Training in progress, step 4000

log

@@ -33449,3 +33449,98 @@ If your task is similar to the task the model of the checkpoint was trained on,
 [INFO|modeling_utils.py:1680] 2022-12-15 05:41:04,188 >> Model weights saved in ./checkpoint-3000/pytorch_model.bin
 [INFO|feature_extraction_utils.py:368] 2022-12-15 05:41:04,202 >> Feature extractor saved in ./checkpoint-3000/preprocessor_config.json
 [INFO|feature_extraction_utils.py:368] 2022-12-15 05:41:08,018 >> Feature extractor saved in ./preprocessor_config.json
+/home/ubuntu/hf_env/lib/python3.8/site-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None
+  warnings.warn("None of the inputs have requires_grad=True. Gradients will be None")
+
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 55%|█████▍    | 3283/6000 [12:25:41<54:04,  1.19s/it]  {'eval_loss': 0.26857462525367737, 'eval_wer': 12.927253880501807, 'eval_runtime': 5158.1008, 'eval_samples_per_second': 2.909, 'eval_steps_per_second': 0.091, 'epoch': 6.03}
+{'loss': 0.0671, 'learning_rate': 4.331636363636363e-06, 'epoch': 6.04}
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+{'loss': 0.0743, 'learning_rate': 4.2589090909090905e-06, 'epoch': 6.04}
+{'loss': 0.0817, 'learning_rate': 4.222545454545454e-06, 'epoch': 6.05}
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+{'loss': 0.0901, 'learning_rate': 4.1498181818181816e-06, 'epoch': 6.06}
+{'loss': 0.094, 'learning_rate': 4.113454545454545e-06, 'epoch': 6.06}
+{'loss': 0.0753, 'learning_rate': 4.077090909090909e-06, 'epoch': 6.06}
+{'loss': 0.0676, 'learning_rate': 4.040727272727273e-06, 'epoch': 6.07}
+{'loss': 0.0714, 'learning_rate': 4.004363636363636e-06, 'epoch': 6.07}
+{'loss': 0.0729, 'learning_rate': 3.968e-06, 'epoch': 6.08}
+
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 67%|██████▋   | 4000/6000 [14:12:28<4:37:23,  8.32s/it][INFO|trainer.py:2955] 2022-12-15 08:10:05,274 >> ***** Running Evaluation *****
+[INFO|trainer.py:2959] 2022-12-15 08:10:05,274 >>   Num examples: Unknown
+[INFO|trainer.py:2960] 2022-12-15 08:10:05,274 >>   Batch size = 32
+{'loss': 0.1378, 'learning_rate': 3.2407272727272726e-06, 'epoch': 8.0}
+{'loss': 0.1425, 'learning_rate': 3.2043636363636363e-06, 'epoch': 8.01}
+{'loss': 0.1534, 'learning_rate': 3.168e-06, 'epoch': 8.01}
+{'loss': 0.139, 'learning_rate': 3.1316363636363632e-06, 'epoch': 8.02}
+{'loss': 0.1286, 'learning_rate': 3.0952727272727273e-06, 'epoch': 8.02}
+{'loss': 0.1312, 'learning_rate': 3.0589090909090906e-06, 'epoch': 8.02}
+{'loss': 0.1612, 'learning_rate': 3.0225454545454547e-06, 'epoch': 8.03}
+{'loss': 0.127, 'learning_rate': 2.986181818181818e-06, 'epoch': 8.03}
+{'loss': 0.1268, 'learning_rate': 2.9498181818181816e-06, 'epoch': 8.04}
+{'loss': 0.1301, 'learning_rate': 2.9134545454545453e-06, 'epoch': 8.04}
+
+
+
+[INFO|trainer_utils.py:689] 2022-12-15 08:10:10,811 >> 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.
+
                                                        
 67%|██████▋   | 4000/6000 [15:37:05<4:37:23,  8.32s/it][INFO|trainer.py:2700] 2022-12-15 09:34:42,071 >> Saving model checkpoint to ./checkpoint-4000
+[INFO|configuration_utils.py:447] 2022-12-15 09:34:42,072 >> Configuration saved in ./checkpoint-4000/config.json
+[INFO|modeling_utils.py:1680] 2022-12-15 09:34:42,921 >> Model weights saved in ./checkpoint-4000/pytorch_model.bin
+[INFO|feature_extraction_utils.py:368] 2022-12-15 09:34:42,936 >> Feature extractor saved in ./checkpoint-4000/preprocessor_config.json
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