| # 💬 Sentiment-Analysis-for-Product-Release-Sentiment | |
| A **BERT-based sentiment analysis model** fine-tuned on a product review dataset. It predicts the sentiment of a text as **Positive**, **Neutral**, or **Negative** with a confidence score. This model is ideal for analyzing customer feedback, reviews, or user comments. | |
| --- | |
| ## ✨ Model Highlights | |
| - 📌 **Architecture**: Based on [`bert-base-uncased`](https://huggingface.co/bert-base-uncased) by Google | |
| - 🔧 **Fine-tuned** on labeled product review data | |
| - 🔍 **3-way sentiment classification**: `Negative (0)`, `Neutral (1)`, `Positive (2)` | |
| - 💾 **Quantized version available** for faster inference | |
| --- | |
| ## 🧠 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 | |
| - ❌ May not perform well on sarcastic or ironic inputs | |
| - ❌ May struggle with domains very different from product reviews | |
| - ❌ Input texts longer than 128 tokens are truncated | |
| --- | |
| ## 🏋️♂️ Training Details | |
| - **Base Model**: `bert-base-uncased` | |
| - **Dataset**: Custom-labeled product review dataset | |
| - **Epochs**: 5 | |
| - **Batch Size**: 8 | |
| - **Max Length**: 128 tokens | |
| - **Optimizer**: AdamW | |
| - **Loss Function**: CrossEntropyLoss (with class balancing) | |
| - **Hardware**: Trained on NVIDIA GPU (CUDA-enabled) | |
| --- | |
| ## 📊 Evaluation Metrics | |
| | Metric | Score | | |
| |------------|-------| | |
| | Accuracy | 0.90 | | |
| | F1 | 0.90 | | |
| | Precision | 0.90 | | |
| | Recall | 0.90 | | |
| --- | |
| ## 🔎 Label Mapping | |
| | Label ID | Sentiment | | |
| |----------|-----------| | |
| | 0 | Negative | | |
| | 1 | Neutral | | |
| | 2 | Positive | | |
| --- | |
| ## 🚀 Usage Example | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments | |
| from transformers import DataCollatorWithPadding | |
| import torch | |
| import torch.nn.functional as F | |
| # Load model and tokenizer | |
| model_name = "AventIQ-AI/Sentiment-Analysis-for-Product-Release-Sentiment" | |
| tokenizer = BertTokenizer.from_pretrained(model_name) | |
| model = BertForSequenceClassification.from_pretrained(model_name) | |
| model.eval() | |
| # Inference | |
| def predict_sentiment(text): | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) | |
| inputs = {k: v.to(quantized_model.device) for k, v in inputs.items()} | |
| with torch.no_grad(): | |
| logits = quantized_model(**inputs).logits | |
| probs = F.softmax(logits, dim=1) | |
| predicted_class_id = torch.argmax(probs, dim=1).item() | |
| confidence = probs[0][predicted_class_id].item() | |
| label_map = {0: "Negative", 1: "Positive"} | |
| label = label_map[predicted_class_id] | |
| confidence_str = f"confidence : {confidence * 100:.1f}%" | |
| return label, confidence_str | |
| # Example | |
| print(predict_sentiment( | |
| "The service was excellent and the staff was friendly.") | |
| ) | |
| ``` | |
| --- | |
| ## 🧪 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 | |
| ``` | |
| --- | |
| ## 📌 Limitations | |
| - May not generalize to completely different domains (e.g., medical, legal) | |
| - Quantized version may show slight drop in accuracy compared to full-precision model | |
| --- | |
| ## 🤝 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. |