| # BERT-Base-Uncased Quantized Model for twitter-tweet-sentiment-classification |
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| This repository hosts a quantized version of the BERT model, fine-tuned for Facebook-post-sentiment-classification tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments. |
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| ## Model Details |
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| - **Model Architecture:** BERT Base Uncased |
| - **Task:** Facebook-post-sentiment-classification |
| - **Dataset:** Stanford Sentiment Treebank v2 (SST2) |
| - **Quantization:** Float16 |
| - **Fine-tuning Framework:** Hugging Face Transformers |
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| ## Usage |
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| ### Installation |
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| ```sh |
| pip install transformers torch |
| ``` |
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| ### Loading the Model |
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| ```python |
| |
| from transformers import BertForSequenceClassification, BertTokenizer |
| import torch |
| |
| # Load quantized model |
| quantized_model_path = "/kaggle/working/bert_finetuned_fp16" |
| quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path) |
| quantized_model.eval() # Set to evaluation mode |
| quantized_model.half() # Convert model to FP16 |
| |
| # Load tokenizer |
| tokenizer = BertTokenizer.from_pretrained("bert-base-uncased") |
| |
| # Define a test sentence |
| test_sentence = "It's just fine, nothing extraordinary" |
| |
| # Tokenize input |
| inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128) |
| |
| # Ensure input tensors are in correct dtype |
| inputs["input_ids"] = inputs["input_ids"].long() # Convert to long type |
| inputs["attention_mask"] = inputs["attention_mask"].long() # Convert to long type |
| |
| # Make prediction |
| with torch.no_grad(): |
| outputs = quantized_model(**inputs) |
| |
| # Get predicted class |
| predicted_class = torch.argmax(outputs.logits, dim=1).item() |
| print(f"Predicted Class: {predicted_class}") |
| |
| |
| label_mapping = {0: "very_negative", 1: "nagative", 2: "neutral", 3: "Positive", 4: "very_positive"} # Example |
| |
| predicted_label = label_mapping[predicted_class] |
| print(f"Predicted Label: {predicted_label}") |
| |
| ``` |
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| ## Performance Metrics |
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| - **Accuracy:** 0.82 |
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| ## Fine-Tuning Details |
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| ### Dataset |
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| The dataset is taken from Kaggle Stanford Sentiment Treebank v2 (SST2). |
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| ### Training |
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| - Number of epochs: 3 |
| - Batch size: 8 |
| - Evaluation strategy: epoch |
| - Learning rate: 2e-5 |
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| ### Quantization |
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| Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency. |
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| ## Repository Structure |
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| ``` |
| . |
| ├── model/ # Contains the quantized model files |
| ├── tokenizer_config/ # Tokenizer configuration and vocabulary files |
| ├── model.safensors/ # Fine Tuned Model |
| ├── README.md # Model documentation |
| ``` |
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| ## Limitations |
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| - 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. |
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| ## Contributing |
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| Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements. |
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