| | --- |
| | license: mit |
| | language: |
| | - en |
| | metrics: |
| | - accuracy |
| | - f1 |
| | - precision |
| | - recall |
| | library_name: transformers |
| | pipeline_tag: text-classification |
| | --- |
| | # Total Samples |
| | Samples: 716017(Train+Test) |
| |
|
| | Training Samples: 579973 |
| |
|
| | Validation Samples :64442 |
| |
|
| | Test Samples :71602 |
| |
|
| | # Overall Metrics |
| | Accuracy :92% |
| |
|
| | F1 Score:92% |
| |
|
| | Recall:92% |
| |
|
| | Precisison: 92% |
| |
|
| | # Fine Tune Parameters |
| | No of epochs: 3 |
| |
|
| | Batch Size: 16 |
| |
|
| | evaluation startegy: epoch |
| |
|
| | optimiser:Adamw |
| |
|
| | learning_rate:2e-5 |
| | |
| | max_steps:1000 |
| |
|
| | warmup_step: 100 |
| | |
| | Monitoring Train & Evaluation:WANDB API |
| | |
| | # Train |
| | train_runtime': 1594.4072, 'train_samples_per_second': 80.281, 'train_steps_per_second': 0.627, 'total_flos': 5589761482241280.0, 'train_loss': 0.26639655661582945, 'epoch': 0.22 |
| |
|
| | # Validation |
| | 'eval_loss': 0.22991116344928741,'eval_accuracy': 0.9211073523478477,'eval_precision': 0.9213582014463746,'eval_recall': 0.921107352347847'eval_f1': 0.9210970707304227, |
| | |
| | 'eval_runtime': 238.5409,'eval_samples_per_second': 270.151,'eval_steps_per_second': 8.443,'epoch': 0.22 |
