| # Microaggression Text Reframing Model - Training Report | |
| ## Model Configuration | |
| - Base Model: t5-base | |
| - Total Parameters: 222,903,552 | |
| - Trainable Parameters: 222,903,552 | |
| - Training Epochs: 5 | |
| - Batch Size: 8 | |
| - Learning Rate: 5e-05 | |
| - Max Sequence Length: 256 | |
| - GPUs Used: 2 | |
| ## Training Results | |
| - Final Training Loss: nan | |
| - Final Validation Loss: 1.1424 | |
| - Best Validation Loss: 1.0700 (Epoch 3) | |
| ## Evaluation Metrics | |
| - Average BLEU Score: 0.1307 | |
| - Average ROUGE-1: 0.4270 | |
| - Average ROUGE-2: 0.1896 | |
| - Average ROUGE-L: 0.3932 | |
| ## Files Saved | |
| - Model files: pytorch_model.bin, config.json | |
| - Tokenizer files: tokenizer.json, spiece.model | |
| - Training history: training_history.json, training_history.pkl | |
| - Evaluation results: evaluation_results.json | |
| - Detailed predictions: detailed_predictions.csv | |
| - Visualization plots: training_plots.png/pdf, evaluation_plots.png/pdf | |
| - Statistics: score_statistics.csv, score_correlation_matrix.csv | |
| - Loss history: loss_history.csv | |
| ## Usage Example | |
| ```python | |
| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| # Load the model | |
| tokenizer = T5Tokenizer.from_pretrained('/kaggle/working/microaggression_reframing_model') | |
| model = T5ForConditionalGeneration.from_pretrained('/kaggle/working/microaggression_reframing_model') | |
| # Generate reframed text | |
| input_text = "Your microaggressive text here" | |
| prefixed_text = f"rephrase: {input_text}" | |
| inputs = tokenizer(prefixed_text, return_tensors='pt', max_length=256, truncation=True) | |
| outputs = model.generate(**inputs, max_length=256, num_beams=4, early_stopping=True) | |
| reframed_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| ``` | |
| Report generated on: 2025-10-22 00:02:37.399982 | |