and speed up inference.
+- Quantized model supports CPU-based deployments.
+
+---
+
+## 📁 Repository Structure
+
+```
+.
+├── model/               # Quantized model files
+├── tokenizer/           # Tokenizer config and vocabulary
+├── model.safetensors/   # Fine-tuned full-precision model
+├── README.md            # Model documentation
+```
+
+---
+
+
+
+## 🤝 Contributing
+
+We welcome contributions! Please feel free to raise an issue or submit a pull request if you find a bug or have a suggestion.