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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|---|---|---|---|---|---|---|---|
| 0.4339 | 1.0 | 3600 | 0.4173 | {'accuracy': 0.8925} | {'precision': 0.857630979498861} | {'recall': 0.94125} | {'f1': 0.8974970202622169} |
| 0.3848 | 2.0 | 7200 | 0.5757 | {'accuracy': 0.854375} | {'precision': 0.9341500765696784} | {'recall': 0.7625} | {'f1': 0.8396421197522368} |
| 0.4094 | 3.0 | 10800 | 0.3543 | {'accuracy': 0.904375} | {'precision': 0.8655367231638418} | {'recall': 0.9575} | {'f1': 0.9091988130563798} |
| 0.3937 | 4.0 | 14400 | 0.2576 | {'accuracy': 0.91125} | {'precision': 0.9092039800995025} | {'recall': 0.91375} | {'f1': 0.9114713216957606} |
| 0.3401 | 5.0 | 18000 | 0.2671 | {'accuracy': 0.91625} | {'precision': 0.9291237113402062} | {'recall': 0.90125} | {'f1': 0.9149746192893401} |
| 0.352 | 6.0 | 21600 | 0.2429 | {'accuracy': 0.91875} | {'precision': 0.9294871794871795} | {'recall': 0.90625} | {'f1': 0.9177215189873418} |
| 0.2883 | 7.0 | 25200 | 0.2857 | {'accuracy': 0.915625} | {'precision': 0.917189460476788} | {'recall': 0.91375} | {'f1': 0.915466499686913} |
| 0.2894 | 8.0 | 28800 | 0.2270 | {'accuracy': 0.92375} | {'precision': 0.9302030456852792} | {'recall': 0.91625} | {'f1': 0.9231738035264484} |
| 0.282 | 9.0 | 32400 | 0.2518 | {'accuracy': 0.92} | {'precision': 0.9189526184538653} | {'recall': 0.92125} | {'f1': 0.920099875156055} |
| 0.2485 | 10.0 | 36000 | 0.2351 | {'accuracy': 0.92375} | {'precision': 0.9269521410579346} | {'recall': 0.92} | {'f1': 0.9234629861982434} |