wandb: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.
wandb: Currently logged in as: nymiz to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.19.9
wandb: Run data is saved locally in /Users/polodealvarado/Desktop/tfg-javito/wandb/run-20250426_101719-58w0rceh
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run distilbert-base-multilingual
wandb: ⭐️ View project at https://wandb.ai/nymiz/amazon-reviews-es
wandb: 🚀 View run at https://wandb.ai/nymiz/amazon-reviews-es/runs/58w0rceh
2025-04-26 10:17:19.780 | INFO     | __main__:main:63 - Loading dataset...
Repo card metadata block was not found. Setting CardData to empty.
2025-04-26 10:17:22.027 | INFO     | __main__:main:67 - Loading tokenizer and model...
Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
2025-04-26 10:17:22.472 | INFO     | __main__:main:84 - Tokenizing dataset...

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/Users/polodealvarado/Desktop/tfg-javito/.venv/lib/python3.12/site-packages/transformers/training_args.py:2243: UserWarning: `use_mps_device` is deprecated and will be removed in version 5.0 of 🤗 Transformers. `mps` device will be used by default if available similar to the way `cuda` device is used.Therefore, no action from user is required. 
  warnings.warn(
2025-04-26 10:17:23.160 | INFO     | __main__:main:124 - Starting training...

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  1%|          | 509/50000 [02:44<4:22:38,  3.14it/s]
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  1%|          | 569/50000 [03:03<4:20:55,  3.16it/s]
  1%|          | 570/50000 [03:03<4:22:09,  3.14it/s]
  1%|          | 571/50000 [03:04<4:22:00,  3.14it/s]
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  1%|          | 590/50000 [03:10<4:21:11,  3.15it/s]
  1%|          | 591/50000 [03:10<4:21:05,  3.15it/s]
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  1%|          | 596/50000 [03:12<4:21:07,  3.15it/s]
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  1%|▏         | 633/50000 [03:23<4:19:43,  3.17it/s]
  1%|▏         | 634/50000 [03:24<4:19:42,  3.17it/s]
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  1%|▏         | 638/50000 [03:25<4:19:48,  3.17it/s]
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  1%|▏         | 640/50000 [03:25<4:20:05,  3.16it/s]
  1%|▏         | 641/50000 [03:26<4:19:40,  3.17it/s]
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  2%|▏         | 750/50000 [04:01<4:22:22,  3.13it/s]
  2%|▏         | 751/50000 [04:01<4:21:09,  3.14it/s]
  2%|▏         | 752/50000 [04:01<4:22:27,  3.13it/s]
  2%|▏         | 753/50000 [04:02<4:22:54,  3.12it/s]
  2%|▏         | 754/50000 [04:02<4:24:59,  3.10it/s]
  2%|▏         | 755/50000 [04:02<4:24:08,  3.11it/s]
  2%|▏         | 756/50000 [04:03<4:22:33,  3.13it/s]
  2%|▏         | 757/50000 [04:03<4:22:08,  3.13it/s]
  2%|▏         | 758/50000 [04:03<4:22:49,  3.12it/s]
  2%|▏         | 759/50000 [04:04<4:21:53,  3.13it/s]
  2%|▏         | 760/50000 [04:04<4:22:53,  3.12it/s]
  2%|▏         | 761/50000 [04:04<4:22:56,  3.12it/s]
  2%|▏         | 762/50000 [04:04<4:22:42,  3.12it/s]
  2%|▏         | 763/50000 [04:05<4:23:09,  3.12it/s]
  2%|▏         | 764/50000 [04:05<4:23:31,  3.11it/s]
  2%|▏         | 765/50000 [04:05<4:23:17,  3.12it/s]
  2%|▏         | 766/50000 [04:06<4:22:09,  3.13it/s]
  2%|▏         | 767/50000 [04:06<4:24:43,  3.10it/s]
  2%|▏         | 768/50000 [04:06<4:26:10,  3.08it/s]
  2%|▏         | 769/50000 [04:07<4:27:40,  3.07it/s]
  2%|▏         | 770/50000 [04:07<4:27:46,  3.06it/s]
  2%|▏         | 771/50000 [04:07<4:27:05,  3.07it/s]
  2%|▏         | 772/50000 [04:08<4:28:45,  3.05it/s]
  2%|▏         | 773/50000 [04:08<4:29:00,  3.05it/s]
  2%|▏         | 774/50000 [04:08<4:27:47,  3.06it/s]
  2%|▏         | 775/50000 [04:09<4:26:39,  3.08it/s]
  2%|▏         | 776/50000 [04:09<4:25:53,  3.09it/s]
  2%|▏         | 777/50000 [04:09<4:23:49,  3.11it/s]
  2%|▏         | 778/50000 [04:10<4:23:24,  3.11it/s]
  2%|▏         | 779/50000 [04:10<4:22:35,  3.12it/s]
  2%|▏         | 780/50000 [04:10<4:21:55,  3.13it/s]
  2%|▏         | 781/50000 [04:11<4:23:20,  3.12it/s]
  2%|▏         | 782/50000 [04:11<4:21:55,  3.13it/s]
  2%|▏         | 783/50000 [04:11<4:21:06,  3.14it/s]
  2%|▏         | 784/50000 [04:12<4:21:38,  3.14it/s]
  2%|▏         | 785/50000 [04:12<4:22:19,  3.13it/s]
  2%|▏         | 786/50000 [04:12<4:21:14,  3.14it/s]
  2%|▏         | 787/50000 [04:13<4:21:29,  3.14it/s]
  2%|▏         | 788/50000 [04:13<4:22:46,  3.12it/s]
  2%|▏         | 789/50000 [04:13<4:22:20,  3.13it/s]
  2%|▏         | 790/50000 [04:13<4:21:26,  3.14it/s]
  2%|▏         | 791/50000 [04:14<4:22:41,  3.12it/s]
  2%|▏         | 792/50000 [04:14<4:21:53,  3.13it/s]
  2%|▏         | 793/50000 [04:14<4:22:36,  3.12it/s]
  2%|▏         | 794/50000 [04:15<4:21:11,  3.14it/s]
  2%|▏         | 795/50000 [04:15<4:22:00,  3.13it/s]
  2%|▏         | 796/50000 [04:15<4:22:39,  3.12it/s]
  2%|▏         | 797/50000 [04:16<4:26:06,  3.08it/s]
  2%|▏         | 798/50000 [04:16<4:25:03,  3.09it/s]
  2%|▏         | 799/50000 [04:16<4:24:19,  3.10it/s]
  2%|▏         | 800/50000 [04:17<4:23:23,  3.11it/s]
  2%|▏         | 801/50000 [04:17<4:24:28,  3.10it/s]
  2%|▏         | 802/50000 [04:17<4:24:46,  3.10it/s]
  2%|▏         | 803/50000 [04:18<4:24:46,  3.10it/s]
  2%|▏         | 804/50000 [04:18<4:23:51,  3.11it/s]
  2%|▏         | 805/50000 [04:18<4:23:48,  3.11it/s]
  2%|▏         | 806/50000 [04:19<4:23:11,  3.12it/s]
  2%|▏         | 807/50000 [04:19<4:22:20,  3.13it/s]
  2%|▏         | 808/50000 [04:19<4:22:05,  3.13it/s]
  2%|▏         | 809/50000 [04:20<4:22:15,  3.13it/s]
  2%|▏         | 810/50000 [04:20<4:21:37,  3.13it/s]
  2%|▏         | 811/50000 [04:20<4:21:57,  3.13it/s]
  2%|▏         | 812/50000 [04:21<4:22:26,  3.12it/s]
  2%|▏         | 813/50000 [04:21<4:22:32,  3.12it/s]
  2%|▏         | 814/50000 [04:21<4:21:39,  3.13it/s]
  2%|▏         | 815/50000 [04:22<4:20:36,  3.15it/s]
  2%|▏         | 816/50000 [04:22<4:26:34,  3.08it/s]
  2%|▏         | 817/50000 [04:22<4:22:03,  3.13it/s]
  2%|▏         | 818/50000 [04:22<4:22:18,  3.12it/s]
  2%|▏         | 819/50000 [04:23<4:25:25,  3.09it/s]
  2%|▏         | 820/50000 [04:23<4:25:40,  3.09it/s]
  2%|▏         | 821/50000 [04:23<4:24:40,  3.10it/s]
  2%|▏         | 822/50000 [04:24<4:23:14,  3.11it/s]
  2%|▏         | 823/50000 [04:24<4:22:17,  3.12it/s]
  2%|▏         | 824/50000 [04:24<4:24:04,  3.10it/s]
  2%|▏         | 825/50000 [04:25<4:24:15,  3.10it/s]
  2%|▏         | 826/50000 [04:25<4:22:36,  3.12it/s]
  2%|▏         | 827/50000 [04:25<4:22:16,  3.12it/s]
  2%|▏         | 828/50000 [04:26<4:21:25,  3.13it/s]
  2%|▏         | 829/50000 [04:26<4:22:05,  3.13it/s]
  2%|▏         | 830/50000 [04:26<4:22:35,  3.12it/s]
  2%|▏         | 831/50000 [04:27<4:22:21,  3.12it/s]
  2%|▏         | 832/50000 [04:27<4:21:48,  3.13it/s]
  2%|▏         | 833/50000 [04:27<4:22:13,  3.12it/s]
  2%|▏         | 834/50000 [04:28<4:23:57,  3.10it/s]
  2%|▏         | 835/50000 [04:28<4:22:37,  3.12it/s]
  2%|▏         | 836/50000 [04:28<4:22:22,  3.12it/s]
  2%|▏         | 837/50000 [04:29<4:22:15,  3.12it/s]
  2%|▏         | 838/50000 [04:29<4:21:57,  3.13it/s]
  2%|▏         | 839/50000 [04:29<4:21:04,  3.14it/s]
  2%|▏         | 840/50000 [04:30<4:21:16,  3.14it/s]
  2%|▏         | 841/50000 [04:30<4:21:38,  3.13it/s]
  2%|▏         | 842/50000 [04:30<4:20:39,  3.14it/s]
  2%|▏         | 843/50000 [04:30<4:20:39,  3.14it/s]
  2%|▏         | 844/50000 [04:31<4:20:17,  3.15it/s]
  2%|▏         | 845/50000 [04:31<4:20:26,  3.15it/s]
  2%|▏         | 846/50000 [04:31<4:20:00,  3.15it/s]
  2%|▏         | 847/50000 [04:32<4:20:27,  3.15it/s]
  2%|▏         | 848/50000 [04:32<4:21:29,  3.13it/s]
  2%|▏         | 849/50000 [04:32<4:20:52,  3.14it/s]
  2%|▏         | 850/50000 [04:33<4:20:33,  3.14it/s]
  2%|▏         | 851/50000 [04:33<4:20:22,  3.15it/s]
  2%|▏         | 852/50000 [04:33<4:20:41,  3.14it/s]
  2%|▏         | 853/50000 [04:34<4:20:23,  3.15it/s]
  2%|▏         | 854/50000 [04:34<4:19:54,  3.15it/s]
  2%|▏         | 855/50000 [04:34<4:19:58,  3.15it/s]
  2%|▏         | 856/50000 [04:35<4:20:07,  3.15it/s]
  2%|▏         | 857/50000 [04:35<4:19:49,  3.15it/s]
  2%|▏         | 858/50000 [04:35<4:20:33,  3.14it/s]
  2%|▏         | 859/50000 [04:36<4:20:18,  3.15it/s]
  2%|▏         | 860/50000 [04:36<4:20:29,  3.14it/s]
  2%|▏         | 861/50000 [04:36<4:20:59,  3.14it/s]
  2%|▏         | 862/50000 [04:37<4:20:40,  3.14it/s]
  2%|▏         | 863/50000 [04:37<4:20:07,  3.15it/s]
  2%|▏         | 864/50000 [04:37<4:20:37,  3.14it/s]
  2%|▏         | 865/50000 [04:37<4:21:08,  3.14it/s]
  2%|▏         | 866/50000 [04:38<4:20:24,  3.14it/s]
  2%|▏         | 867/50000 [04:38<4:21:11,  3.14it/s]
  2%|▏         | 868/50000 [04:38<4:20:46,  3.14it/s]
  2%|▏         | 869/50000 [04:39<4:20:34,  3.14it/s]
  2%|▏         | 870/50000 [04:39<4:21:18,  3.13it/s]
  2%|▏         | 871/50000 [04:39<4:21:48,  3.13it/s]
  2%|▏         | 872/50000 [04:40<4:21:17,  3.13it/s]
  2%|▏         | 873/50000 [04:40<4:22:05,  3.12it/s]
  2%|▏         | 874/50000 [04:40<4:21:48,  3.13it/s]
  2%|▏         | 875/50000 [04:41<4:22:27,  3.12it/s]
  2%|▏         | 876/50000 [04:41<4:22:16,  3.12it/s]
  2%|▏         | 877/50000 [04:41<4:25:46,  3.08it/s]
  2%|▏         | 878/50000 [04:42<4:21:13,  3.13it/s]
  2%|▏         | 879/50000 [04:42<4:20:51,  3.14it/s]
  2%|▏         | 880/50000 [04:42<4:20:49,  3.14it/s]
  2%|▏         | 881/50000 [04:43<4:21:20,  3.13it/s]
  2%|▏         | 882/50000 [04:43<4:19:34,  3.15it/s]
  2%|▏         | 883/50000 [04:43<4:19:47,  3.15it/s]
  2%|▏         | 884/50000 [04:44<4:19:48,  3.15it/s]
  2%|▏         | 885/50000 [04:44<4:20:27,  3.14it/s]
  2%|▏         | 886/50000 [04:44<4:19:54,  3.15it/s]
  2%|▏         | 887/50000 [04:44<4:19:47,  3.15it/s]
  2%|▏         | 888/50000 [04:45<4:19:53,  3.15it/s]
  2%|▏         | 889/50000 [04:45<4:19:42,  3.15it/s]
  2%|▏         | 890/50000 [04:45<4:20:15,  3.15it/s]
  2%|▏         | 891/50000 [04:46<4:19:51,  3.15it/s]
  2%|▏         | 892/50000 [04:46<4:21:06,  3.13it/s]
  2%|▏         | 893/50000 [04:46<4:19:32,  3.15it/s]
  2%|▏         | 894/50000 [04:47<4:19:27,  3.15it/s]
  2%|▏         | 895/50000 [04:47<4:19:38,  3.15it/s]
  2%|▏         | 896/50000 [04:47<4:20:04,  3.15it/s]
  2%|▏         | 897/50000 [04:48<4:19:40,  3.15it/s]
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  2%|▏         | 902/50000 [04:49<4:21:07,  3.13it/s]
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  2%|▏         | 906/50000 [04:51<4:19:33,  3.15it/s]
  2%|▏         | 907/50000 [04:51<4:19:39,  3.15it/s]
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  2%|▏         | 910/50000 [04:52<4:24:48,  3.09it/s]
  2%|▏         | 911/50000 [04:52<4:19:09,  3.16it/s]
  2%|▏         | 912/50000 [04:52<4:19:41,  3.15it/s]
  2%|▏         | 913/50000 [04:53<4:19:42,  3.15it/s]
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  2%|▏         | 922/50000 [04:56<4:21:08,  3.13it/s]
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  2%|▏         | 924/50000 [04:56<4:22:19,  3.12it/s]
  2%|▏         | 925/50000 [04:57<4:24:22,  3.09it/s]
  2%|▏         | 926/50000 [04:57<4:20:30,  3.14it/s]
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  2%|▏         | 932/50000 [04:59<4:19:37,  3.15it/s]
  2%|▏         | 933/50000 [04:59<4:19:31,  3.15it/s]
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  2%|▏         | 937/50000 [05:00<4:20:10,  3.14it/s]
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  2%|▏         | 1113/50000 [07:36<4:17:06,  3.17it/s]
  2%|▏         | 1114/50000 [07:36<4:17:19,  3.17it/s]
  2%|▏         | 1115/50000 [07:37<4:17:17,  3.17it/s]
  2%|▏         | 1116/50000 [07:37<4:17:23,  3.17it/s]
  2%|▏         | 1117/50000 [07:37<4:17:25,  3.16it/s]
  2%|▏         | 1118/50000 [07:38<4:17:11,  3.17it/s]
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  2%|▏         | 1120/50000 [07:38<4:17:21,  3.17it/s]
  2%|▏         | 1121/50000 [07:39<4:17:21,  3.17it/s]
  2%|▏         | 1122/50000 [07:39<4:17:19,  3.17it/s]
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  2%|▏         | 1124/50000 [07:40<4:17:33,  3.16it/s]
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  2%|▏         | 1126/50000 [07:40<4:18:17,  3.15it/s]
  2%|▏         | 1127/50000 [07:41<4:18:01,  3.16it/s]
  2%|▏         | 1128/50000 [07:41<4:17:54,  3.16it/s]
  2%|▏         | 1129/50000 [07:41<4:18:43,  3.15it/s]
  2%|▏         | 1130/50000 [07:42<4:17:48,  3.16it/s]
  2%|▏         | 1131/50000 [07:42<4:17:44,  3.16it/s]
  2%|▏         | 1132/50000 [07:42<4:17:26,  3.16it/s]
  2%|▏         | 1133/50000 [07:42<4:17:05,  3.17it/s]
  2%|▏         | 1134/50000 [07:43<4:17:15,  3.17it/s]
  2%|▏         | 1135/50000 [07:43<4:17:19,  3.16it/s]
  2%|▏         | 1136/50000 [07:43<4:17:27,  3.16it/s]
  2%|▏         | 1137/50000 [07:44<4:17:22,  3.16it/s]
  2%|▏         | 1138/50000 [07:44<4:17:32,  3.16it/s]
  2%|▏         | 1139/50000 [07:44<4:17:16,  3.17it/s]
  2%|▏         | 1140/50000 [07:45<4:17:10,  3.17it/s]
  2%|▏         | 1141/50000 [07:45<4:17:08,  3.17it/s]
  2%|▏         | 1142/50000 [07:45<4:18:19,  3.15it/s]
  2%|▏         | 1143/50000 [07:46<4:18:30,  3.15it/s]
  2%|▏         | 1144/50000 [07:46<4:17:14,  3.17it/s]
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  2%|▏         | 1146/50000 [07:47<4:17:12,  3.17it/s]
  2%|▏         | 1147/50000 [07:47<4:17:08,  3.17it/s]
  2%|▏         | 1148/50000 [07:47<4:17:21,  3.16it/s]
  2%|▏         | 1149/50000 [07:48<4:17:03,  3.17it/s]
  2%|▏         | 1150/50000 [07:48<4:17:04,  3.17it/s]
  2%|▏         | 1151/50000 [07:48<4:16:58,  3.17it/s]
  2%|▏         | 1152/50000 [07:48<4:16:53,  3.17it/s]
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  2%|▏         | 1154/50000 [07:49<4:17:55,  3.16it/s]
  2%|▏         | 1155/50000 [07:49<4:18:45,  3.15it/s]
  2%|▏         | 1156/50000 [07:50<4:16:46,  3.17it/s]
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  2%|▏         | 1158/50000 [07:50<4:18:28,  3.15it/s]
  2%|▏         | 1159/50000 [07:51<4:17:58,  3.16it/s]
  2%|▏         | 1160/50000 [07:51<4:17:25,  3.16it/s]
  2%|▏         | 1161/50000 [07:51<4:18:05,  3.15it/s]
  2%|▏         | 1162/50000 [07:52<4:17:52,  3.16it/s]
  2%|▏         | 1163/50000 [07:52<4:17:35,  3.16it/s]
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  2%|▏         | 1165/50000 [07:53<4:17:43,  3.16it/s]
  2%|▏         | 1166/50000 [07:53<4:17:39,  3.16it/s]
  2%|▏         | 1167/50000 [07:53<4:17:27,  3.16it/s]
  2%|▏         | 1168/50000 [07:54<4:19:11,  3.14it/s]
  2%|▏         | 1169/50000 [07:54<4:17:05,  3.17it/s]
  2%|▏         | 1170/50000 [07:54<4:18:35,  3.15it/s]
  2%|▏         | 1171/50000 [07:54<4:16:30,  3.17it/s]
  2%|▏         | 1172/50000 [07:55<4:16:29,  3.17it/s]
  2%|▏         | 1173/50000 [07:55<4:16:35,  3.17it/s]
  2%|▏         | 1174/50000 [07:55<4:16:58,  3.17it/s]
  2%|▏         | 1175/50000 [07:56<4:16:43,  3.17it/s]
  2%|▏         | 1176/50000 [07:56<4:16:56,  3.17it/s]
  2%|▏         | 1177/50000 [07:56<4:17:02,  3.17it/s]
  2%|▏         | 1178/50000 [07:57<4:17:02,  3.17it/s]
  2%|▏         | 1179/50000 [07:57<4:16:59,  3.17it/s]
  2%|▏         | 1180/50000 [07:57<4:16:50,  3.17it/s]
  2%|▏         | 1181/50000 [07:58<4:16:40,  3.17it/s]
  2%|▏         | 1182/50000 [07:58<4:16:38,  3.17it/s]
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  2%|▏         | 1184/50000 [07:59<4:17:02,  3.17it/s]
  2%|▏         | 1185/50000 [07:59<4:16:50,  3.17it/s]
  2%|▏         | 1186/50000 [07:59<4:16:48,  3.17it/s]
  2%|▏         | 1187/50000 [08:00<4:16:49,  3.17it/s]
  2%|▏         | 1188/50000 [08:00<4:16:40,  3.17it/s]
  2%|▏         | 1189/50000 [08:00<4:16:35,  3.17it/s]
  2%|▏         | 1190/50000 [08:00<4:16:32,  3.17it/s]
  2%|▏         | 1191/50000 [08:01<4:16:36,  3.17it/s]
  2%|▏         | 1192/50000 [08:01<4:16:40,  3.17it/s]
  2%|▏         | 1193/50000 [08:01<4:16:54,  3.17it/s]
  2%|▏         | 1194/50000 [08:02<4:16:37,  3.17it/s]
  2%|▏         | 1195/50000 [08:02<4:17:12,  3.16it/s]
  2%|▏         | 1196/50000 [08:02<4:16:45,  3.17it/s]
  2%|▏         | 1197/50000 [08:03<4:16:26,  3.17it/s]
  2%|▏         | 1198/50000 [08:03<4:16:42,  3.17it/s]
  2%|▏         | 1199/50000 [08:03<4:18:52,  3.14it/s]
  2%|▏         | 1200/50000 [08:04<4:16:25,  3.17it/s]
  2%|▏         | 1201/50000 [08:04<4:16:11,  3.17it/s]
  2%|▏         | 1202/50000 [08:04<4:16:09,  3.17it/s]
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  2%|▏         | 1206/50000 [08:06<4:18:08,  3.15it/s]
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  2%|▏         | 1209/50000 [08:06<4:16:56,  3.16it/s]
  2%|▏         | 1210/50000 [08:07<4:16:11,  3.17it/s]
  2%|▏         | 1211/50000 [08:07<4:16:15,  3.17it/s]
  2%|▏         | 1212/50000 [08:07<4:16:20,  3.17it/s]
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  2%|▏         | 1222/50000 [08:11<4:16:26,  3.17it/s]
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  2%|▏         | 1224/50000 [08:11<4:18:39,  3.14it/s]
  2%|▏         | 1225/50000 [08:12<4:16:00,  3.18it/s]
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  2%|▏         | 1227/50000 [08:12<4:16:18,  3.17it/s]
  2%|▏         | 1228/50000 [08:12<4:16:12,  3.17it/s]
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  2%|▏         | 1231/50000 [08:13<4:16:29,  3.17it/s]
  2%|▏         | 1232/50000 [08:14<4:16:30,  3.17it/s]
  2%|▏         | 1233/50000 [08:14<4:16:45,  3.17it/s]
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  2%|▏         | 1235/50000 [08:15<4:16:37,  3.17it/s]
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  2%|▎         | 1250/50000 [08:19<4:15:47,  3.18it/s]
  3%|▎         | 1251/50000 [08:20<4:16:04,  3.17it/s]
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  3%|▎         | 1257/50000 [08:22<4:19:14,  3.13it/s]
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  3%|▎         | 1260/50000 [08:23<4:15:59,  3.17it/s]
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  4%|▍         | 2000/50000 [12:18<4:20:21,  3.07it/s]{'eval_loss': 1.2391728162765503, 'eval_accuracy': 0.4542, 'eval_f1': 0.43426746474182226, 'eval_precision': 0.4369865342622743, 'eval_recall': 0.4542, 'eval_class_0_precision': 0.6313559322033898, 'eval_class_0_recall': 0.596, 'eval_class_0_f1': 0.6131687242798354, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.38716148445336007, 'eval_class_1_recall': 0.386, 'eval_class_1_f1': 0.38657986980470704, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.36332574031890663, 'eval_class_2_recall': 0.319, 'eval_class_2_f1': 0.3397231096911608, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.3120567375886525, 'eval_class_3_recall': 0.176, 'eval_class_3_f1': 0.22506393861892582, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.49103277674706247, 'eval_class_4_recall': 0.794, 'eval_class_4_f1': 0.6068016813144822, 'eval_class_4_support': 1000.0, 'eval_runtime': 97.6842, 'eval_samples_per_second': 51.185, 'eval_steps_per_second': 12.796, 'epoch': 0.02}
{'loss': 1.2292, 'grad_norm': 6.351487636566162, 'learning_rate': 1.92004e-05, 'epoch': 0.04}


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  6%|▌         | 3000/50000 [19:19<4:08:37,  3.15it/s]{'eval_loss': 1.204885482788086, 'eval_accuracy': 0.4556, 'eval_f1': 0.430216868920478, 'eval_precision': 0.49834060779419953, 'eval_recall': 0.4556, 'eval_class_0_precision': 0.7545638945233266, 'eval_class_0_recall': 0.372, 'eval_class_0_f1': 0.4983255190890824, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.3691340106434446, 'eval_class_1_recall': 0.763, 'eval_class_1_f1': 0.49755461362895337, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4448669201520912, 'eval_class_2_recall': 0.117, 'eval_class_2_f1': 0.18527315914489312, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.3562753036437247, 'eval_class_3_recall': 0.352, 'eval_class_3_f1': 0.35412474849094566, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.5668629100084104, 'eval_class_4_recall': 0.674, 'eval_class_4_f1': 0.6158063042485153, 'eval_class_4_support': 1000.0, 'eval_runtime': 97.5071, 'eval_samples_per_second': 51.278, 'eval_steps_per_second': 12.82, 'epoch': 0.04}
{'loss': 1.1638, 'grad_norm': 12.078287124633789, 'learning_rate': 1.88004e-05, 'epoch': 0.06}


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  7%|▋         | 3260/50000 [22:20<4:06:35,  3.16it/s]
  7%|▋         | 3261/50000 [22:21<4:07:06,  3.15it/s]
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  7%|▋         | 3263/50000 [22:21<4:06:11,  3.16it/s]
  7%|▋         | 3264/50000 [22:22<4:06:55,  3.15it/s]
  7%|▋         | 3265/50000 [22:22<4:10:31,  3.11it/s]
  7%|▋         | 3266/50000 [22:22<4:04:50,  3.18it/s]
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  7%|▋         | 3276/50000 [22:25<4:06:12,  3.16it/s]
  7%|▋         | 3277/50000 [22:26<4:06:38,  3.16it/s]
  7%|▋         | 3278/50000 [22:26<4:06:23,  3.16it/s]
  7%|▋         | 3279/50000 [22:26<4:06:25,  3.16it/s]
  7%|▋         | 3280/50000 [22:27<4:06:40,  3.16it/s]
  7%|▋         | 3281/50000 [22:27<4:06:29,  3.16it/s]
  7%|▋         | 3282/50000 [22:27<4:06:20,  3.16it/s]
  7%|▋         | 3283/50000 [22:28<4:06:27,  3.16it/s]
  7%|▋         | 3284/50000 [22:28<4:06:16,  3.16it/s]
  7%|▋         | 3285/50000 [22:28<4:06:26,  3.16it/s]
  7%|▋         | 3286/50000 [22:29<4:05:38,  3.17it/s]
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  7%|▋         | 3312/50000 [22:37<4:05:52,  3.16it/s]
  7%|▋         | 3313/50000 [22:37<4:05:39,  3.17it/s]
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  7%|▋         | 3493/50000 [23:34<4:04:21,  3.17it/s]
  7%|▋         | 3494/50000 [23:34<4:04:05,  3.18it/s]
  7%|▋         | 3495/50000 [23:35<4:04:09,  3.17it/s]
  7%|▋         | 3496/50000 [23:35<4:04:22,  3.17it/s]
  7%|▋         | 3497/50000 [23:35<4:04:22,  3.17it/s]
  7%|▋         | 3498/50000 [23:36<4:04:41,  3.17it/s]
  7%|▋         | 3499/50000 [23:36<4:04:23,  3.17it/s]
  7%|▋         | 3500/50000 [23:36<4:04:19,  3.17it/s]
  7%|▋         | 3501/50000 [23:37<4:04:29,  3.17it/s]
  7%|▋         | 3502/50000 [23:37<4:04:33,  3.17it/s]
  7%|▋         | 3503/50000 [23:37<4:04:38,  3.17it/s]
  7%|▋         | 3504/50000 [23:37<4:04:34,  3.17it/s]
  7%|▋         | 3505/50000 [23:38<4:04:48,  3.17it/s]
  7%|▋         | 3506/50000 [23:38<4:04:43,  3.17it/s]
  7%|▋         | 3507/50000 [23:38<4:05:00,  3.16it/s]
  7%|▋         | 3508/50000 [23:39<4:05:13,  3.16it/s]
  7%|▋         | 3509/50000 [23:39<4:05:03,  3.16it/s]
  7%|▋         | 3510/50000 [23:39<4:05:10,  3.16it/s]
  7%|▋         | 3511/50000 [23:40<4:05:09,  3.16it/s]
  7%|▋         | 3512/50000 [23:40<4:05:48,  3.15it/s]
  7%|▋         | 3513/50000 [23:40<4:04:51,  3.16it/s]
  7%|▋         | 3514/50000 [23:41<4:04:46,  3.17it/s]
  7%|▋         | 3515/50000 [23:41<4:05:00,  3.16it/s]
  7%|▋         | 3516/50000 [23:41<4:04:22,  3.17it/s]
  7%|▋         | 3517/50000 [23:42<4:04:43,  3.17it/s]
  7%|▋         | 3518/50000 [23:42<4:06:41,  3.14it/s]
  7%|▋         | 3519/50000 [23:42<4:05:46,  3.15it/s]
  7%|▋         | 3520/50000 [23:43<4:05:53,  3.15it/s]
  7%|▋         | 3521/50000 [23:43<4:06:03,  3.15it/s]
  7%|▋         | 3522/50000 [23:43<4:05:34,  3.15it/s]
  7%|▋         | 3523/50000 [23:43<4:05:47,  3.15it/s]
  7%|▋         | 3524/50000 [23:44<4:07:34,  3.13it/s]
  7%|▋         | 3525/50000 [23:44<4:06:47,  3.14it/s]
  7%|▋         | 3526/50000 [23:44<4:06:55,  3.14it/s]
  7%|▋         | 3527/50000 [23:45<4:06:20,  3.14it/s]
  7%|▋         | 3528/50000 [23:45<4:05:40,  3.15it/s]
  7%|▋         | 3529/50000 [23:45<4:05:34,  3.15it/s]
  7%|▋         | 3530/50000 [23:46<4:06:07,  3.15it/s]
  7%|▋         | 3531/50000 [23:46<4:06:15,  3.15it/s]
  7%|▋         | 3532/50000 [23:46<4:05:14,  3.16it/s]
  7%|▋         | 3533/50000 [23:47<4:05:00,  3.16it/s]
  7%|▋         | 3534/50000 [23:47<4:05:08,  3.16it/s]
  7%|▋         | 3535/50000 [23:47<4:05:25,  3.16it/s]
  7%|▋         | 3536/50000 [23:48<4:06:06,  3.15it/s]
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  7%|▋         | 3538/50000 [23:48<4:05:26,  3.15it/s]
  7%|▋         | 3539/50000 [23:49<4:05:04,  3.16it/s]
  7%|▋         | 3540/50000 [23:49<4:04:59,  3.16it/s]
  7%|▋         | 3541/50000 [23:49<4:04:26,  3.17it/s]
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  7%|▋         | 3546/50000 [23:51<4:03:57,  3.17it/s]
  7%|▋         | 3547/50000 [23:51<4:04:40,  3.16it/s]
  7%|▋         | 3548/50000 [23:51<4:04:34,  3.17it/s]
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  7%|▋         | 3550/50000 [23:52<4:04:19,  3.17it/s]
  7%|▋         | 3551/50000 [23:52<4:04:04,  3.17it/s]
  7%|▋         | 3552/50000 [23:53<4:04:05,  3.17it/s]
  7%|▋         | 3553/50000 [23:53<4:04:16,  3.17it/s]
  7%|▋         | 3554/50000 [23:53<4:04:02,  3.17it/s]
  7%|▋         | 3555/50000 [23:54<4:04:28,  3.17it/s]
  7%|▋         | 3556/50000 [23:54<4:04:16,  3.17it/s]
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  7%|▋         | 3558/50000 [23:55<4:04:17,  3.17it/s]
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  7%|▋         | 3560/50000 [23:55<4:04:18,  3.17it/s]
  7%|▋         | 3561/50000 [23:56<4:04:14,  3.17it/s]
  7%|▋         | 3562/50000 [23:56<4:04:14,  3.17it/s]
  7%|▋         | 3563/50000 [23:56<4:04:11,  3.17it/s]
  7%|▋         | 3564/50000 [23:56<4:04:04,  3.17it/s]
  7%|▋         | 3565/50000 [23:57<4:04:23,  3.17it/s]
  7%|▋         | 3566/50000 [23:57<4:04:12,  3.17it/s]
  7%|▋         | 3567/50000 [23:57<4:04:13,  3.17it/s]
  7%|▋         | 3568/50000 [23:58<4:03:50,  3.17it/s]
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  7%|▋         | 3571/50000 [23:59<4:03:35,  3.18it/s]
  7%|▋         | 3572/50000 [23:59<4:03:38,  3.18it/s]
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  7%|▋         | 3575/50000 [24:00<4:03:44,  3.17it/s]
  7%|▋         | 3576/50000 [24:00<4:03:38,  3.18it/s]
  7%|▋         | 3577/50000 [24:01<4:03:25,  3.18it/s]
  7%|▋         | 3578/50000 [24:01<4:03:25,  3.18it/s]
  7%|▋         | 3579/50000 [24:01<4:03:29,  3.18it/s]
  7%|▋         | 3580/50000 [24:01<4:03:39,  3.18it/s]
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  7%|▋         | 3613/50000 [24:12<4:03:28,  3.18it/s]
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  7%|▋         | 3619/50000 [24:14<4:03:20,  3.18it/s]
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  8%|▊         | 4000/50000 [26:14<4:02:38,  3.16it/s]{'eval_loss': 1.1764825582504272, 'eval_accuracy': 0.4828, 'eval_f1': 0.4656604551922082, 'eval_precision': 0.48193164201083327, 'eval_recall': 0.4828, 'eval_class_0_precision': 0.6304780876494024, 'eval_class_0_recall': 0.633, 'eval_class_0_f1': 0.6317365269461078, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4002026342451874, 'eval_class_1_recall': 0.395, 'eval_class_1_f1': 0.39758429793658784, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.3686411149825784, 'eval_class_2_recall': 0.529, 'eval_class_2_f1': 0.4344969199178645, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.43169398907103823, 'eval_class_3_recall': 0.158, 'eval_class_3_f1': 0.23133235724743778, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.5786423841059603, 'eval_class_4_recall': 0.699, 'eval_class_4_f1': 0.6331521739130435, 'eval_class_4_support': 1000.0, 'eval_runtime': 96.4392, 'eval_samples_per_second': 51.846, 'eval_steps_per_second': 12.962, 'epoch': 0.06}
{'loss': 1.1516, 'grad_norm': 7.7430419921875, 'learning_rate': 1.84004e-05, 'epoch': 0.08}


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  8%|▊         | 4091/50000 [28:21<4:01:32,  3.17it/s]
  8%|▊         | 4092/50000 [28:22<4:01:26,  3.17it/s]
  8%|▊         | 4093/50000 [28:22<4:05:58,  3.11it/s]
  8%|▊         | 4094/50000 [28:22<4:00:49,  3.18it/s]
  8%|▊         | 4095/50000 [28:22<4:00:54,  3.18it/s]
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  8%|▊         | 4110/50000 [28:27<4:00:51,  3.18it/s]
  8%|▊         | 4111/50000 [28:28<4:01:12,  3.17it/s]
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  8%|▊         | 4116/50000 [28:29<4:05:46,  3.11it/s]
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 10%|█         | 5000/50000 [33:13<3:58:20,  3.15it/s]{'eval_loss': 1.1434950828552246, 'eval_accuracy': 0.4986, 'eval_f1': 0.49820695362201467, 'eval_precision': 0.5006116264973847, 'eval_recall': 0.4986, 'eval_class_0_precision': 0.6367041198501873, 'eval_class_0_recall': 0.68, 'eval_class_0_f1': 0.6576402321083172, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.43559718969555034, 'eval_class_1_recall': 0.372, 'eval_class_1_f1': 0.40129449838187703, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.3858603066439523, 'eval_class_2_recall': 0.453, 'eval_class_2_f1': 0.41674333026678934, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4203045685279188, 'eval_class_3_recall': 0.414, 'eval_class_3_f1': 0.4171284634760705, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6245919477693145, 'eval_class_4_recall': 0.574, 'eval_class_4_f1': 0.5982282438770192, 'eval_class_4_support': 1000.0, 'eval_runtime': 96.1606, 'eval_samples_per_second': 51.996, 'eval_steps_per_second': 12.999, 'epoch': 0.08}
{'loss': 1.1583, 'grad_norm': 9.508795738220215, 'learning_rate': 1.80004e-05, 'epoch': 0.1}


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 12%|█▏        | 6000/50000 [40:11<3:59:20,  3.06it/s]{'eval_loss': 1.1880158185958862, 'eval_accuracy': 0.478, 'eval_f1': 0.46763965291372434, 'eval_precision': 0.5071116053419875, 'eval_recall': 0.478, 'eval_class_0_precision': 0.7040816326530612, 'eval_class_0_recall': 0.552, 'eval_class_0_f1': 0.6188340807174888, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.40083217753120665, 'eval_class_1_recall': 0.578, 'eval_class_1_f1': 0.47338247338247336, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.34219269102990035, 'eval_class_2_recall': 0.515, 'eval_class_2_f1': 0.4111776447105788, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4588607594936709, 'eval_class_3_recall': 0.145, 'eval_class_3_f1': 0.22036474164133737, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6295907660020986, 'eval_class_4_recall': 0.6, 'eval_class_4_f1': 0.6144393241167435, 'eval_class_4_support': 1000.0, 'eval_runtime': 95.453, 'eval_samples_per_second': 52.382, 'eval_steps_per_second': 13.095, 'epoch': 0.1}
{'loss': 1.1384, 'grad_norm': 19.316335678100586, 'learning_rate': 1.7600400000000002e-05, 'epoch': 0.12}


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 14%|█▍        | 7000/50000 [47:08<3:48:03,  3.14it/s]{'eval_loss': 1.136484146118164, 'eval_accuracy': 0.5012, 'eval_f1': 0.5010238877624797, 'eval_precision': 0.5235876615571663, 'eval_recall': 0.5012, 'eval_class_0_precision': 0.6461684011352885, 'eval_class_0_recall': 0.683, 'eval_class_0_f1': 0.664073894020418, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.40835266821345706, 'eval_class_1_recall': 0.528, 'eval_class_1_f1': 0.46053205407762754, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.42159090909090907, 'eval_class_2_recall': 0.371, 'eval_class_2_f1': 0.3946808510638298, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4361525704809287, 'eval_class_3_recall': 0.526, 'eval_class_3_f1': 0.4768812330009066, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.7056737588652482, 'eval_class_4_recall': 0.398, 'eval_class_4_f1': 0.5089514066496164, 'eval_class_4_support': 1000.0, 'eval_runtime': 96.1654, 'eval_samples_per_second': 51.994, 'eval_steps_per_second': 12.998, 'epoch': 0.12}
{'loss': 1.0855, 'grad_norm': 14.321106910705566, 'learning_rate': 1.7200400000000002e-05, 'epoch': 0.14}


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 16%|█▌        | 8000/50000 [54:10<3:42:12,  3.15it/s]{'eval_loss': 1.0957872867584229, 'eval_accuracy': 0.512, 'eval_f1': 0.5060544889717916, 'eval_precision': 0.5043526744518689, 'eval_recall': 0.512, 'eval_class_0_precision': 0.6279863481228669, 'eval_class_0_recall': 0.736, 'eval_class_0_f1': 0.6777163904235728, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.44543147208121825, 'eval_class_1_recall': 0.351, 'eval_class_1_f1': 0.3926174496644295, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.3971153846153846, 'eval_class_2_recall': 0.413, 'eval_class_2_f1': 0.40490196078431373, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4323189926547744, 'eval_class_3_recall': 0.412, 'eval_class_3_f1': 0.42191500256016384, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6189111747851003, 'eval_class_4_recall': 0.648, 'eval_class_4_f1': 0.6331216414264778, 'eval_class_4_support': 1000.0, 'eval_runtime': 96.676, 'eval_samples_per_second': 51.719, 'eval_steps_per_second': 12.93, 'epoch': 0.14}
{'loss': 1.0814, 'grad_norm': 19.342615127563477, 'learning_rate': 1.6800400000000003e-05, 'epoch': 0.16}


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 18%|█▊        | 9000/50000 [1:01:03<3:35:33,  3.17it/s]{'eval_loss': 1.1257401704788208, 'eval_accuracy': 0.5168, 'eval_f1': 0.5079763503972896, 'eval_precision': 0.5068954256294005, 'eval_recall': 0.5168, 'eval_class_0_precision': 0.6063912704598597, 'eval_class_0_recall': 0.778, 'eval_class_0_f1': 0.6815593517301796, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4218436873747495, 'eval_class_1_recall': 0.421, 'eval_class_1_f1': 0.4214214214214214, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.39672131147540984, 'eval_class_2_recall': 0.363, 'eval_class_2_f1': 0.37911227154046995, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.479002624671916, 'eval_class_3_recall': 0.365, 'eval_class_3_f1': 0.41430192962542567, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6305182341650671, 'eval_class_4_recall': 0.657, 'eval_class_4_f1': 0.643486777668952, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.901, 'eval_samples_per_second': 52.686, 'eval_steps_per_second': 13.172, 'epoch': 0.16}
{'loss': 1.1099, 'grad_norm': 12.229375839233398, 'learning_rate': 1.64004e-05, 'epoch': 0.18}


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100%|█████████▉| 1248/1250 [01:30<00:00, 13.74it/s]

100%|██████████| 1250/1250 [01:31<00:00, 13.77it/s]
                                                        

                                                   

 18%|█▊        | 9000/50000 [1:02:38<3:35:33,  3.17it/s]

100%|██████████| 1250/1250 [01:34<00:00, 13.77it/s]

                                                   
 18%|█▊        | 9001/50000 [1:02:40<333:02:54, 29.24s/it]
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 20%|██        | 10000/50000 [1:07:55<3:30:10,  3.17it/s]{'eval_loss': 1.1368818283081055, 'eval_accuracy': 0.519, 'eval_f1': 0.512182529138641, 'eval_precision': 0.5154804013903789, 'eval_recall': 0.519, 'eval_class_0_precision': 0.6254325259515571, 'eval_class_0_recall': 0.723, 'eval_class_0_f1': 0.6706864564007421, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4171249018067557, 'eval_class_1_recall': 0.531, 'eval_class_1_f1': 0.4672239331280246, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4311377245508982, 'eval_class_2_recall': 0.288, 'eval_class_2_f1': 0.34532374100719426, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4663101604278075, 'eval_class_3_recall': 0.436, 'eval_class_3_f1': 0.4506459948320413, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.637396694214876, 'eval_class_4_recall': 0.617, 'eval_class_4_f1': 0.6270325203252033, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.4678, 'eval_samples_per_second': 52.928, 'eval_steps_per_second': 13.232, 'epoch': 0.18}
{'loss': 1.1047, 'grad_norm': 14.036566734313965, 'learning_rate': 1.60004e-05, 'epoch': 0.2}


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 22%|██▏       | 11000/50000 [1:14:49<3:24:24,  3.18it/s]{'eval_loss': 1.1010087728500366, 'eval_accuracy': 0.5118, 'eval_f1': 0.5117673058187904, 'eval_precision': 0.5291774356885353, 'eval_recall': 0.5118, 'eval_class_0_precision': 0.6791666666666667, 'eval_class_0_recall': 0.652, 'eval_class_0_f1': 0.6653061224489796, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.45038167938931295, 'eval_class_1_recall': 0.295, 'eval_class_1_f1': 0.3564954682779456, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.39185393258426965, 'eval_class_2_recall': 0.558, 'eval_class_2_f1': 0.4603960396039604, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.441151566469094, 'eval_class_3_recall': 0.521, 'eval_class_3_f1': 0.4777624942686841, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6833333333333333, 'eval_class_4_recall': 0.533, 'eval_class_4_f1': 0.5988764044943821, 'eval_class_4_support': 1000.0, 'eval_runtime': 97.8623, 'eval_samples_per_second': 51.092, 'eval_steps_per_second': 12.773, 'epoch': 0.2}
{'loss': 1.0877, 'grad_norm': 12.954442977905273, 'learning_rate': 1.56004e-05, 'epoch': 0.22}


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 24%|██▍       | 12000/50000 [1:21:39<3:18:45,  3.19it/s]{'eval_loss': 1.0899925231933594, 'eval_accuracy': 0.5254, 'eval_f1': 0.5232779432213686, 'eval_precision': 0.5237556976621598, 'eval_recall': 0.5254, 'eval_class_0_precision': 0.6689520078354554, 'eval_class_0_recall': 0.683, 'eval_class_0_f1': 0.6759030183077684, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4388794567062818, 'eval_class_1_recall': 0.517, 'eval_class_1_f1': 0.47474747474747475, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.42907801418439717, 'eval_class_2_recall': 0.363, 'eval_class_2_f1': 0.39328277356446373, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.45686900958466453, 'eval_class_3_recall': 0.429, 'eval_class_3_f1': 0.4424961320268179, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.625, 'eval_class_4_recall': 0.635, 'eval_class_4_f1': 0.6299603174603174, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.062, 'eval_samples_per_second': 53.156, 'eval_steps_per_second': 13.289, 'epoch': 0.22}
{'loss': 1.0803, 'grad_norm': 7.797364234924316, 'learning_rate': 1.5200400000000001e-05, 'epoch': 0.24}


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 26%|██▌       | 13000/50000 [1:28:29<3:13:26,  3.19it/s]{'eval_loss': 1.107537031173706, 'eval_accuracy': 0.523, 'eval_f1': 0.5121374631043359, 'eval_precision': 0.5161598950780283, 'eval_recall': 0.523, 'eval_class_0_precision': 0.5719225449515906, 'eval_class_0_recall': 0.827, 'eval_class_0_f1': 0.6762060506950123, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4498567335243553, 'eval_class_1_recall': 0.314, 'eval_class_1_f1': 0.36984687868080096, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4287200832466181, 'eval_class_2_recall': 0.412, 'eval_class_2_f1': 0.4201937786843447, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4688128772635815, 'eval_class_3_recall': 0.466, 'eval_class_3_f1': 0.4674022066198596, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6614872364039955, 'eval_class_4_recall': 0.596, 'eval_class_4_f1': 0.6270384008416623, 'eval_class_4_support': 1000.0, 'eval_runtime': 93.8788, 'eval_samples_per_second': 53.26, 'eval_steps_per_second': 13.315, 'epoch': 0.24}
{'loss': 1.0799, 'grad_norm': 7.481654644012451, 'learning_rate': 1.4800400000000001e-05, 'epoch': 0.26}


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 28%|██▊       | 14000/50000 [1:35:20<3:08:15,  3.19it/s]{'eval_loss': 1.0668607950210571, 'eval_accuracy': 0.5248, 'eval_f1': 0.5271243261632222, 'eval_precision': 0.5374817064795958, 'eval_recall': 0.5248, 'eval_class_0_precision': 0.7387387387387387, 'eval_class_0_recall': 0.574, 'eval_class_0_f1': 0.6460326392796849, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4368029739776952, 'eval_class_1_recall': 0.47, 'eval_class_1_f1': 0.4527938342967245, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.40960912052117265, 'eval_class_2_recall': 0.503, 'eval_class_2_f1': 0.45152603231597843, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4713603818615752, 'eval_class_3_recall': 0.395, 'eval_class_3_f1': 0.42981501632208924, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6308973172987974, 'eval_class_4_recall': 0.682, 'eval_class_4_f1': 0.6554541086016338, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.7106, 'eval_samples_per_second': 52.792, 'eval_steps_per_second': 13.198, 'epoch': 0.26}
{'loss': 1.0634, 'grad_norm': 14.57588005065918, 'learning_rate': 1.44004e-05, 'epoch': 0.28}


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 30%|███       | 15000/50000 [1:42:12<3:03:38,  3.18it/s]{'eval_loss': 1.0939515829086304, 'eval_accuracy': 0.5194, 'eval_f1': 0.5187534831135134, 'eval_precision': 0.534149684933154, 'eval_recall': 0.5194, 'eval_class_0_precision': 0.6403127715030409, 'eval_class_0_recall': 0.737, 'eval_class_0_f1': 0.6852626685262668, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.43895619757688725, 'eval_class_1_recall': 0.471, 'eval_class_1_f1': 0.4544138929088278, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4149797570850202, 'eval_class_2_recall': 0.41, 'eval_class_2_f1': 0.4124748490945674, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4578005115089514, 'eval_class_3_recall': 0.537, 'eval_class_3_f1': 0.4942475839852738, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.71869918699187, 'eval_class_4_recall': 0.442, 'eval_class_4_f1': 0.5473684210526316, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.2495, 'eval_samples_per_second': 53.051, 'eval_steps_per_second': 13.263, 'epoch': 0.28}
{'loss': 1.0513, 'grad_norm': 15.063302993774414, 'learning_rate': 1.40004e-05, 'epoch': 0.3}


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 32%|███▏      | 16000/50000 [1:49:05<2:58:44,  3.17it/s]{'eval_loss': 1.0504347085952759, 'eval_accuracy': 0.5368, 'eval_f1': 0.5341680787562957, 'eval_precision': 0.5368742991352995, 'eval_recall': 0.5368, 'eval_class_0_precision': 0.6835699797160243, 'eval_class_0_recall': 0.674, 'eval_class_0_f1': 0.6787512588116817, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.438871473354232, 'eval_class_1_recall': 0.56, 'eval_class_1_f1': 0.492091388400703, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.43530834340991537, 'eval_class_2_recall': 0.36, 'eval_class_2_f1': 0.39408866995073893, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4833524684270953, 'eval_class_3_recall': 0.421, 'eval_class_3_f1': 0.450026723677178, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6432692307692308, 'eval_class_4_recall': 0.669, 'eval_class_4_f1': 0.6558823529411765, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.6453, 'eval_samples_per_second': 52.829, 'eval_steps_per_second': 13.207, 'epoch': 0.3}
{'loss': 1.0771, 'grad_norm': 6.2468767166137695, 'learning_rate': 1.3600400000000001e-05, 'epoch': 0.32}


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 34%|███▍      | 17000/50000 [1:55:56<2:52:54,  3.18it/s]{'eval_loss': 1.0793464183807373, 'eval_accuracy': 0.5276, 'eval_f1': 0.5338764002040923, 'eval_precision': 0.5472452998866002, 'eval_recall': 0.5276, 'eval_class_0_precision': 0.7397959183673469, 'eval_class_0_recall': 0.58, 'eval_class_0_f1': 0.6502242152466368, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4307692307692308, 'eval_class_1_recall': 0.504, 'eval_class_1_f1': 0.4645161290322581, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4127659574468085, 'eval_class_2_recall': 0.485, 'eval_class_2_f1': 0.4459770114942529, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4760935910478128, 'eval_class_3_recall': 0.468, 'eval_class_3_f1': 0.4720121028744327, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6768018018018018, 'eval_class_4_recall': 0.601, 'eval_class_4_f1': 0.6366525423728814, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.5553, 'eval_samples_per_second': 52.879, 'eval_steps_per_second': 13.22, 'epoch': 0.32}
{'loss': 1.0742, 'grad_norm': 18.4812068939209, 'learning_rate': 1.3200400000000002e-05, 'epoch': 0.34}


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 36%|███▌      | 18000/50000 [2:02:47<2:47:47,  3.18it/s]{'eval_loss': 1.0901994705200195, 'eval_accuracy': 0.5276, 'eval_f1': 0.5172926041150948, 'eval_precision': 0.5219129576007325, 'eval_recall': 0.5276, 'eval_class_0_precision': 0.6155075939248601, 'eval_class_0_recall': 0.77, 'eval_class_0_f1': 0.6841403820524211, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.42824074074074076, 'eval_class_1_recall': 0.555, 'eval_class_1_f1': 0.4834494773519164, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4102902374670185, 'eval_class_2_recall': 0.311, 'eval_class_2_f1': 0.35381114903299204, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5028735632183908, 'eval_class_3_recall': 0.35, 'eval_class_3_f1': 0.41273584905660377, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6526526526526526, 'eval_class_4_recall': 0.652, 'eval_class_4_f1': 0.6523261630815408, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.3153, 'eval_samples_per_second': 53.014, 'eval_steps_per_second': 13.253, 'epoch': 0.34}
{'loss': 1.0462, 'grad_norm': 26.674646377563477, 'learning_rate': 1.2800400000000002e-05, 'epoch': 0.36}


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 38%|███▊      | 19000/50000 [2:09:43<2:43:20,  3.16it/s]{'eval_loss': 1.0500136613845825, 'eval_accuracy': 0.5342, 'eval_f1': 0.5278291892699142, 'eval_precision': 0.5322275073998034, 'eval_recall': 0.5342, 'eval_class_0_precision': 0.7008547008547008, 'eval_class_0_recall': 0.656, 'eval_class_0_f1': 0.6776859504132231, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.448, 'eval_class_1_recall': 0.504, 'eval_class_1_f1': 0.4743529411764706, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.416023166023166, 'eval_class_2_recall': 0.431, 'eval_class_2_f1': 0.42337917485265225, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4841628959276018, 'eval_class_3_recall': 0.321, 'eval_class_3_f1': 0.38604930847865304, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6120967741935484, 'eval_class_4_recall': 0.759, 'eval_class_4_f1': 0.6776785714285715, 'eval_class_4_support': 1000.0, 'eval_runtime': 96.7315, 'eval_samples_per_second': 51.689, 'eval_steps_per_second': 12.922, 'epoch': 0.36}
{'loss': 1.0601, 'grad_norm': 23.395845413208008, 'learning_rate': 1.24004e-05, 'epoch': 0.38}


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 40%|████      | 20000/50000 [2:16:36<2:37:45,  3.17it/s]{'eval_loss': 1.0666615962982178, 'eval_accuracy': 0.5342, 'eval_f1': 0.5297403359370441, 'eval_precision': 0.5278336747103917, 'eval_recall': 0.5342, 'eval_class_0_precision': 0.6474820143884892, 'eval_class_0_recall': 0.72, 'eval_class_0_f1': 0.6818181818181818, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4354066985645933, 'eval_class_1_recall': 0.455, 'eval_class_1_f1': 0.4449877750611247, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.42168674698795183, 'eval_class_2_recall': 0.385, 'eval_class_2_f1': 0.40250914793518033, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4912689173457509, 'eval_class_3_recall': 0.422, 'eval_class_3_f1': 0.45400753093060786, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6433239962651728, 'eval_class_4_recall': 0.689, 'eval_class_4_f1': 0.6653790439401256, 'eval_class_4_support': 1000.0, 'eval_runtime': 95.4415, 'eval_samples_per_second': 52.388, 'eval_steps_per_second': 13.097, 'epoch': 0.38}
{'loss': 1.0278, 'grad_norm': 15.671320915222168, 'learning_rate': 1.2000400000000001e-05, 'epoch': 0.4}


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 42%|████▏     | 21000/50000 [2:23:39<2:32:07,  3.18it/s]{'eval_loss': 1.0788010358810425, 'eval_accuracy': 0.535, 'eval_f1': 0.5303507377055685, 'eval_precision': 0.5297604600536172, 'eval_recall': 0.535, 'eval_class_0_precision': 0.6261151662611517, 'eval_class_0_recall': 0.772, 'eval_class_0_f1': 0.6914464845499328, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4335378323108384, 'eval_class_1_recall': 0.424, 'eval_class_1_f1': 0.4287158746208291, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4200626959247649, 'eval_class_2_recall': 0.402, 'eval_class_2_f1': 0.4108329075114972, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.49716231555051077, 'eval_class_3_recall': 0.438, 'eval_class_3_f1': 0.46570972886762363, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6719242902208202, 'eval_class_4_recall': 0.639, 'eval_class_4_f1': 0.65504869297796, 'eval_class_4_support': 1000.0, 'eval_runtime': 105.6652, 'eval_samples_per_second': 47.319, 'eval_steps_per_second': 11.83, 'epoch': 0.4}
{'loss': 1.0408, 'grad_norm': 22.769193649291992, 'learning_rate': 1.16004e-05, 'epoch': 0.42}


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 44%|████▍     | 22000/50000 [2:30:51<2:26:57,  3.18it/s]{'eval_loss': 1.0846076011657715, 'eval_accuracy': 0.5416, 'eval_f1': 0.535039467793784, 'eval_precision': 0.5510296953160877, 'eval_recall': 0.5416, 'eval_class_0_precision': 0.7355769230769231, 'eval_class_0_recall': 0.612, 'eval_class_0_f1': 0.6681222707423581, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4296028880866426, 'eval_class_1_recall': 0.595, 'eval_class_1_f1': 0.4989517819706499, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.43696581196581197, 'eval_class_2_recall': 0.409, 'eval_class_2_f1': 0.4225206611570248, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.535472972972973, 'eval_class_3_recall': 0.317, 'eval_class_3_f1': 0.39824120603015073, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6175298804780877, 'eval_class_4_recall': 0.775, 'eval_class_4_f1': 0.6873614190687362, 'eval_class_4_support': 1000.0, 'eval_runtime': 115.0848, 'eval_samples_per_second': 43.446, 'eval_steps_per_second': 10.862, 'epoch': 0.42}
{'loss': 1.033, 'grad_norm': 15.458209037780762, 'learning_rate': 1.12004e-05, 'epoch': 0.44}


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 46%|████▌     | 23000/50000 [2:37:43<2:21:59,  3.17it/s]{'eval_loss': 1.0639476776123047, 'eval_accuracy': 0.54, 'eval_f1': 0.5375952923077632, 'eval_precision': 0.5424392025316611, 'eval_recall': 0.54, 'eval_class_0_precision': 0.7014925373134329, 'eval_class_0_recall': 0.658, 'eval_class_0_f1': 0.6790505675954592, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4319571865443425, 'eval_class_1_recall': 0.565, 'eval_class_1_f1': 0.4896013864818024, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4369942196531792, 'eval_class_2_recall': 0.378, 'eval_class_2_f1': 0.4053619302949062, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4975124378109453, 'eval_class_3_recall': 0.4, 'eval_class_3_f1': 0.4434589800443459, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6442396313364055, 'eval_class_4_recall': 0.699, 'eval_class_4_f1': 0.6705035971223021, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.6792, 'eval_samples_per_second': 52.81, 'eval_steps_per_second': 13.202, 'epoch': 0.44}
{'loss': 1.0412, 'grad_norm': 5.6992878913879395, 'learning_rate': 1.0800399999999999e-05, 'epoch': 0.46}


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 48%|████▊     | 24000/50000 [2:44:48<2:16:13,  3.18it/s]{'eval_loss': 1.0435279607772827, 'eval_accuracy': 0.531, 'eval_f1': 0.5308573974386658, 'eval_precision': 0.5385261095867409, 'eval_recall': 0.531, 'eval_class_0_precision': 0.7298919567827131, 'eval_class_0_recall': 0.608, 'eval_class_0_f1': 0.6633933442444081, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.42065299924069854, 'eval_class_1_recall': 0.554, 'eval_class_1_f1': 0.478204574881312, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4206161137440758, 'eval_class_2_recall': 0.355, 'eval_class_2_f1': 0.38503253796095444, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4841875681570338, 'eval_class_3_recall': 0.444, 'eval_class_3_f1': 0.46322378716744916, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6372819100091828, 'eval_class_4_recall': 0.694, 'eval_class_4_f1': 0.6644327429392054, 'eval_class_4_support': 1000.0, 'eval_runtime': 108.0379, 'eval_samples_per_second': 46.28, 'eval_steps_per_second': 11.57, 'epoch': 0.46}
{'loss': 1.0316, 'grad_norm': 15.327022552490234, 'learning_rate': 1.0400400000000001e-05, 'epoch': 0.48}


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 50%|█████     | 25000/50000 [2:51:39<2:10:50,  3.18it/s]{'eval_loss': 1.0466254949569702, 'eval_accuracy': 0.5502, 'eval_f1': 0.5465589588574441, 'eval_precision': 0.5485106963050941, 'eval_recall': 0.5502, 'eval_class_0_precision': 0.6958290946083419, 'eval_class_0_recall': 0.684, 'eval_class_0_f1': 0.6898638426626323, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.44208809135399674, 'eval_class_1_recall': 0.542, 'eval_class_1_f1': 0.48697214734950584, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.45066991473812423, 'eval_class_2_recall': 0.37, 'eval_class_2_f1': 0.4063701263042284, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5064327485380117, 'eval_class_3_recall': 0.433, 'eval_class_3_f1': 0.4668463611859838, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6475336322869956, 'eval_class_4_recall': 0.722, 'eval_class_4_f1': 0.68274231678487, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.3392, 'eval_samples_per_second': 53.0, 'eval_steps_per_second': 13.25, 'epoch': 0.48}
{'loss': 1.0377, 'grad_norm': 19.91824722290039, 'learning_rate': 1.0000400000000002e-05, 'epoch': 0.5}


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 52%|█████▏    | 26000/50000 [2:58:31<2:05:56,  3.18it/s]{'eval_loss': 1.0404223203659058, 'eval_accuracy': 0.5444, 'eval_f1': 0.5454433349811023, 'eval_precision': 0.5475173678728207, 'eval_recall': 0.5444, 'eval_class_0_precision': 0.6801579466929911, 'eval_class_0_recall': 0.689, 'eval_class_0_f1': 0.6845504222553402, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46531483457844186, 'eval_class_1_recall': 0.436, 'eval_class_1_f1': 0.45018069179143005, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.43215613382899626, 'eval_class_2_recall': 0.465, 'eval_class_2_f1': 0.4479768786127168, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4838709677419355, 'eval_class_3_recall': 0.51, 'eval_class_3_f1': 0.49659201557935734, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6760869565217391, 'eval_class_4_recall': 0.622, 'eval_class_4_f1': 0.6479166666666667, 'eval_class_4_support': 1000.0, 'eval_runtime': 95.0006, 'eval_samples_per_second': 52.631, 'eval_steps_per_second': 13.158, 'epoch': 0.5}
{'loss': 1.0386, 'grad_norm': 11.934659004211426, 'learning_rate': 9.6004e-06, 'epoch': 0.52}


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 54%|█████▍    | 27000/50000 [3:05:22<2:00:15,  3.19it/s]{'eval_loss': 1.0606560707092285, 'eval_accuracy': 0.5476, 'eval_f1': 0.5361231663817525, 'eval_precision': 0.5404029509933457, 'eval_recall': 0.5476, 'eval_class_0_precision': 0.6257261410788382, 'eval_class_0_recall': 0.754, 'eval_class_0_f1': 0.6839002267573696, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46424870466321244, 'eval_class_1_recall': 0.448, 'eval_class_1_f1': 0.45597964376590333, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.435515873015873, 'eval_class_2_recall': 0.439, 'eval_class_2_f1': 0.43725099601593626, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.547107438016529, 'eval_class_3_recall': 0.331, 'eval_class_3_f1': 0.4124610591900312, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6294165981922761, 'eval_class_4_recall': 0.766, 'eval_class_4_f1': 0.6910239061795219, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.4847, 'eval_samples_per_second': 52.919, 'eval_steps_per_second': 13.23, 'epoch': 0.52}
{'loss': 1.0395, 'grad_norm': 10.79891300201416, 'learning_rate': 9.200400000000001e-06, 'epoch': 0.54}


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 56%|█████▌    | 28000/50000 [3:12:13<1:55:06,  3.19it/s]{'eval_loss': 1.039871096611023, 'eval_accuracy': 0.5412, 'eval_f1': 0.5419798331945144, 'eval_precision': 0.5440996125011924, 'eval_recall': 0.5412, 'eval_class_0_precision': 0.6824782187802517, 'eval_class_0_recall': 0.705, 'eval_class_0_f1': 0.6935563207083129, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.458955223880597, 'eval_class_1_recall': 0.492, 'eval_class_1_f1': 0.4749034749034749, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4328824141519251, 'eval_class_2_recall': 0.416, 'eval_class_2_f1': 0.4242733299337073, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.47464114832535886, 'eval_class_3_recall': 0.496, 'eval_class_3_f1': 0.48508557457212714, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.671541057367829, 'eval_class_4_recall': 0.597, 'eval_class_4_f1': 0.6320804658549497, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.5336, 'eval_samples_per_second': 52.891, 'eval_steps_per_second': 13.223, 'epoch': 0.54}
{'loss': 1.0319, 'grad_norm': 11.433354377746582, 'learning_rate': 8.800400000000001e-06, 'epoch': 0.56}


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 58%|█████▊    | 29000/50000 [3:19:04<1:50:04,  3.18it/s]{'eval_loss': 1.0162022113800049, 'eval_accuracy': 0.5488, 'eval_f1': 0.5470435862509262, 'eval_precision': 0.5464367884089357, 'eval_recall': 0.5488, 'eval_class_0_precision': 0.6866404715127702, 'eval_class_0_recall': 0.699, 'eval_class_0_f1': 0.6927651139742319, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4609297725024728, 'eval_class_1_recall': 0.466, 'eval_class_1_f1': 0.46345101939333666, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.436950146627566, 'eval_class_2_recall': 0.447, 'eval_class_2_f1': 0.44191794364804743, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5, 'eval_class_3_recall': 0.439, 'eval_class_3_f1': 0.46751863684771033, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6476635514018692, 'eval_class_4_recall': 0.693, 'eval_class_4_f1': 0.6695652173913044, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.3429, 'eval_samples_per_second': 52.998, 'eval_steps_per_second': 13.25, 'epoch': 0.56}
{'loss': 0.9901, 'grad_norm': 10.437809944152832, 'learning_rate': 8.4004e-06, 'epoch': 0.58}


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 60%|██████    | 30000/50000 [3:25:55<1:44:38,  3.19it/s]{'eval_loss': 1.0538381338119507, 'eval_accuracy': 0.554, 'eval_f1': 0.5452163211712885, 'eval_precision': 0.5495142478395124, 'eval_recall': 0.554, 'eval_class_0_precision': 0.6772983114446529, 'eval_class_0_recall': 0.722, 'eval_class_0_f1': 0.6989351403678606, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.44480257856567285, 'eval_class_1_recall': 0.552, 'eval_class_1_f1': 0.4926372155287818, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.45454545454545453, 'eval_class_2_recall': 0.355, 'eval_class_2_f1': 0.39865244244806286, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5426136363636364, 'eval_class_3_recall': 0.382, 'eval_class_3_f1': 0.44835680751173707, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6283112582781457, 'eval_class_4_recall': 0.759, 'eval_class_4_f1': 0.6875, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.3777, 'eval_samples_per_second': 52.979, 'eval_steps_per_second': 13.245, 'epoch': 0.58}
{'loss': 1.0321, 'grad_norm': 8.891352653503418, 'learning_rate': 8.0004e-06, 'epoch': 0.6}


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 62%|██████▏   | 31000/50000 [3:32:45<1:40:09,  3.16it/s]{'eval_loss': 1.0299631357192993, 'eval_accuracy': 0.5436, 'eval_f1': 0.5395471528699689, 'eval_precision': 0.538240014749832, 'eval_recall': 0.5436, 'eval_class_0_precision': 0.6411149825783972, 'eval_class_0_recall': 0.736, 'eval_class_0_f1': 0.6852886405959032, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.45366795366795365, 'eval_class_1_recall': 0.47, 'eval_class_1_f1': 0.46168958742632615, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4475374732334047, 'eval_class_2_recall': 0.418, 'eval_class_2_f1': 0.43226473629782836, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4994152046783626, 'eval_class_3_recall': 0.427, 'eval_class_3_f1': 0.46037735849056605, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6494644595910418, 'eval_class_4_recall': 0.667, 'eval_class_4_f1': 0.6581154415392205, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.4269, 'eval_samples_per_second': 52.951, 'eval_steps_per_second': 13.238, 'epoch': 0.6}
{'loss': 1.0219, 'grad_norm': 20.003572463989258, 'learning_rate': 7.600400000000001e-06, 'epoch': 0.62}


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 64%|██████▍   | 32000/50000 [3:39:37<1:34:16,  3.18it/s]{'eval_loss': 1.0366626977920532, 'eval_accuracy': 0.5492, 'eval_f1': 0.5422179301862382, 'eval_precision': 0.5415622502836058, 'eval_recall': 0.5492, 'eval_class_0_precision': 0.6349206349206349, 'eval_class_0_recall': 0.76, 'eval_class_0_f1': 0.6918525261720528, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4508723599632691, 'eval_class_1_recall': 0.491, 'eval_class_1_f1': 0.4700813786500718, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.45544554455445546, 'eval_class_2_recall': 0.368, 'eval_class_2_f1': 0.40707964601769914, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5191846522781774, 'eval_class_3_recall': 0.433, 'eval_class_3_f1': 0.4721919302071974, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6473880597014925, 'eval_class_4_recall': 0.694, 'eval_class_4_f1': 0.6698841698841699, 'eval_class_4_support': 1000.0, 'eval_runtime': 95.276, 'eval_samples_per_second': 52.479, 'eval_steps_per_second': 13.12, 'epoch': 0.62}
{'loss': 1.0134, 'grad_norm': 9.42296028137207, 'learning_rate': 7.2004000000000006e-06, 'epoch': 0.64}


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 66%|██████▌   | 33000/50000 [3:46:28<1:28:55,  3.19it/s]{'eval_loss': 1.0222407579421997, 'eval_accuracy': 0.5414, 'eval_f1': 0.5411661159773026, 'eval_precision': 0.5469551095427497, 'eval_recall': 0.5414, 'eval_class_0_precision': 0.698559670781893, 'eval_class_0_recall': 0.679, 'eval_class_0_f1': 0.6886409736308317, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4413793103448276, 'eval_class_1_recall': 0.576, 'eval_class_1_f1': 0.4997830802603037, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.44292803970223327, 'eval_class_2_recall': 0.357, 'eval_class_2_f1': 0.3953488372093023, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4801192842942346, 'eval_class_3_recall': 0.483, 'eval_class_3_f1': 0.481555333998006, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6717892425905598, 'eval_class_4_recall': 0.612, 'eval_class_4_f1': 0.640502354788069, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.3088, 'eval_samples_per_second': 53.017, 'eval_steps_per_second': 13.254, 'epoch': 0.64}
{'loss': 1.0287, 'grad_norm': 15.69183349609375, 'learning_rate': 6.8004e-06, 'epoch': 0.66}


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 68%|██████▊   | 34000/50000 [3:53:19<1:24:28,  3.16it/s]{'eval_loss': 1.0218431949615479, 'eval_accuracy': 0.5442, 'eval_f1': 0.5465016221093608, 'eval_precision': 0.5495491076893726, 'eval_recall': 0.5442, 'eval_class_0_precision': 0.6978947368421052, 'eval_class_0_recall': 0.663, 'eval_class_0_f1': 0.68, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46418056918547596, 'eval_class_1_recall': 0.473, 'eval_class_1_f1': 0.46854878652798415, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4372093023255814, 'eval_class_2_recall': 0.47, 'eval_class_2_f1': 0.4530120481927711, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4789422135161606, 'eval_class_3_recall': 0.489, 'eval_class_3_f1': 0.4839188520534389, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6695187165775401, 'eval_class_4_recall': 0.626, 'eval_class_4_f1': 0.6470284237726098, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.4069, 'eval_samples_per_second': 52.962, 'eval_steps_per_second': 13.241, 'epoch': 0.66}
{'loss': 1.0026, 'grad_norm': 26.703550338745117, 'learning_rate': 6.4004000000000014e-06, 'epoch': 0.68}


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 70%|███████   | 35000/50000 [4:00:10<1:18:37,  3.18it/s]{'eval_loss': 1.0332975387573242, 'eval_accuracy': 0.5458, 'eval_f1': 0.5437212845210043, 'eval_precision': 0.5451656119867041, 'eval_recall': 0.5458, 'eval_class_0_precision': 0.6506437768240343, 'eval_class_0_recall': 0.758, 'eval_class_0_f1': 0.7002309468822171, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4771689497716895, 'eval_class_1_recall': 0.418, 'eval_class_1_f1': 0.44562899786780386, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.435634328358209, 'eval_class_2_recall': 0.467, 'eval_class_2_f1': 0.45077220077220076, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.48296593186372744, 'eval_class_3_recall': 0.482, 'eval_class_3_f1': 0.4824824824824825, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6794150731158605, 'eval_class_4_recall': 0.604, 'eval_class_4_f1': 0.6394917946003176, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.1263, 'eval_samples_per_second': 53.12, 'eval_steps_per_second': 13.28, 'epoch': 0.68}
{'loss': 0.9868, 'grad_norm': 9.695844650268555, 'learning_rate': 6.000400000000001e-06, 'epoch': 0.7}


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 72%|███████▏  | 36000/50000 [4:07:02<1:13:43,  3.17it/s]{'eval_loss': 1.0282750129699707, 'eval_accuracy': 0.5538, 'eval_f1': 0.5558868906396712, 'eval_precision': 0.5597748058996598, 'eval_recall': 0.5538, 'eval_class_0_precision': 0.72271714922049, 'eval_class_0_recall': 0.649, 'eval_class_0_f1': 0.6838777660695469, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46422182468694095, 'eval_class_1_recall': 0.519, 'eval_class_1_f1': 0.49008498583569404, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4498106060606061, 'eval_class_2_recall': 0.475, 'eval_class_2_f1': 0.46206225680933855, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.49946178686759957, 'eval_class_3_recall': 0.464, 'eval_class_3_f1': 0.481078278900985, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6626626626626627, 'eval_class_4_recall': 0.662, 'eval_class_4_f1': 0.6623311655827914, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.55, 'eval_samples_per_second': 52.882, 'eval_steps_per_second': 13.221, 'epoch': 0.7}
{'loss': 1.0074, 'grad_norm': 9.656718254089355, 'learning_rate': 5.600400000000001e-06, 'epoch': 0.72}


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 74%|███████▍  | 37000/50000 [4:13:54<1:08:19,  3.17it/s]{'eval_loss': 1.0323526859283447, 'eval_accuracy': 0.5448, 'eval_f1': 0.5474185571681972, 'eval_precision': 0.5518701049425082, 'eval_recall': 0.5448, 'eval_class_0_precision': 0.6996935648621042, 'eval_class_0_recall': 0.685, 'eval_class_0_f1': 0.6922688226376958, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46641074856046066, 'eval_class_1_recall': 0.486, 'eval_class_1_f1': 0.47600391772771794, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.44164332399626516, 'eval_class_2_recall': 0.473, 'eval_class_2_f1': 0.45678416224046353, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.47564469914040114, 'eval_class_3_recall': 0.498, 'eval_class_3_f1': 0.4865657059110894, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6759581881533101, 'eval_class_4_recall': 0.582, 'eval_class_4_f1': 0.6254701773240193, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.1941, 'eval_samples_per_second': 53.082, 'eval_steps_per_second': 13.27, 'epoch': 0.72}
{'loss': 0.9942, 'grad_norm': 15.871129035949707, 'learning_rate': 5.2004e-06, 'epoch': 0.74}


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 76%|███████▌  | 38000/50000 [4:20:46<1:02:58,  3.18it/s]{'eval_loss': 1.0402014255523682, 'eval_accuracy': 0.548, 'eval_f1': 0.5451634850601795, 'eval_precision': 0.5474760881683601, 'eval_recall': 0.548, 'eval_class_0_precision': 0.6846153846153846, 'eval_class_0_recall': 0.712, 'eval_class_0_f1': 0.6980392156862745, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4554794520547945, 'eval_class_1_recall': 0.532, 'eval_class_1_f1': 0.4907749077490775, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.43409316154608524, 'eval_class_2_recall': 0.438, 'eval_class_2_f1': 0.43603782976605276, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5093833780160858, 'eval_class_3_recall': 0.38, 'eval_class_3_f1': 0.4352806414662085, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6538090646094503, 'eval_class_4_recall': 0.678, 'eval_class_4_f1': 0.6656848306332842, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.5618, 'eval_samples_per_second': 52.875, 'eval_steps_per_second': 13.219, 'epoch': 0.74}
{'loss': 1.027, 'grad_norm': 6.238520622253418, 'learning_rate': 4.800400000000001e-06, 'epoch': 0.76}


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 78%|███████▊  | 39000/50000 [4:27:38<57:45,  3.17it/s]{'eval_loss': 1.0184117555618286, 'eval_accuracy': 0.5522, 'eval_f1': 0.552426428289222, 'eval_precision': 0.5582354981515472, 'eval_recall': 0.5522, 'eval_class_0_precision': 0.7262693156732892, 'eval_class_0_recall': 0.658, 'eval_class_0_f1': 0.6904512067156349, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4534351145038168, 'eval_class_1_recall': 0.594, 'eval_class_1_f1': 0.5142857142857142, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.44994246260069043, 'eval_class_2_recall': 0.391, 'eval_class_2_f1': 0.4184055644729802, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4962080173347779, 'eval_class_3_recall': 0.458, 'eval_class_3_f1': 0.4763390535621425, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6653225806451613, 'eval_class_4_recall': 0.66, 'eval_class_4_f1': 0.6626506024096386, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.4182, 'eval_samples_per_second': 52.956, 'eval_steps_per_second': 13.239, 'epoch': 0.76}
{'loss': 1.0125, 'grad_norm': 12.566045761108398, 'learning_rate': 4.4004e-06, 'epoch': 0.78}


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 80%|████████  | 40000/50000 [4:34:29<52:32,  3.17it/s]{'eval_loss': 1.0146613121032715, 'eval_accuracy': 0.5526, 'eval_f1': 0.5538708986421357, 'eval_precision': 0.5555431780813889, 'eval_recall': 0.5526, 'eval_class_0_precision': 0.6892712550607287, 'eval_class_0_recall': 0.681, 'eval_class_0_f1': 0.6851106639839034, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4768928220255654, 'eval_class_1_recall': 0.485, 'eval_class_1_f1': 0.480912245909767, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4446529080675422, 'eval_class_2_recall': 0.474, 'eval_class_2_f1': 0.45885769603097776, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4918200408997955, 'eval_class_3_recall': 0.481, 'eval_class_3_f1': 0.4863498483316481, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6750788643533123, 'eval_class_4_recall': 0.642, 'eval_class_4_f1': 0.6581240389543823, 'eval_class_4_support': 1000.0, 'eval_runtime': 93.948, 'eval_samples_per_second': 53.221, 'eval_steps_per_second': 13.305, 'epoch': 0.78}
{'loss': 0.9936, 'grad_norm': 10.769098281860352, 'learning_rate': 4.000400000000001e-06, 'epoch': 0.8}


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 82%|████████▏ | 41000/50000 [4:41:21<47:22,  3.17it/s]{'eval_loss': 1.0276356935501099, 'eval_accuracy': 0.5528, 'eval_f1': 0.552333321465175, 'eval_precision': 0.5537162630786862, 'eval_recall': 0.5528, 'eval_class_0_precision': 0.6795112781954887, 'eval_class_0_recall': 0.723, 'eval_class_0_f1': 0.7005813953488372, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4815595363540569, 'eval_class_1_recall': 0.457, 'eval_class_1_f1': 0.4689584402257568, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.45348837209302323, 'eval_class_2_recall': 0.429, 'eval_class_2_f1': 0.4409044193216855, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.47345132743362833, 'eval_class_3_recall': 0.535, 'eval_class_3_f1': 0.5023474178403756, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6805708013172338, 'eval_class_4_recall': 0.62, 'eval_class_4_f1': 0.6488749345892203, 'eval_class_4_support': 1000.0, 'eval_runtime': 93.9963, 'eval_samples_per_second': 53.194, 'eval_steps_per_second': 13.298, 'epoch': 0.8}
{'loss': 1.0162, 'grad_norm': 8.323751449584961, 'learning_rate': 3.6004000000000007e-06, 'epoch': 0.82}


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 84%|████████▍ | 42000/50000 [4:48:12<42:05,  3.17it/s]{'eval_loss': 1.014536738395691, 'eval_accuracy': 0.554, 'eval_f1': 0.5547267166265099, 'eval_precision': 0.5559315438737827, 'eval_recall': 0.554, 'eval_class_0_precision': 0.6875612144955926, 'eval_class_0_recall': 0.702, 'eval_class_0_f1': 0.6947055912914398, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4792079207920792, 'eval_class_1_recall': 0.484, 'eval_class_1_f1': 0.481592039800995, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.44706994328922495, 'eval_class_2_recall': 0.473, 'eval_class_2_f1': 0.45966958211856174, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.49382716049382713, 'eval_class_3_recall': 0.48, 'eval_class_3_f1': 0.486815415821501, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6719914802981896, 'eval_class_4_recall': 0.631, 'eval_class_4_f1': 0.6508509541000516, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.3694, 'eval_samples_per_second': 52.983, 'eval_steps_per_second': 13.246, 'epoch': 0.82}
{'loss': 0.9898, 'grad_norm': 12.2147216796875, 'learning_rate': 3.2004000000000003e-06, 'epoch': 0.84}


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 86%|████████▌ | 43000/50000 [4:55:04<36:43,  3.18it/s]{'eval_loss': 1.0192607641220093, 'eval_accuracy': 0.5472, 'eval_f1': 0.5518884216668721, 'eval_precision': 0.561468800287105, 'eval_recall': 0.5472, 'eval_class_0_precision': 0.7250280583613917, 'eval_class_0_recall': 0.646, 'eval_class_0_f1': 0.6832363828662084, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4792719919110212, 'eval_class_1_recall': 0.474, 'eval_class_1_f1': 0.4766214177978884, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4300254452926209, 'eval_class_2_recall': 0.507, 'eval_class_2_f1': 0.4653510784763653, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4810810810810811, 'eval_class_3_recall': 0.534, 'eval_class_3_f1': 0.5061611374407583, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6919374247894103, 'eval_class_4_recall': 0.575, 'eval_class_4_f1': 0.6280720917531404, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.6789, 'eval_samples_per_second': 52.81, 'eval_steps_per_second': 13.203, 'epoch': 0.84}
{'loss': 0.9914, 'grad_norm': 11.230884552001953, 'learning_rate': 2.8004000000000003e-06, 'epoch': 0.86}


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 88%|████████▊ | 44000/50000 [5:01:55<31:31,  3.17it/s]{'eval_loss': 1.0299302339553833, 'eval_accuracy': 0.5556, 'eval_f1': 0.551365881216573, 'eval_precision': 0.5507845584352633, 'eval_recall': 0.5556, 'eval_class_0_precision': 0.6663636363636364, 'eval_class_0_recall': 0.733, 'eval_class_0_f1': 0.6980952380952381, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46672582076308783, 'eval_class_1_recall': 0.526, 'eval_class_1_f1': 0.49459332393041844, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4655819774718398, 'eval_class_2_recall': 0.372, 'eval_class_2_f1': 0.41356309060589214, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4989406779661017, 'eval_class_3_recall': 0.471, 'eval_class_3_f1': 0.4845679012345679, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6563106796116505, 'eval_class_4_recall': 0.676, 'eval_class_4_f1': 0.6660098522167488, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.6467, 'eval_samples_per_second': 52.828, 'eval_steps_per_second': 13.207, 'epoch': 0.86}
{'loss': 0.9935, 'grad_norm': 11.953861236572266, 'learning_rate': 2.4004000000000003e-06, 'epoch': 0.88}


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 90%|█████████ | 45000/50000 [5:45:27<26:12,  3.18it/s]{'eval_loss': 1.0160523653030396, 'eval_accuracy': 0.5498, 'eval_f1': 0.5528508873655968, 'eval_precision': 0.5572375224400341, 'eval_recall': 0.5498, 'eval_class_0_precision': 0.7178649237472767, 'eval_class_0_recall': 0.659, 'eval_class_0_f1': 0.6871741397288843, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46834264432029793, 'eval_class_1_recall': 0.503, 'eval_class_1_f1': 0.485053037608486, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.43834296724470134, 'eval_class_2_recall': 0.455, 'eval_class_2_f1': 0.4465161923454367, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.48233046800382046, 'eval_class_3_recall': 0.505, 'eval_class_3_f1': 0.4934049829018075, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6793066088840737, 'eval_class_4_recall': 0.627, 'eval_class_4_f1': 0.6521060842433697, 'eval_class_4_support': 1000.0, 'eval_runtime': 94.7444, 'eval_samples_per_second': 52.774, 'eval_steps_per_second': 13.193, 'epoch': 0.88}
{'loss': 0.9924, 'grad_norm': 18.64478874206543, 'learning_rate': 2.0004e-06, 'epoch': 0.9}


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 92%|█████████▏| 46000/50000 [6:21:22<42:08,  1.58it/s]{'eval_loss': 1.0130107402801514, 'eval_accuracy': 0.5528, 'eval_f1': 0.5522278578197882, 'eval_precision': 0.552650115045828, 'eval_recall': 0.5528, 'eval_class_0_precision': 0.6912065439672802, 'eval_class_0_recall': 0.676, 'eval_class_0_f1': 0.6835187057633973, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46919431279620855, 'eval_class_1_recall': 0.495, 'eval_class_1_f1': 0.48175182481751827, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4396467124631992, 'eval_class_2_recall': 0.448, 'eval_class_2_f1': 0.4437840515106488, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5072788353863382, 'eval_class_3_recall': 0.453, 'eval_class_3_f1': 0.4786053882725832, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6559241706161137, 'eval_class_4_recall': 0.692, 'eval_class_4_f1': 0.6734793187347932, 'eval_class_4_support': 1000.0, 'eval_runtime': 1701.0726, 'eval_samples_per_second': 2.939, 'eval_steps_per_second': 0.735, 'epoch': 0.9}
{'loss': 0.999, 'grad_norm': 9.798530578613281, 'learning_rate': 1.6004e-06, 'epoch': 0.92}


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 94%|█████████▍| 47000/50000 [6:36:20<36:08,  1.38it/s]{'eval_loss': 1.0099101066589355, 'eval_accuracy': 0.5538, 'eval_f1': 0.5537983288585727, 'eval_precision': 0.5553788176413458, 'eval_recall': 0.5538, 'eval_class_0_precision': 0.6980552712384852, 'eval_class_0_recall': 0.682, 'eval_class_0_f1': 0.689934243803743, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46798917944093776, 'eval_class_1_recall': 0.519, 'eval_class_1_f1': 0.492176386913229, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.43980582524271844, 'eval_class_2_recall': 0.453, 'eval_class_2_f1': 0.4463054187192118, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5063437139561707, 'eval_class_3_recall': 0.439, 'eval_class_3_f1': 0.4702731655061596, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6647000983284169, 'eval_class_4_recall': 0.676, 'eval_class_4_f1': 0.6703024293505205, 'eval_class_4_support': 1000.0, 'eval_runtime': 211.8702, 'eval_samples_per_second': 23.599, 'eval_steps_per_second': 5.9, 'epoch': 0.92}
{'loss': 0.9754, 'grad_norm': 7.958282470703125, 'learning_rate': 1.2004e-06, 'epoch': 0.94}


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 96%|█████████▌| 48000/50000 [6:53:48<26:48,  1.24it/s]{'eval_loss': 1.0098158121109009, 'eval_accuracy': 0.552, 'eval_f1': 0.5532752454405644, 'eval_precision': 0.5560841876056494, 'eval_recall': 0.552, 'eval_class_0_precision': 0.7055674518201285, 'eval_class_0_recall': 0.659, 'eval_class_0_f1': 0.6814891416752844, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4645390070921986, 'eval_class_1_recall': 0.524, 'eval_class_1_f1': 0.4924812030075188, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.44089147286821706, 'eval_class_2_recall': 0.455, 'eval_class_2_f1': 0.4478346456692913, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5027563395810364, 'eval_class_3_recall': 0.456, 'eval_class_3_f1': 0.47823807026743576, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6666666666666666, 'eval_class_4_recall': 0.666, 'eval_class_4_f1': 0.6663331665832917, 'eval_class_4_support': 1000.0, 'eval_runtime': 247.7553, 'eval_samples_per_second': 20.181, 'eval_steps_per_second': 5.045, 'epoch': 0.94}
{'loss': 0.9854, 'grad_norm': 10.414144515991211, 'learning_rate': 8.004000000000001e-07, 'epoch': 0.96}


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 98%|█████████▊| 49000/50000 [7:11:44<13:30,  1.23it/s]{'eval_loss': 1.0084733963012695, 'eval_accuracy': 0.5554, 'eval_f1': 0.5545845137316453, 'eval_precision': 0.5551659149010935, 'eval_recall': 0.5554, 'eval_class_0_precision': 0.6981519507186859, 'eval_class_0_recall': 0.68, 'eval_class_0_f1': 0.6889564336372846, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46869409660107336, 'eval_class_1_recall': 0.524, 'eval_class_1_f1': 0.4948064211520302, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4485294117647059, 'eval_class_2_recall': 0.427, 'eval_class_2_f1': 0.4375, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5033112582781457, 'eval_class_3_recall': 0.456, 'eval_class_3_f1': 0.47848898216159497, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6571428571428571, 'eval_class_4_recall': 0.69, 'eval_class_4_f1': 0.6731707317073171, 'eval_class_4_support': 1000.0, 'eval_runtime': 259.0779, 'eval_samples_per_second': 19.299, 'eval_steps_per_second': 4.825, 'epoch': 0.96}
{'loss': 0.9987, 'grad_norm': 9.013275146484375, 'learning_rate': 4.004e-07, 'epoch': 0.98}


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100%|██████████| 50000/50000 [8:08:02<00:00,  1.84it/s]{'eval_loss': 1.006805658340454, 'eval_accuracy': 0.5512, 'eval_f1': 0.5522950715044098, 'eval_precision': 0.5543235700957377, 'eval_recall': 0.5512, 'eval_class_0_precision': 0.7026178010471205, 'eval_class_0_recall': 0.671, 'eval_class_0_f1': 0.6864450127877237, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.4677565849227975, 'eval_class_1_recall': 0.515, 'eval_class_1_f1': 0.4902427415516421, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4382896015549077, 'eval_class_2_recall': 0.451, 'eval_class_2_f1': 0.4445539674716609, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.4972972972972973, 'eval_class_3_recall': 0.46, 'eval_class_3_f1': 0.4779220779220779, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6656565656565656, 'eval_class_4_recall': 0.659, 'eval_class_4_f1': 0.6623115577889447, 'eval_class_4_support': 1000.0, 'eval_runtime': 1126.6363, 'eval_samples_per_second': 4.438, 'eval_steps_per_second': 1.109, 'epoch': 0.98}
{'loss': 0.9721, 'grad_norm': 10.795476913452148, 'learning_rate': 4.0000000000000007e-10, 'epoch': 1.0}


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2025-04-26 18:28:49.543 | INFO     | __main__:main:128 - Evaluating on test set...
{'eval_loss': 1.0071595907211304, 'eval_accuracy': 0.5512, 'eval_f1': 0.5509402907144375, 'eval_precision': 0.5517164824336298, 'eval_recall': 0.5512, 'eval_class_0_precision': 0.6919959473150963, 'eval_class_0_recall': 0.683, 'eval_class_0_f1': 0.6874685455460493, 'eval_class_0_support': 1000.0, 'eval_class_1_precision': 0.46422018348623856, 'eval_class_1_recall': 0.506, 'eval_class_1_f1': 0.4842105263157895, 'eval_class_1_support': 1000.0, 'eval_class_2_precision': 0.4383697813121272, 'eval_class_2_recall': 0.441, 'eval_class_2_f1': 0.43968095712861416, 'eval_class_2_support': 1000.0, 'eval_class_3_precision': 0.5044843049327354, 'eval_class_3_recall': 0.45, 'eval_class_3_f1': 0.47568710359408034, 'eval_class_3_support': 1000.0, 'eval_class_4_precision': 0.6595121951219513, 'eval_class_4_recall': 0.676, 'eval_class_4_f1': 0.6676543209876543, 'eval_class_4_support': 1000.0, 'eval_runtime': 201.0014, 'eval_samples_per_second': 24.875, 'eval_steps_per_second': 6.219, 'epoch': 1.0}
{'train_runtime': 29486.2896, 'train_samples_per_second': 6.783, 'train_steps_per_second': 1.696, 'train_loss': 1.0508904418945313, 'epoch': 1.0}

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2025-04-26 18:32:24.072 | INFO     | __main__:main:135 - Saving model...
wandb:                                                                                
wandb: 
wandb: Run history:
wandb:           eval/accuracy ▁▃▃▄▄▄▅▅▅▅▅▅▅▅▅▆▅▆▆▆▆▇▆▆▆▆▆▆▆▆▆▇▆▇▆▆▆▇▆█
wandb:         eval/class_0_f1 ▁▅▆▅▆▇▇▆▇▇▇▇▆▇▇▇▇▆▇▇▇▇█▇▇▇█▇▇▇▇█▇▇▇▇▇▇▇█
wandb:  eval/class_0_precision ▃▃▄▇▄▂▃▅▅▁▄▆█▆▄█▆█▆▆▆▆▅▄▄▆▄▇▆▆▆▅▆▇▅▆▇▆▆▇
wandb:     eval/class_0_recall ▁▅▆▄▆▇▅▆█▄▆▄▇▅▆▅▅▅▆▇▆▆▇▇▆▇▅▆▆▅▆▆▅▇▅▆▅▆▆▆
wandb:    eval/class_0_support ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:         eval/class_1_f1 ▂▇▃▃▅▄▆▁▆▂▇▅▆▆▅▇▆▆▆▅▆▅▇▆▇▅▆▆▆▇▆▆▆▇▆▇▇▇▆█
wandb:  eval/class_1_precision ▂▁▃▄▃▅▄▃▅▅▅▅▅▄▄▄▄▄▄▄▆▆▆▆▅▅▅▆▇▆▅▇▇▇▆▆▆▆▆█
wandb:     eval/class_1_recall ▂█▂▂▅▂▃▅▁▄▄▄▅▅▄▃▅▅▅▅▃▄▄▄▄▄▃▄▄▅▄▃▄▄▄▄▄▄▄▅
wandb:    eval/class_1_support ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:         eval/class_2_f1 ▅▁▇▆▆▆▆▅█▆▇▆▆▇▅▆▇▆▆▆▇▇▇▆▇▆▇▇█▇▇▇█▆▇▇▇▇▇█
wandb:  eval/class_2_precision ▂▆▂▁▅▅▃▅▅▄▄▄▅▅▅▆▅▆▅▅▆▆▆▆▆▅▆▆▅▆▆▆▅▇▆▆▆▆▆█
wandb:     eval/class_2_recall ▄▁▆▇▅▅▄█▅▆▆▅▇▄▆▆▅▅▅▇▆▆▅▆▅▇▇▇▇▆▇▆▇▇▅▆▆▆▆▇
wandb:    eval/class_2_support ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:         eval/class_3_f1 ▁▄▁▆▁▆▆▆▇▆▆█▆▇▆▇▅▆▇▇▆▇▇▆▇▇▇▇▇▇▇█▇█▇▇▇▇▇█
wandb:  eval/class_3_precision ▁▂▅▄▅▅▆▆▅▅▆▅▆▆▇▇▇▆▇▆▆▇█▇▇▆▆▇▆▇▆▆▇▆▆▇▇▇▇▇
wandb:     eval/class_3_recall ▁▅▁▆█▅▆█▆▇█▆▇▅▄▆▄▅▆▆▄▇▆▅▆▇▇▇▇▇▇▇██▇▆▇▇▇█
wandb:    eval/class_3_support ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:         eval/class_4_f1 ▅▆▅▅▁▆▆▅▆▇▇▆▇█▇█▇▇█▆▆▇█▇▇▆▇▆▇▇▆▇▆▇▇▇▇▇▇▇
wandb:  eval/class_4_precision ▁▃▄▅▅▅▅▆▇▅▅█▆▇▆▆▇▅▆▆▅▇▆▅▆▇▆▇▆▆▇▇▇▆▇▆▆▆▆▇
wandb:     eval/class_4_recall █▆▆▄▄▅▄▃▅▄▁▆▄▅▇▅█▆▆▇▄▆▇▅▆▅▄▅▄▆▅▅▅▆▅▆▅▆▅▅
wandb:    eval/class_4_support ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁
wandb:                 eval/f1 ▁▁▃▄▃▅▅▅▅▅▅▅▆▆▅▆▆▆▆▆▆▆▆▆▆▆▆▆▆▇▆▇▇▇▇▇▇▇▇█
wandb:               eval/loss █▇▆▆▇▄▅▄▄▅▃▄▃▄▄▃▃▃▃▃▂▃▃▃▂▃▃▃▃▂▃▂▂▃▂▂▂▂▂▁
wandb:          eval/precision ▁▄▃▄▄▄▄▅▅▅▆▆▅▆▅▆▆▆▆▆▆▆▆▆▆▆▇▇▆▇▇▇▇▆▇▇▇▇▇█
wandb:             eval/recall ▁▁▃▂▄▄▅▅▅▅▆▅▅▅▅▆▆▅▆▆▆▆▇▆▆▆▆▇▆▆▆▇▆▇▆▇▆▇▆█
wandb:            eval/runtime ▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▂▂▂█▂
wandb: eval/samples_per_second ███████████████▇█▇█████████████████▄▃▃▁▄
wandb:   eval/steps_per_second ███████████████▇▇█▇████████████████▄▃▃▁▄
wandb:             train/epoch ▁▁▁▁▁▁▂▂▂▂▂▂▃▃▃▄▄▄▄▄▅▅▅▅▅▅▅▆▆▆▆▆▇▇▇█████
wandb:       train/global_step ▁▁▂▂▂▃▃▃▃▃▃▄▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▇▇▇▇▇▇▇▇████
wandb:         train/grad_norm ▃▁▃▂▂▅▃▄▃▂▄▄▁▅█▄▇▄▄▆▃▃▂▂▆▄█▂▂▄▃▃▂▃▃▅▂▂▂▃
wandb:     train/learning_rate ████▇▇▇▇▇▆▆▆▆▆▆▅▅▅▅▅▄▄▄▄▄▃▃▃▃▃▃▂▂▂▂▂▂▁▁▁
wandb:              train/loss █▆▅▄▄▃▄▃▃▃▃▂▃▃▂▂▂▂▂▂▂▂▁▂▂▂▂▁▂▁▂▁▂▁▁▁▁▁▁▁
wandb: 
wandb: Run summary:
wandb:            eval/accuracy 0.5808
wandb:          eval/class_0_f1 0.71378
wandb:   eval/class_0_precision 0.72069
wandb:      eval/class_0_recall 0.707
wandb:     eval/class_0_support 1000
wandb:          eval/class_1_f1 0.52787
wandb:   eval/class_1_precision 0.50409
wandb:      eval/class_1_recall 0.554
wandb:     eval/class_1_support 1000
wandb:          eval/class_2_f1 0.48146
wandb:   eval/class_2_precision 0.48916
wandb:      eval/class_2_recall 0.474
wandb:     eval/class_2_support 1000
wandb:          eval/class_3_f1 0.51406
wandb:   eval/class_3_precision 0.51613
wandb:      eval/class_3_recall 0.512
wandb:     eval/class_3_support 1000
wandb:          eval/class_4_f1 0.67075
wandb:   eval/class_4_precision 0.68509
wandb:      eval/class_4_recall 0.657
wandb:     eval/class_4_support 1000
wandb:                  eval/f1 0.58158
wandb:                eval/loss 0.96973
wandb:           eval/precision 0.58303
wandb:              eval/recall 0.5808
wandb:             eval/runtime 214.5274
wandb:  eval/samples_per_second 23.307
wandb:    eval/steps_per_second 5.827
wandb:               total_flos 2.6494897152e+16
wandb:              train/epoch 1
wandb:        train/global_step 50000
wandb:          train/grad_norm 10.79548
wandb:      train/learning_rate 0.0
wandb:               train/loss 0.9721
wandb:               train_loss 1.05089
wandb:            train_runtime 29486.2896
wandb: train_samples_per_second 6.783
wandb:   train_steps_per_second 1.696
wandb: 
wandb: 🚀 View run distilbert-base-multilingual at: https://wandb.ai/nymiz/amazon-reviews-es/runs/58w0rceh
wandb: ⭐️ View project at: https://wandb.ai/nymiz/amazon-reviews-es
wandb: Synced 8 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)
wandb: Find logs at: ./wandb/run-20250426_101719-58w0rceh/logs