| # BERT-Base-Uncased Quantized Model for customer feedback sentiment analysis | |
| This repository hosts a quantized version of the **bert-base-uncased** model, fine-tuned for social media sentiment analysis tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments. | |
| ## Model Details | |
| - **Model Architecture:** BERT Base Uncased | |
| - **Task:** Social Media Sentiment Analysis | |
| - **Dataset:** Social Media Sentiments Analysis Dataset [Kaggle] | |
| - **Quantization:** Float16 | |
| - **Fine-tuning Framework:** Hugging Face Transformers | |
| ## Usage | |
| ### Installation | |
| ```sh | |
| pip install transformers torch | |
| ``` | |
| ### Loading the Model | |
| ```python | |
| from transformers import BertForSequenceClassification, BertTokenizer | |
| import torch | |
| device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
| # Load quantized model | |
| model_name = "AventIQ-AI/sentiment_analysis_for_customer_feedback" | |
| model = BertForSequenceClassification.from_pretrained(model_name).to(device) | |
| tokenizer = BertTokenizer.from_pretrained(model_name) | |
| #Function to make analysis | |
| def predict_sentiment(text): | |
| # Tokenize input text | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512) | |
| # Move tensors to GPU if available | |
| inputs = {key: val.to(device) for key, val in inputs.items()} | |
| # Get model prediction | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| # Get predicted class | |
| logits = outputs.logits | |
| predicted_class = torch.argmax(logits, dim=1).item() | |
| # Map back to sentiment labels | |
| sentiment_labels = {0: "Negative", 1: "Neutral", 2: "Positive"} | |
| return sentiment_labels[predicted_class] | |
| # Define a test sentence | |
| test_sentence = "Spending time with family always brings me so much joy." | |
| print(f"Predicted Sentiment: {predict_sentiment(text)}") | |
| ``` | |
| ## Performance Metrics | |
| - **Accuracy:** 0.82 | |
| - **Precision:** 0.68 | |
| - **Recall:** 0.82 | |
| - **F1 Score:** 0.73 | |
| ## Fine-Tuning Details | |
| ### Dataset | |
| The dataset is taken from Kaggle Social Media Sentiment Analysis. | |
| ### Training | |
| - Number of epochs: 6 | |
| - Batch size: 8 | |
| - Evaluation strategy: epoch | |
| - Learning rate: 3e-5 | |
| ### Quantization | |
| Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency. | |
| ## Repository Structure | |
| ``` | |
| . | |
| ├── model/ # Contains the quantized model files | |
| ├── tokenizer_config/ # Tokenizer configuration and vocabulary files | |
| ├── model.safensors/ # Fine Tuned Model | |
| ├── README.md # Model documentation | |
| ``` | |
| ## Limitations | |
| - The model may not generalize well to domains outside the fine-tuning dataset. | |
| - Quantization may result in minor accuracy degradation compared to full-precision models. | |
| ## Contributing | |
| Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements. | |