AventIQ-AI / Sentiment-Analysis-for-Contract-Sentiment

📄 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

🔎 Label Mapping

🚀 Usage Example

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.