Training in progress, step 3000

nohup.out

@@ -70369,3 +70369,65 @@ If your task is similar to the task the model of the checkpoint was trained on,
 [INFO|modeling_utils.py:1693] 2023-12-29 18:02:23,729 >> Model weights saved in ./checkpoint-2000/pytorch_model.bin
 [INFO|feature_extraction_utils.py:368] 2023-12-29 18:02:23,735 >> Feature extractor saved in ./checkpoint-2000/preprocessor_config.json
 [INFO|feature_extraction_utils.py:368] 2023-12-29 18:02:29,350 >> Feature extractor saved in ./preprocessor_config.json
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 60%|██████    | 3000/5000 [4:21:30<2:13:31,  4.01s/it][INFO|trainer.py:2955] 2023-12-29 19:10:39,229 >> ***** Running Evaluation *****
+[INFO|trainer.py:2959] 2023-12-29 19:10:39,229 >>   Num examples: Unknown
+[INFO|trainer.py:2960] 2023-12-29 19:10:39,229 >>   Batch size = 16
+{'loss': 0.0601, 'learning_rate': 5.895555555555557e-06, 'epoch': 3.0}
+{'loss': 0.056, 'learning_rate': 5.84e-06, 'epoch': 3.01}
+{'loss': 0.062, 'learning_rate': 5.784444444444445e-06, 'epoch': 3.01}
+{'loss': 0.0645, 'learning_rate': 5.72888888888889e-06, 'epoch': 3.02}
+{'loss': 0.0595, 'learning_rate': 5.673333333333334e-06, 'epoch': 3.02}
+{'loss': 0.0543, 'learning_rate': 5.617777777777779e-06, 'epoch': 3.03}
+{'loss': 0.0525, 'learning_rate': 5.562222222222222e-06, 'epoch': 3.03}
+{'loss': 0.0494, 'learning_rate': 5.506666666666667e-06, 'epoch': 3.04}
+{'loss': 0.0473, 'learning_rate': 5.451111111111112e-06, 'epoch': 3.04}
+{'loss': 0.0483, 'learning_rate': 5.3955555555555565e-06, 'epoch': 3.05}
+{'loss': 0.0467, 'learning_rate': 5.3400000000000005e-06, 'epoch': 3.05}
+{'loss': 0.0503, 'learning_rate': 5.2844444444444454e-06, 'epoch': 3.06}
+{'loss': 0.0428, 'learning_rate': 5.228888888888889e-06, 'epoch': 3.06}
+{'loss': 0.0418, 'learning_rate': 5.1733333333333335e-06, 'epoch': 3.07}
+{'loss': 0.0424, 'learning_rate': 5.117777777777778e-06, 'epoch': 3.07}
+{'loss': 0.0406, 'learning_rate': 5.062222222222222e-06, 'epoch': 3.08}
+{'loss': 0.0327, 'learning_rate': 5.006666666666667e-06, 'epoch': 3.08}
+{'loss': 0.0371, 'learning_rate': 4.951111111111111e-06, 'epoch': 3.09}
+{'loss': 0.0313, 'learning_rate': 4.895555555555556e-06, 'epoch': 3.09}
+{'loss': 0.0295, 'learning_rate': 4.84e-06, 'epoch': 3.1}
+{'loss': 0.0284, 'learning_rate': 4.784444444444445e-06, 'epoch': 3.1}
+{'loss': 0.0322, 'learning_rate': 4.728888888888889e-06, 'epoch': 3.11}
+{'loss': 0.0307, 'learning_rate': 4.673333333333333e-06, 'epoch': 3.11}
+{'loss': 0.0316, 'learning_rate': 4.617777777777778e-06, 'epoch': 3.12}
+{'loss': 0.0286, 'learning_rate': 4.562222222222222e-06, 'epoch': 3.12}
+{'loss': 0.0289, 'learning_rate': 4.506666666666667e-06, 'epoch': 3.13}
+{'loss': 0.0264, 'learning_rate': 4.451111111111112e-06, 'epoch': 3.13}
+
+
+
+[INFO|trainer_utils.py:689] 2023-12-29 19:10:42,815 >> The following columns in the evaluation set don't have a corresponding argument in `WhisperForConditionalGeneration.forward` and have been ignored: input_length. If input_length are not expected by `WhisperForConditionalGeneration.forward`,  you can safely ignore this message.
+
                                                       
 60%|██████    | 3000/5000 [4:49:44<2:13:31,  4.01s/it][INFO|trainer.py:2700] 2023-12-29 19:38:53,074 >> Saving model checkpoint to ./checkpoint-3000
+[INFO|configuration_utils.py:447] 2023-12-29 19:38:53,075 >> Configuration saved in ./checkpoint-3000/config.json
+[INFO|modeling_utils.py:1693] 2023-12-29 19:38:54,229 >> Model weights saved in ./checkpoint-3000/pytorch_model.bin
+[INFO|feature_extraction_utils.py:368] 2023-12-29 19:38:54,235 >> Feature extractor saved in ./checkpoint-3000/preprocessor_config.json
+[INFO|feature_extraction_utils.py:368] 2023-12-29 19:38:59,893 >> Feature extractor saved in ./preprocessor_config.json

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