| # DistilBERT Base Uncased Quantized Model for Sentiment Analysis | |
| This repository hosts a quantized version of the DistilBERT model, fine-tuned for 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:** DistilBERT Base Uncased | |
| - **Task:** Sentiment Analysis | |
| - **Dataset:** IMDB Reviews | |
| - **Quantization:** Float16 | |
| - **Fine-tuning Framework:** Hugging Face Transformers | |
| ## Usage | |
| ### Installation | |
| ```sh | |
| pip install transformers torch | |
| ``` | |
| ### Loading the Model | |
| ```python | |
| from transformers import DistilBertTokenizer, DistilBertForSequenceClassification, Trainer, TrainingArguments | |
| import torch | |
| model_name = "AventIQ-AI/distilbert-base-uncased-sentiment-analysis" | |
| tokenizer = DistilBertTokenizer.from_pretrained(model_name) | |
| model = DistilBertForSequenceClassification.from_pretrained(model_name) | |
| def predict_sentiment(text): | |
| inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512) | |
| with torch.no_grad(): | |
| logits = model(**inputs).logits | |
| predicted_class_id = torch.argmax(logits, dim=-1).item() | |
| return "Positive" if predicted_class_id == 1 else "Negative" | |
| # Test the model with a sample sentence | |
| test_text = "I absolutely loved the movie! It was fantastic." | |
| print(f"Sentiment: {predict_sentiment(test_text)}") | |
| ``` | |
| ## Performance Metrics | |
| - **Accuracy:** 0.56 | |
| - **F1 Score:** 0.56 | |
| - **Precision:** 0.68 | |
| - **Recall:** 0.56 | |
| ## Fine-Tuning Details | |
| ### Dataset | |
| The IMDb Reviews dataset was used, containing both positive and negative sentiment examples. | |
| ### Training | |
| - Number of epochs: 3 | |
| - Batch size: 16 | |
| - Evaluation strategy: epoch | |
| - Learning rate: 2e-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. | |