diff --git "a/run-2024-10-25T01:17:54+00:00.log" "b/run-2024-10-25T01:17:54+00:00.log"
new file mode 100644--- /dev/null
+++ "b/run-2024-10-25T01:17:54+00:00.log"
@@ -0,0 +1,1109 @@
+The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.
+
0it [00:00, ?it/s]
0it [00:00, ?it/s]
+/opt/conda/lib/python3.10/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: '/opt/conda/lib/python3.10/site-packages/torchvision/image.so: undefined symbol: _ZN3c1017RegisterOperatorsD1Ev'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
+  warn(
+2024-10-25 01:18:03.423955: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
+2024-10-25 01:18:03.424084: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
+2024-10-25 01:18:03.562275: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
+/opt/conda/lib/python3.10/site-packages/transformers/deepspeed.py:24: FutureWarning: transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/transformers/training_args.py:1525: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead
+  warnings.warn(
+/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884
+  warnings.warn(
+
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+
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+
Running tokenizer on train dataset:   0%|          | 0/92867 [00:00<?, ? examples/s]/opt/conda/lib/python3.10/site-packages/transformers/tokenization_utils_base.py:4126: UserWarning: `as_target_tokenizer` is deprecated and will be removed in v5 of Transformers. You can tokenize your labels by using the argument `text_target` of the regular `__call__` method (either in the same call as your input texts if you use the same keyword arguments, or in a separate call.
+  warnings.warn(
+
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+Saving cached train data ...
+
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+
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+Saving cached validation data ...
+
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+training_args.use_lang_prefix=True
+
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+/opt/conda/lib/python3.10/site-packages/transformers/optimization.py:591: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
+  warnings.warn(
+All 61904 steps, warm_up steps: 200
+wandb: WARNING The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.
+wandb: Currently logged in as: abdiharyadi. Use `wandb login --relogin` to force relogin
+wandb: wandb version 0.18.5 is available!  To upgrade, please run:
+wandb:  $ pip install wandb --upgrade
+wandb: Tracking run with wandb version 0.17.5
+wandb: Run data is saved locally in /kaggle/tmp/amr-tst-indo/AMRBART-id/fine-tune/wandb/run-20241025_012052-278wbs6z
+wandb: Run `wandb offline` to turn off syncing.
+wandb: Syncing run /kaggle/tmp/amr-tst-indo/AMRBART-id/fine-tune/../outputs/mbart-en-id-smaller-fted
+wandb: ⭐️ View project at https://wandb.ai/abdiharyadi/amr-tst
+wandb: 🚀 View run at https://wandb.ai/abdiharyadi/amr-tst/runs/278wbs6z
+
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+  self.pid = os.fork()
+
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{'loss': 3.6995, 'learning_rate': 1e-09, 'epoch': 0.0}
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{'loss': 3.6397, 'learning_rate': 2e-08, 'epoch': 0.01}
+
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{'loss': 3.6775, 'learning_rate': 4e-08, 'epoch': 0.01}
+
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{'loss': 3.8059, 'learning_rate': 6e-08, 'epoch': 0.02}
+
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{'loss': 3.6028, 'learning_rate': 8e-08, 'epoch': 0.02}
+
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{'loss': 3.5831, 'learning_rate': 1e-07, 'epoch': 0.03}
+
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{'loss': 3.4939, 'learning_rate': 1.2e-07, 'epoch': 0.03}
+
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{'loss': 3.4946, 'learning_rate': 1.3999999999999998e-07, 'epoch': 0.04}
+
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{'loss': 3.4607, 'learning_rate': 1.6e-07, 'epoch': 0.04}
+
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{'loss': 3.4372, 'learning_rate': 1.8e-07, 'epoch': 0.05}
+
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{'loss': 3.3843, 'learning_rate': 2e-07, 'epoch': 0.05}
+
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{'loss': 3.4237, 'learning_rate': 1.9996758719045768e-07, 'epoch': 0.06}
+
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{'loss': 3.4052, 'learning_rate': 1.9993517438091531e-07, 'epoch': 0.06}
+
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{'loss': 3.4037, 'learning_rate': 1.99902761571373e-07, 'epoch': 0.07}
+
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{'loss': 3.4114, 'learning_rate': 1.9987034876183066e-07, 'epoch': 0.07}
+
  0%|          | 280/61904 [06:24<23:07:32,  1.35s/it]
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{'loss': 3.4259, 'learning_rate': 1.9983793595228833e-07, 'epoch': 0.08}
+
  0%|          | 300/61904 [06:52<23:41:15,  1.38s/it]
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{'loss': 3.3792, 'learning_rate': 1.9980552314274602e-07, 'epoch': 0.08}
+
  1%|          | 320/61904 [07:19<22:19:21,  1.30s/it]
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{'loss': 3.2866, 'learning_rate': 1.9977311033320368e-07, 'epoch': 0.09}
+
  1%|          | 340/61904 [07:46<22:28:05,  1.31s/it]
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{'loss': 3.3454, 'learning_rate': 1.9974069752366134e-07, 'epoch': 0.09}
+
  1%|          | 360/61904 [08:13<22:35:19,  1.32s/it]
  1%|          | 361/61904 [08:15<23:31:31,  1.38s/it]
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  1%|          | 380/61904 [08:40<23:31:20,  1.38s/it]
                                                      
{'loss': 3.3625, 'learning_rate': 1.9970828471411903e-07, 'epoch': 0.1}
+
  1%|          | 380/61904 [08:40<23:31:20,  1.38s/it]
  1%|          | 381/61904 [08:42<23:57:56,  1.40s/it]
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{'loss': 3.3624, 'learning_rate': 1.9967587190457666e-07, 'epoch': 0.1}
+
  1%|          | 400/61904 [09:08<23:46:34,  1.39s/it]
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{'loss': 3.3077, 'learning_rate': 1.9964345909503435e-07, 'epoch': 0.11}
+
  1%|          | 420/61904 [09:37<24:39:36,  1.44s/it]
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{'loss': 3.274, 'learning_rate': 1.99611046285492e-07, 'epoch': 0.11}
+
  1%|          | 440/61904 [10:04<21:56:06,  1.28s/it]
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{'loss': 3.3271, 'learning_rate': 1.9957863347594967e-07, 'epoch': 0.12}
+
  1%|          | 460/61904 [10:31<22:45:57,  1.33s/it]
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{'loss': 3.2978, 'learning_rate': 1.9954622066640736e-07, 'epoch': 0.12}
+
  1%|          | 480/61904 [10:59<24:03:25,  1.41s/it]
  1%|          | 481/61904 [11:00<23:54:08,  1.40s/it]
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  1%|          | 483/61904 [11:03<23:38:59,  1.39s/it]
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  1%|          | 485/61904 [11:06<23:32:13,  1.38s/it]
  1%|          | 486/61904 [11:07<23:28:54,  1.38s/it]
  1%|          | 487/61904 [11:09<23:09:15,  1.36s/it]
  1%|          | 488/61904 [11:10<24:29:09,  1.44s/it]
  1%|          | 489/61904 [11:12<24:38:43,  1.44s/it]
  1%|          | 490/61904 [11:13<23:41:30,  1.39s/it]
  1%|          | 491/61904 [11:14<23:13:58,  1.36s/it]
  1%|          | 492/61904 [11:16<23:05:11,  1.35s/it]
  1%|          | 493/61904 [11:17<23:36:10,  1.38s/it]
  1%|          | 494/61904 [11:18<23:40:54,  1.39s/it]
  1%|          | 495/61904 [11:20<24:40:00,  1.45s/it]
  1%|          | 496/61904 [11:21<24:00:19,  1.41s/it]
  1%|          | 497/61904 [11:23<23:51:15,  1.40s/it]
  1%|          | 498/61904 [11:24<23:13:03,  1.36s/it]
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  1%|          | 500/61904 [11:27<23:13:42,  1.36s/it]
                                                      
{'loss': 3.3003, 'learning_rate': 1.9951380785686502e-07, 'epoch': 0.13}
+
  1%|          | 500/61904 [11:27<23:13:42,  1.36s/it]
  1%|          | 501/61904 [11:28<23:15:10,  1.36s/it]
  1%|          | 502/61904 [11:30<23:33:36,  1.38s/it]
  1%|          | 503/61904 [11:31<23:11:38,  1.36s/it]
  1%|          | 504/61904 [11:32<23:13:29,  1.36s/it]
  1%|          | 505/61904 [11:34<23:48:49,  1.40s/it]
  1%|          | 506/61904 [11:35<24:12:24,  1.42s/it]
  1%|          | 507/61904 [11:36<23:26:33,  1.37s/it]
  1%|          | 508/61904 [11:38<23:23:20,  1.37s/it]
  1%|          | 509/61904 [11:39<23:22:36,  1.37s/it]
  1%|          | 510/61904 [11:41<23:40:33,  1.39s/it]
  1%|          | 511/61904 [11:42<23:59:19,  1.41s/it]
  1%|          | 512/61904 [11:43<23:27:47,  1.38s/it]
  1%|          | 513/61904 [11:45<24:33:22,  1.44s/it]
  1%|          | 514/61904 [11:46<24:27:23,  1.43s/it]
  1%|          | 515/61904 [11:48<24:21:17,  1.43s/it]
  1%|          | 516/61904 [11:49<24:07:14,  1.41s/it]
  1%|          | 517/61904 [11:51<24:32:09,  1.44s/it]
  1%|          | 518/61904 [11:52<24:15:23,  1.42s/it]
  1%|          | 519/61904 [11:53<23:28:43,  1.38s/it]
  1%|          | 520/61904 [11:55<23:24:00,  1.37s/it]
                                                      
{'loss': 3.2743, 'learning_rate': 1.9948139504732269e-07, 'epoch': 0.13}
+
  1%|          | 520/61904 [11:55<23:24:00,  1.37s/it]
  1%|          | 521/61904 [11:56<23:14:41,  1.36s/it]
  1%|          | 522/61904 [11:57<23:04:14,  1.35s/it]
  1%|          | 523/61904 [11:59<24:01:02,  1.41s/it]
  1%|          | 524/61904 [12:00<24:06:02,  1.41s/it]
  1%|          | 525/61904 [12:02<23:46:39,  1.39s/it]
  1%|          | 526/61904 [12:03<23:40:41,  1.39s/it]
  1%|          | 527/61904 [12:04<23:56:13,  1.40s/it]
  1%|          | 528/61904 [12:06<23:57:33,  1.41s/it]
  1%|          | 529/61904 [12:07<23:53:17,  1.40s/it]
  1%|          | 530/61904 [12:09<24:26:05,  1.43s/it]
  1%|          | 531/61904 [12:10<23:31:17,  1.38s/it]
  1%|          | 532/61904 [12:11<23:35:44,  1.38s/it]
  1%|          | 533/61904 [12:13<23:20:04,  1.37s/it]
  1%|          | 534/61904 [12:14<23:51:36,  1.40s/it]
  1%|          | 535/61904 [12:16<23:24:11,  1.37s/it]
  1%|          | 536/61904 [12:17<23:25:09,  1.37s/it]
  1%|          | 537/61904 [12:18<23:19:35,  1.37s/it]
  1%|          | 538/61904 [12:20<23:22:52,  1.37s/it]
  1%|          | 539/61904 [12:21<23:06:48,  1.36s/it]
  1%|          | 540/61904 [12:22<23:02:28,  1.35s/it]
                                                      
{'loss': 3.3829, 'learning_rate': 1.9944898223778038e-07, 'epoch': 0.14}
+
  1%|          | 540/61904 [12:22<23:02:28,  1.35s/it]
  1%|          | 541/61904 [12:24<22:55:01,  1.34s/it]
  1%|          | 542/61904 [12:25<22:05:02,  1.30s/it]
  1%|          | 543/61904 [12:26<23:11:16,  1.36s/it]
  1%|          | 544/61904 [12:28<22:54:36,  1.34s/it]
  1%|          | 545/61904 [12:29<23:33:52,  1.38s/it]
  1%|          | 546/61904 [12:31<23:51:02,  1.40s/it]
  1%|          | 547/61904 [12:32<23:33:17,  1.38s/it]
  1%|          | 548/61904 [12:33<22:57:21,  1.35s/it]
  1%|          | 549/61904 [12:34<22:50:41,  1.34s/it]
  1%|          | 550/61904 [12:36<23:03:23,  1.35s/it]
  1%|          | 551/61904 [12:37<22:51:17,  1.34s/it]
  1%|          | 552/61904 [12:39<23:05:14,  1.35s/it]
  1%|          | 553/61904 [12:40<23:10:36,  1.36s/it]
  1%|          | 554/61904 [12:41<23:30:14,  1.38s/it]
  1%|          | 555/61904 [12:43<23:47:46,  1.40s/it]
  1%|          | 556/61904 [12:44<23:34:27,  1.38s/it]
  1%|          | 557/61904 [12:45<23:13:15,  1.36s/it]
  1%|          | 558/61904 [12:47<23:08:29,  1.36s/it]
  1%|          | 559/61904 [12:48<22:41:02,  1.33s/it]
  1%|          | 560/61904 [12:49<22:54:53,  1.34s/it]
                                                      
{'loss': 3.2859, 'learning_rate': 1.9941656942823804e-07, 'epoch': 0.14}
+
  1%|          | 560/61904 [12:49<22:54:53,  1.34s/it]
  1%|          | 561/61904 [12:51<22:34:16,  1.32s/it]
  1%|          | 562/61904 [12:52<22:41:05,  1.33s/it]
  1%|          | 563/61904 [12:53<22:29:02,  1.32s/it]
  1%|          | 564/61904 [12:55<23:03:58,  1.35s/it]
  1%|          | 565/61904 [12:56<23:50:51,  1.40s/it]
  1%|          | 566/61904 [12:58<23:33:34,  1.38s/it]
  1%|          | 567/61904 [12:59<23:17:41,  1.37s/it]
  1%|          | 568/61904 [13:00<23:23:15,  1.37s/it]
  1%|          | 569/61904 [13:02<23:14:27,  1.36s/it]
  1%|          | 570/61904 [13:03<23:24:45,  1.37s/it]
  1%|          | 571/61904 [13:04<22:31:48,  1.32s/it]
  1%|          | 572/61904 [13:06<22:28:55,  1.32s/it]
  1%|          | 573/61904 [13:07<22:05:33,  1.30s/it]
  1%|          | 574/61904 [13:08<22:02:36,  1.29s/it]
  1%|          | 575/61904 [13:10<22:25:26,  1.32s/it]
  1%|          | 576/61904 [13:11<22:23:04,  1.31s/it]
  1%|          | 577/61904 [13:12<22:22:14,  1.31s/it]
  1%|          | 578/61904 [13:13<22:05:12,  1.30s/it]
  1%|          | 579/61904 [13:15<22:33:49,  1.32s/it]
  1%|          | 580/61904 [13:16<22:42:42,  1.33s/it]
                                                      
{'loss': 3.2563, 'learning_rate': 1.993841566186957e-07, 'epoch': 0.15}
+
  1%|          | 580/61904 [13:16<22:42:42,  1.33s/it]
  1%|          | 581/61904 [13:17<22:15:16,  1.31s/it]
  1%|          | 582/61904 [13:19<22:04:26,  1.30s/it]
  1%|          | 583/61904 [13:20<22:21:50,  1.31s/it]
  1%|          | 584/61904 [13:21<22:13:44,  1.31s/it]
  1%|          | 585/61904 [13:23<22:49:00,  1.34s/it]
  1%|          | 586/61904 [13:24<22:45:25,  1.34s/it]
  1%|          | 587/61904 [13:25<23:03:44,  1.35s/it]
  1%|          | 588/61904 [13:27<23:20:01,  1.37s/it]
  1%|          | 589/61904 [13:28<23:11:08,  1.36s/it]
  1%|          | 590/61904 [13:30<24:19:09,  1.43s/it]
  1%|          | 591/61904 [13:31<23:42:26,  1.39s/it]
  1%|          | 592/61904 [13:32<23:36:06,  1.39s/it]
  1%|          | 593/61904 [13:34<23:14:13,  1.36s/it]
  1%|          | 594/61904 [13:35<23:26:27,  1.38s/it]
  1%|          | 595/61904 [13:37<23:54:38,  1.40s/it]
  1%|          | 596/61904 [13:38<23:06:56,  1.36s/it]
  1%|          | 597/61904 [13:39<23:08:31,  1.36s/it]
  1%|          | 598/61904 [13:41<23:38:20,  1.39s/it]
  1%|          | 599/61904 [13:42<24:16:27,  1.43s/it]
  1%|          | 600/61904 [13:44<25:06:31,  1.47s/it]
                                                      
{'loss': 3.2624, 'learning_rate': 1.993517438091534e-07, 'epoch': 0.16}
+
  1%|          | 600/61904 [13:44<25:06:31,  1.47s/it]
  1%|          | 601/61904 [13:45<24:46:49,  1.46s/it]
  1%|          | 602/61904 [13:47<24:02:15,  1.41s/it]
  1%|          | 603/61904 [13:48<23:54:04,  1.40s/it]
  1%|          | 604/61904 [13:49<23:32:27,  1.38s/it]
  1%|          | 605/61904 [13:51<23:17:05,  1.37s/it]
  1%|          | 606/61904 [13:52<23:13:51,  1.36s/it]
  1%|          | 607/61904 [13:53<23:07:58,  1.36s/it]
  1%|          | 608/61904 [13:55<23:15:13,  1.37s/it]
  1%|          | 609/61904 [13:56<23:31:24,  1.38s/it]
  1%|          | 610/61904 [13:58<24:06:25,  1.42s/it]
  1%|          | 611/61904 [13:59<23:45:56,  1.40s/it]
  1%|          | 612/61904 [14:00<23:51:59,  1.40s/it]
  1%|          | 613/61904 [14:02<22:52:51,  1.34s/it]
  1%|          | 614/61904 [14:03<23:33:54,  1.38s/it]
  1%|          | 615/61904 [14:04<23:30:14,  1.38s/it]
  1%|          | 616/61904 [14:06<24:26:10,  1.44s/it]
  1%|          | 617/61904 [14:07<23:40:37,  1.39s/it]
  1%|          | 618/61904 [14:09<23:09:47,  1.36s/it]
  1%|          | 619/61904 [14:10<23:20:49,  1.37s/it]
  1%|          | 620/61904 [14:11<22:40:34,  1.33s/it]
                                                      
{'loss': 3.3833, 'learning_rate': 1.9931933099961102e-07, 'epoch': 0.16}
+
  1%|          | 620/61904 [14:11<22:40:34,  1.33s/it]
  1%|          | 621/61904 [14:13<23:11:47,  1.36s/it]
  1%|          | 622/61904 [14:14<23:05:22,  1.36s/it]
  1%|          | 623/61904 [14:15<22:56:14,  1.35s/it]
  1%|          | 624/61904 [14:17<23:03:33,  1.35s/it]
  1%|          | 625/61904 [14:18<23:32:24,  1.38s/it]
  1%|          | 626/61904 [14:20<23:49:53,  1.40s/it]
  1%|          | 627/61904 [14:21<24:16:10,  1.43s/it]
  1%|          | 628/61904 [14:22<23:47:14,  1.40s/it]
  1%|          | 629/61904 [14:24<24:04:02,  1.41s/it]
  1%|          | 630/61904 [14:25<23:54:07,  1.40s/it]
  1%|          | 631/61904 [14:27<23:32:24,  1.38s/it]
  1%|          | 632/61904 [14:28<23:43:59,  1.39s/it]
  1%|          | 633/61904 [14:29<23:12:07,  1.36s/it]
  1%|          | 634/61904 [14:31<23:31:25,  1.38s/it]
  1%|          | 635/61904 [14:32<23:21:47,  1.37s/it]
  1%|          | 636/61904 [14:34<24:02:51,  1.41s/it]
  1%|          | 637/61904 [14:35<24:24:34,  1.43s/it]
  1%|          | 638/61904 [14:36<24:06:36,  1.42s/it]
  1%|          | 639/61904 [14:38<23:30:12,  1.38s/it]
  1%|          | 640/61904 [14:39<23:39:53,  1.39s/it]
                                                      
{'loss': 3.2947, 'learning_rate': 1.992869181900687e-07, 'epoch': 0.17}
+
  1%|          | 640/61904 [14:39<23:39:53,  1.39s/it]
  1%|          | 641/61904 [14:40<23:35:34,  1.39s/it]
  1%|          | 642/61904 [14:42<24:26:47,  1.44s/it]
  1%|          | 643/61904 [14:43<24:37:51,  1.45s/it]
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  1%|          | 646/61904 [14:48<25:09:44,  1.48s/it]
  1%|          | 647/61904 [14:49<24:32:25,  1.44s/it]
  1%|          | 648/61904 [14:51<24:28:46,  1.44s/it]
  1%|          | 649/61904 [14:52<24:45:10,  1.45s/it]
  1%|          | 650/61904 [14:53<23:52:43,  1.40s/it]
  1%|          | 651/61904 [14:55<24:05:41,  1.42s/it]
  1%|          | 652/61904 [14:56<24:06:11,  1.42s/it]
  1%|          | 653/61904 [14:58<23:47:38,  1.40s/it]
  1%|          | 654/61904 [14:59<23:57:32,  1.41s/it]
  1%|          | 655/61904 [15:01<24:18:23,  1.43s/it]
  1%|          | 656/61904 [15:02<23:25:35,  1.38s/it]
  1%|          | 657/61904 [15:03<23:19:00,  1.37s/it]
  1%|          | 658/61904 [15:05<23:48:26,  1.40s/it]
  1%|          | 659/61904 [15:06<23:28:07,  1.38s/it]
  1%|          | 660/61904 [15:07<23:06:34,  1.36s/it]
                                                      
{'loss': 3.2239, 'learning_rate': 1.9925450538052637e-07, 'epoch': 0.17}
+
  1%|          | 660/61904 [15:07<23:06:34,  1.36s/it]
  1%|          | 661/61904 [15:09<23:08:28,  1.36s/it]
  1%|          | 662/61904 [15:10<22:53:01,  1.35s/it]
  1%|          | 663/61904 [15:11<23:26:13,  1.38s/it]
  1%|          | 664/61904 [15:13<23:03:47,  1.36s/it]
  1%|          | 665/61904 [15:14<23:24:48,  1.38s/it]
  1%|          | 666/61904 [15:16<23:41:36,  1.39s/it]
  1%|          | 667/61904 [15:17<23:45:01,  1.40s/it]
  1%|          | 668/61904 [15:18<23:32:52,  1.38s/it]
  1%|          | 669/61904 [15:20<23:38:21,  1.39s/it]
  1%|          | 670/61904 [15:21<23:30:52,  1.38s/it]
  1%|          | 671/61904 [15:23<23:33:24,  1.38s/it]
  1%|          | 672/61904 [15:24<24:22:42,  1.43s/it]
  1%|          | 673/61904 [15:25<24:19:35,  1.43s/it]
  1%|          | 674/61904 [15:27<24:32:21,  1.44s/it]
  1%|          | 675/61904 [15:28<24:38:22,  1.45s/it]
  1%|          | 676/61904 [15:30<24:03:13,  1.41s/it]
  1%|          | 677/61904 [15:31<23:46:13,  1.40s/it]
  1%|          | 678/61904 [15:32<23:32:51,  1.38s/it]
  1%|          | 679/61904 [15:34<23:22:21,  1.37s/it]
  1%|          | 680/61904 [15:35<22:56:44,  1.35s/it]
                                                      
{'loss': 3.2292, 'learning_rate': 1.9922209257098403e-07, 'epoch': 0.18}
+
  1%|          | 680/61904 [15:35<22:56:44,  1.35s/it]
  1%|          | 681/61904 [15:36<23:00:22,  1.35s/it]
  1%|          | 682/61904 [15:38<23:08:58,  1.36s/it]
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  1%|          | 684/61904 [15:41<23:16:00,  1.37s/it]
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  1%|          | 686/61904 [15:43<23:22:35,  1.37s/it]
  1%|          | 687/61904 [15:45<23:40:48,  1.39s/it]
  1%|          | 688/61904 [15:46<23:34:55,  1.39s/it]
  1%|          | 689/61904 [15:48<23:16:29,  1.37s/it]
  1%|          | 690/61904 [15:49<23:19:05,  1.37s/it]
  1%|          | 691/61904 [15:50<23:32:06,  1.38s/it]
  1%|          | 692/61904 [15:52<23:52:08,  1.40s/it]
  1%|          | 693/61904 [15:53<23:25:37,  1.38s/it]
  1%|          | 694/61904 [15:54<23:05:20,  1.36s/it]
  1%|          | 695/61904 [15:56<22:53:30,  1.35s/it]
  1%|          | 696/61904 [15:57<23:01:29,  1.35s/it]
  1%|          | 697/61904 [15:58<22:32:50,  1.33s/it]
  1%|          | 698/61904 [16:00<22:35:31,  1.33s/it]
  1%|          | 699/61904 [16:01<22:27:18,  1.32s/it]
  1%|          | 700/61904 [16:02<22:34:59,  1.33s/it]
                                                      
{'loss': 3.2841, 'learning_rate': 1.9918967976144172e-07, 'epoch': 0.18}
+
  1%|          | 700/61904 [16:02<22:34:59,  1.33s/it]
  1%|          | 701/61904 [16:04<22:56:26,  1.35s/it]
  1%|          | 702/61904 [16:05<23:26:06,  1.38s/it]
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  1%|          | 704/61904 [16:08<24:03:13,  1.41s/it]
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  1%|          | 706/61904 [16:11<24:17:03,  1.43s/it]
  1%|          | 707/61904 [16:12<23:47:40,  1.40s/it]
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{'loss': 3.2612, 'learning_rate': 1.9915726695189938e-07, 'epoch': 0.19}
+
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{'loss': 3.2516, 'learning_rate': 1.9912485414235705e-07, 'epoch': 0.19}
+
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{'loss': 3.2687, 'learning_rate': 1.9909244133281474e-07, 'epoch': 0.2}
+
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{'loss': 3.196, 'learning_rate': 1.9906002852327237e-07, 'epoch': 0.2}
+
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{'loss': 3.2396, 'learning_rate': 1.9902761571373006e-07, 'epoch': 0.21}
+
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{'loss': 3.2147, 'learning_rate': 1.9899520290418775e-07, 'epoch': 0.21}
+
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{'loss': 3.1813, 'learning_rate': 1.9896279009464538e-07, 'epoch': 0.22}
+
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{'loss': 3.2279, 'learning_rate': 1.9893037728510307e-07, 'epoch': 0.22}
+
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{'loss': 3.2183, 'learning_rate': 1.9889796447556073e-07, 'epoch': 0.23}
+
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{'loss': 3.2086, 'learning_rate': 1.988655516660184e-07, 'epoch': 0.23}
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{'loss': 3.1735, 'learning_rate': 1.9883313885647608e-07, 'epoch': 0.24}
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{'loss': 3.2435, 'learning_rate': 1.9880072604693374e-07, 'epoch': 0.24}
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{'loss': 3.1959, 'learning_rate': 1.987683132373914e-07, 'epoch': 0.25}
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{'loss': 3.1981, 'learning_rate': 1.987359004278491e-07, 'epoch': 0.25}
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{'loss': 3.166, 'learning_rate': 1.9870348761830673e-07, 'epoch': 0.26}
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{'loss': 3.2563, 'learning_rate': 1.9867107480876442e-07, 'epoch': 0.26}
+
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{'loss': 3.1861, 'learning_rate': 1.9863866199922208e-07, 'epoch': 0.27}
+
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{'loss': 3.1872, 'learning_rate': 1.9860624918967974e-07, 'epoch': 0.27}
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{'loss': 3.1865, 'learning_rate': 1.9857383638013743e-07, 'epoch': 0.28}
+
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{'loss': 3.2557, 'learning_rate': 1.985414235705951e-07, 'epoch': 0.28}
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{'loss': 3.1534, 'learning_rate': 1.9850901076105275e-07, 'epoch': 0.29}
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{'loss': 3.2257, 'learning_rate': 1.9847659795151044e-07, 'epoch': 0.29}
+
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{'loss': 3.2544, 'learning_rate': 1.984441851419681e-07, 'epoch': 0.3}
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{'loss': 3.1723, 'learning_rate': 1.9841177233242577e-07, 'epoch': 0.3}
+
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{'loss': 3.2413, 'learning_rate': 1.9837935952288345e-07, 'epoch': 0.31}
+
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{'loss': 3.1966, 'learning_rate': 1.983469467133411e-07, 'epoch': 0.32}
+
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{'loss': 3.1888, 'learning_rate': 1.9831453390379878e-07, 'epoch': 0.32}
+
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{'loss': 3.1284, 'learning_rate': 1.9828212109425644e-07, 'epoch': 0.33}
+
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{'loss': 3.2349, 'learning_rate': 1.982497082847141e-07, 'epoch': 0.33}
+
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{'loss': 3.1775, 'learning_rate': 1.982172954751718e-07, 'epoch': 0.34}
+
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{'loss': 3.1479, 'learning_rate': 1.9818488266562945e-07, 'epoch': 0.34}
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{'loss': 3.1342, 'learning_rate': 1.9815246985608711e-07, 'epoch': 0.35}
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{'loss': 3.1436, 'learning_rate': 1.981200570465448e-07, 'epoch': 0.35}
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{'loss': 3.1753, 'learning_rate': 1.9808764423700244e-07, 'epoch': 0.36}
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{'loss': 3.0928, 'learning_rate': 1.9805523142746013e-07, 'epoch': 0.36}
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{'loss': 3.0965, 'learning_rate': 1.9802281861791781e-07, 'epoch': 0.37}
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{'loss': 3.1065, 'learning_rate': 1.9799040580837545e-07, 'epoch': 0.37}
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{'loss': 3.1232, 'learning_rate': 1.9795799299883314e-07, 'epoch': 0.38}
+
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{'loss': 3.2101, 'learning_rate': 1.979255801892908e-07, 'epoch': 0.38}
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{'loss': 3.1625, 'learning_rate': 1.9789316737974846e-07, 'epoch': 0.39}
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{'loss': 3.0927, 'learning_rate': 1.9786075457020615e-07, 'epoch': 0.39}
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{'loss': 3.1796, 'learning_rate': 1.978283417606638e-07, 'epoch': 0.4}
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{'loss': 3.1212, 'learning_rate': 1.9779592895112147e-07, 'epoch': 0.4}
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{'loss': 3.2041, 'learning_rate': 1.9776351614157916e-07, 'epoch': 0.41}
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{'loss': 3.1158, 'learning_rate': 1.977311033320368e-07, 'epoch': 0.41}
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{'loss': 3.1244, 'learning_rate': 1.9769869052249449e-07, 'epoch': 0.42}
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{'loss': 3.1836, 'learning_rate': 1.9766627771295215e-07, 'epoch': 0.42}
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{'loss': 3.1079, 'learning_rate': 1.976338649034098e-07, 'epoch': 0.43}
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{'loss': 3.1643, 'learning_rate': 1.976014520938675e-07, 'epoch': 0.43}
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{'loss': 3.1435, 'learning_rate': 1.9756903928432516e-07, 'epoch': 0.44}
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{'loss': 3.2259, 'learning_rate': 1.9753662647478282e-07, 'epoch': 0.44}
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{'loss': 3.2045, 'learning_rate': 1.975042136652405e-07, 'epoch': 0.45}
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  3%|▎         | 1757/61904 [40:12<23:52:22,  1.43s/it]
  3%|▎         | 1758/61904 [40:13<22:57:50,  1.37s/it]
  3%|▎         | 1759/61904 [40:15<22:41:42,  1.36s/it]
  3%|▎         | 1760/61904 [40:16<22:33:29,  1.35s/it]
                                                       
{'loss': 3.1356, 'learning_rate': 1.9747180085569817e-07, 'epoch': 0.45}
+
  3%|▎         | 1760/61904 [40:16<22:33:29,  1.35s/it]
  3%|▎         | 1761/61904 [40:17<22:09:25,  1.33s/it]
  3%|▎         | 1762/61904 [40:19<22:27:41,  1.34s/it]
  3%|▎         | 1763/61904 [40:20<22:03:30,  1.32s/it]
  3%|▎         | 1764/61904 [40:21<21:35:20,  1.29s/it]
  3%|▎         | 1765/61904 [40:22<22:15:07,  1.33s/it]
  3%|▎         | 1766/61904 [40:24<22:53:49,  1.37s/it]
  3%|▎         | 1767/61904 [40:25<22:59:52,  1.38s/it]
  3%|▎         | 1768/61904 [40:27<23:05:02,  1.38s/it]
  3%|▎         | 1769/61904 [40:28<23:56:31,  1.43s/it]
  3%|▎         | 1770/61904 [40:30<23:51:01,  1.43s/it]
  3%|▎         | 1771/61904 [40:31<23:34:37,  1.41s/it]
  3%|▎         | 1772/61904 [40:32<22:59:40,  1.38s/it]
  3%|▎         | 1773/61904 [40:34<24:05:30,  1.44s/it]
  3%|▎         | 1774/61904 [40:35<23:40:31,  1.42s/it]
  3%|▎         | 1775/61904 [40:37<23:30:44,  1.41s/it]
  3%|▎         | 1776/61904 [40:38<23:22:34,  1.40s/it]
  3%|▎         | 1777/61904 [40:39<23:24:15,  1.40s/it]
  3%|▎         | 1778/61904 [40:41<22:48:54,  1.37s/it]
  3%|▎         | 1779/61904 [40:42<23:35:14,  1.41s/it]
  3%|▎         | 1780/61904 [40:43<22:49:53,  1.37s/it]
                                                       
{'loss': 3.1475, 'learning_rate': 1.9743938804615583e-07, 'epoch': 0.46}
+
  3%|▎         | 1780/61904 [40:43<22:49:53,  1.37s/it]
  3%|▎         | 1781/61904 [40:45<22:38:49,  1.36s/it]
  3%|▎         | 1782/61904 [40:46<22:44:16,  1.36s/it]
  3%|▎         | 1783/61904 [40:48<23:16:30,  1.39s/it]
  3%|▎         | 1784/61904 [40:49<22:20:11,  1.34s/it]
  3%|▎         | 1785/61904 [40:50<21:58:59,  1.32s/it]
  3%|▎         | 1786/61904 [40:52<22:16:16,  1.33s/it]
  3%|▎         | 1787/61904 [40:53<21:51:44,  1.31s/it]
  3%|▎         | 1788/61904 [40:54<22:17:21,  1.33s/it]
  3%|▎         | 1789/61904 [40:56<23:17:40,  1.40s/it]
  3%|▎         | 1790/61904 [40:57<22:23:18,  1.34s/it]
  3%|▎         | 1791/61904 [40:58<22:54:03,  1.37s/it]
  3%|▎         | 1792/61904 [41:00<22:54:33,  1.37s/it]
  3%|▎         | 1793/61904 [41:01<23:17:00,  1.39s/it]
  3%|▎         | 1794/61904 [41:03<23:27:27,  1.40s/it]
  3%|▎         | 1795/61904 [41:04<23:23:56,  1.40s/it]
  3%|▎         | 1796/61904 [41:05<23:18:17,  1.40s/it]
  3%|▎         | 1797/61904 [41:07<22:30:35,  1.35s/it]
  3%|▎         | 1798/61904 [41:08<22:13:12,  1.33s/it]
  3%|▎         | 1799/61904 [41:09<22:03:16,  1.32s/it]
  3%|▎         | 1800/61904 [41:11<22:35:16,  1.35s/it]
                                                       
{'loss': 3.1801, 'learning_rate': 1.9740697523661352e-07, 'epoch': 0.47}
+
  3%|▎         | 1800/61904 [41:11<22:35:16,  1.35s/it]
  3%|▎         | 1801/61904 [41:12<22:49:10,  1.37s/it]
  3%|▎         | 1802/61904 [41:13<22:18:06,  1.34s/it]
  3%|▎         | 1803/61904 [41:15<22:44:29,  1.36s/it]
  3%|▎         | 1804/61904 [41:16<21:57:23,  1.32s/it]
  3%|▎         | 1805/61904 [41:17<21:44:43,  1.30s/it]
  3%|▎         | 1806/61904 [41:19<22:33:13,  1.35s/it]
  3%|▎         | 1807/61904 [41:20<22:02:44,  1.32s/it]
  3%|▎         | 1808/61904 [41:21<22:15:25,  1.33s/it]
  3%|▎         | 1809/61904 [41:23<22:06:40,  1.32s/it]
  3%|▎         | 1810/61904 [41:24<22:19:39,  1.34s/it]
  3%|▎         | 1811/61904 [41:25<22:11:11,  1.33s/it]
  3%|▎         | 1812/61904 [41:27<22:42:44,  1.36s/it]
  3%|▎         | 1813/61904 [41:28<22:54:29,  1.37s/it]
  3%|▎         | 1814/61904 [41:29<22:48:32,  1.37s/it]
  3%|▎         | 1815/61904 [41:31<22:33:05,  1.35s/it]
  3%|▎         | 1816/61904 [41:32<22:08:09,  1.33s/it]
  3%|▎         | 1817/61904 [41:33<21:36:00,  1.29s/it]
  3%|▎         | 1818/61904 [41:35<22:17:58,  1.34s/it]
  3%|▎         | 1819/61904 [41:36<22:55:22,  1.37s/it]
  3%|▎         | 1820/61904 [41:38<23:09:57,  1.39s/it]
                                                       
{'loss': 3.1391, 'learning_rate': 1.9737456242707116e-07, 'epoch': 0.47}
+
  3%|▎         | 1820/61904 [41:38<23:09:57,  1.39s/it]
  3%|▎         | 1821/61904 [41:39<23:13:49,  1.39s/it]
  3%|▎         | 1822/61904 [41:40<22:23:45,  1.34s/it]
  3%|▎         | 1823/61904 [41:42<22:36:24,  1.35s/it]
  3%|▎         | 1824/61904 [41:43<22:49:37,  1.37s/it]
  3%|▎         | 1825/61904 [41:44<22:42:45,  1.36s/it]
  3%|▎         | 1826/61904 [41:46<22:58:23,  1.38s/it]
  3%|▎         | 1827/61904 [41:47<22:52:16,  1.37s/it]
  3%|▎         | 1828/61904 [41:48<22:24:30,  1.34s/it]
  3%|▎         | 1829/61904 [41:50<22:13:47,  1.33s/it]
  3%|▎         | 1830/61904 [41:51<22:15:31,  1.33s/it]
  3%|▎         | 1831/61904 [41:52<22:20:52,  1.34s/it]
  3%|▎         | 1832/61904 [41:54<22:22:07,  1.34s/it]
  3%|▎         | 1833/61904 [41:55<22:19:23,  1.34s/it]
  3%|▎         | 1834/61904 [41:56<22:21:06,  1.34s/it]
  3%|▎         | 1835/61904 [41:58<22:15:25,  1.33s/it]
  3%|▎         | 1836/61904 [41:59<22:48:30,  1.37s/it]
  3%|▎         | 1837/61904 [42:01<23:21:40,  1.40s/it]
  3%|▎         | 1838/61904 [42:02<23:52:39,  1.43s/it]
  3%|▎         | 1839/61904 [42:04<24:18:13,  1.46s/it]
  3%|▎         | 1840/61904 [42:05<23:45:41,  1.42s/it]
                                                       
{'loss': 3.151, 'learning_rate': 1.9734214961752885e-07, 'epoch': 0.48}
+
  3%|▎         | 1840/61904 [42:05<23:45:41,  1.42s/it]
  3%|▎         | 1841/61904 [42:06<24:02:36,  1.44s/it]
  3%|▎         | 1842/61904 [42:08<23:50:20,  1.43s/it]
  3%|▎         | 1843/61904 [42:09<23:36:57,  1.42s/it]
  3%|▎         | 1844/61904 [42:11<23:48:04,  1.43s/it]
  3%|▎         | 1845/61904 [42:12<23:40:10,  1.42s/it]
  3%|▎         | 1846/61904 [42:14<24:57:29,  1.50s/it]
  3%|▎         | 1847/61904 [42:15<24:22:33,  1.46s/it]
  3%|▎         | 1848/61904 [42:16<23:32:09,  1.41s/it]
  3%|▎         | 1849/61904 [42:18<23:46:25,  1.43s/it]
  3%|▎         | 1850/61904 [42:19<22:46:33,  1.37s/it]
  3%|▎         | 1851/61904 [42:20<22:09:32,  1.33s/it]
  3%|▎         | 1852/61904 [42:22<22:01:20,  1.32s/it]
  3%|▎         | 1853/61904 [42:23<22:06:40,  1.33s/it]
  3%|▎         | 1854/61904 [42:24<22:11:56,  1.33s/it]
  3%|▎         | 1855/61904 [42:26<22:21:25,  1.34s/it]
  3%|▎         | 1856/61904 [42:27<21:40:38,  1.30s/it]
  3%|▎         | 1857/61904 [42:28<22:06:59,  1.33s/it]
  3%|▎         | 1858/61904 [42:30<22:00:35,  1.32s/it]
  3%|▎         | 1859/61904 [42:31<22:01:12,  1.32s/it]
  3%|▎         | 1860/61904 [42:32<21:32:35,  1.29s/it]
                                                       
{'loss': 3.1714, 'learning_rate': 1.973097368079865e-07, 'epoch': 0.48}
+
  3%|▎         | 1860/61904 [42:32<21:32:35,  1.29s/it]
  3%|▎         | 1861/61904 [42:33<21:23:12,  1.28s/it]
  3%|▎         | 1862/61904 [42:35<22:01:04,  1.32s/it]
  3%|▎         | 1863/61904 [42:36<22:36:19,  1.36s/it]
  3%|▎         | 1864/61904 [42:38<22:17:06,  1.34s/it]
  3%|▎         | 1865/61904 [42:39<23:07:59,  1.39s/it]
  3%|▎         | 1866/61904 [42:41<23:50:46,  1.43s/it]
  3%|▎         | 1867/61904 [42:42<23:27:06,  1.41s/it]
  3%|▎         | 1868/61904 [42:43<22:55:45,  1.37s/it]
  3%|▎         | 1869/61904 [42:45<23:12:25,  1.39s/it]
  3%|▎         | 1870/61904 [42:46<23:01:23,  1.38s/it]
  3%|▎         | 1871/61904 [42:47<22:35:18,  1.35s/it]
  3%|▎         | 1872/61904 [42:49<22:38:37,  1.36s/it]
  3%|▎         | 1873/61904 [42:50<22:59:07,  1.38s/it]
  3%|▎         | 1874/61904 [42:51<22:13:33,  1.33s/it]
  3%|▎         | 1875/61904 [42:53<21:46:54,  1.31s/it]
  3%|▎         | 1876/61904 [42:54<21:40:24,  1.30s/it]
  3%|▎         | 1877/61904 [42:55<22:14:52,  1.33s/it]
  3%|▎         | 1878/61904 [42:57<22:09:42,  1.33s/it]
  3%|▎         | 1879/61904 [42:58<22:01:31,  1.32s/it]
  3%|▎         | 1880/61904 [42:59<21:48:57,  1.31s/it]
                                                       
{'loss': 3.0818, 'learning_rate': 1.9727732399844417e-07, 'epoch': 0.49}
+
  3%|▎         | 1880/61904 [42:59<21:48:57,  1.31s/it]
  3%|▎         | 1881/61904 [43:00<21:36:46,  1.30s/it]
  3%|▎         | 1882/61904 [43:02<23:10:11,  1.39s/it]
  3%|▎         | 1883/61904 [43:03<22:47:02,  1.37s/it]
  3%|▎         | 1884/61904 [43:05<22:35:51,  1.36s/it]
  3%|▎         | 1885/61904 [43:06<23:08:38,  1.39s/it]
  3%|▎         | 1886/61904 [43:07<22:43:52,  1.36s/it]
  3%|▎         | 1887/61904 [43:09<22:54:50,  1.37s/it]
  3%|▎         | 1888/61904 [43:10<22:17:57,  1.34s/it]
  3%|▎         | 1889/61904 [43:11<22:05:07,  1.32s/it]
  3%|▎         | 1890/61904 [43:13<22:28:19,  1.35s/it]
  3%|▎         | 1891/61904 [43:14<22:35:53,  1.36s/it]
  3%|▎         | 1892/61904 [43:15<21:50:01,  1.31s/it]
  3%|▎         | 1893/61904 [43:17<22:06:59,  1.33s/it]
  3%|▎         | 1894/61904 [43:18<22:17:32,  1.34s/it]
  3%|▎         | 1895/61904 [43:19<22:10:27,  1.33s/it]
  3%|▎         | 1896/61904 [43:21<21:59:21,  1.32s/it]
  3%|▎         | 1897/61904 [43:22<22:14:51,  1.33s/it]
  3%|▎         | 1898/61904 [43:24<22:29:19,  1.35s/it]
  3%|▎         | 1899/61904 [43:25<22:12:45,  1.33s/it]
  3%|▎         | 1900/61904 [43:26<22:47:37,  1.37s/it]
                                                       
{'loss': 3.0955, 'learning_rate': 1.9724491118890186e-07, 'epoch': 0.49}
+
  3%|▎         | 1900/61904 [43:26<22:47:37,  1.37s/it]
  3%|▎         | 1901/61904 [43:28<22:32:28,  1.35s/it]
  3%|▎         | 1902/61904 [43:29<23:03:09,  1.38s/it]
  3%|▎         | 1903/61904 [43:30<22:39:10,  1.36s/it]
  3%|▎         | 1904/61904 [43:32<22:15:43,  1.34s/it]
  3%|▎         | 1905/61904 [43:33<22:07:59,  1.33s/it]
  3%|▎         | 1906/61904 [43:34<21:38:08,  1.30s/it]
  3%|▎         | 1907/61904 [43:35<21:24:34,  1.28s/it]
  3%|▎         | 1908/61904 [43:37<22:00:51,  1.32s/it]
  3%|▎         | 1909/61904 [43:38<22:05:28,  1.33s/it]
  3%|▎         | 1910/61904 [43:39<22:16:13,  1.34s/it]
  3%|▎         | 1911/61904 [43:41<22:45:11,  1.37s/it]
  3%|▎         | 1912/61904 [43:42<23:11:28,  1.39s/it]
  3%|▎         | 1913/61904 [43:44<22:29:12,  1.35s/it]
  3%|▎         | 1914/61904 [43:45<22:49:44,  1.37s/it]
  3%|▎         | 1915/61904 [43:46<23:07:38,  1.39s/it]
  3%|▎         | 1916/61904 [43:48<23:24:56,  1.41s/it]
  3%|▎         | 1917/61904 [43:49<23:14:29,  1.39s/it]
  3%|▎         | 1918/61904 [43:51<22:54:32,  1.37s/it]
  3%|▎         | 1919/61904 [43:52<23:32:59,  1.41s/it]
  3%|▎         | 1920/61904 [43:54<23:21:07,  1.40s/it]
                                                       
{'loss': 3.1134, 'learning_rate': 1.9721249837935952e-07, 'epoch': 0.5}
+
  3%|▎         | 1920/61904 [43:54<23:21:07,  1.40s/it]
  3%|▎         | 1921/61904 [43:55<23:30:01,  1.41s/it]
  3%|▎         | 1922/61904 [43:56<23:16:15,  1.40s/it]
  3%|▎         | 1923/61904 [43:58<24:27:42,  1.47s/it]
  3%|▎         | 1924/61904 [43:59<23:39:00,  1.42s/it]
  3%|▎         | 1925/61904 [44:01<23:06:17,  1.39s/it]
  3%|▎         | 1926/61904 [44:02<23:12:52,  1.39s/it]
  3%|▎         | 1927/61904 [44:04<24:21:10,  1.46s/it]
  3%|▎         | 1928/61904 [44:05<23:30:55,  1.41s/it]
  3%|▎         | 1929/61904 [44:06<23:09:07,  1.39s/it]
  3%|▎         | 1930/61904 [44:08<23:23:24,  1.40s/it]
  3%|▎         | 1931/61904 [44:09<23:05:41,  1.39s/it]
  3%|▎         | 1932/61904 [44:10<23:05:50,  1.39s/it]
  3%|▎         | 1933/61904 [44:12<23:20:13,  1.40s/it]
  3%|▎         | 1934/61904 [44:13<23:16:59,  1.40s/it]
  3%|▎         | 1935/61904 [44:15<22:52:44,  1.37s/it]
  3%|▎         | 1936/61904 [44:16<22:41:57,  1.36s/it]
  3%|▎         | 1937/61904 [44:17<22:20:21,  1.34s/it]
  3%|▎         | 1938/61904 [44:19<22:31:31,  1.35s/it]
  3%|▎         | 1939/61904 [44:20<22:24:33,  1.35s/it]
  3%|▎         | 1940/61904 [44:21<21:51:46,  1.31s/it]
                                                       
{'loss': 3.1444, 'learning_rate': 1.9718008556981718e-07, 'epoch': 0.5}
+
  3%|▎         | 1940/61904 [44:21<21:51:46,  1.31s/it]
  3%|▎         | 1941/61904 [44:22<22:11:01,  1.33s/it]
  3%|▎         | 1942/61904 [44:24<21:34:50,  1.30s/it]
  3%|▎         | 1943/61904 [44:25<22:49:55,  1.37s/it]
  3%|▎         | 1944/61904 [44:27<22:47:40,  1.37s/it]
  3%|▎         | 1945/61904 [44:28<22:57:42,  1.38s/it]
  3%|▎         | 1946/61904 [44:30<23:46:35,  1.43s/it]
  3%|▎         | 1947/61904 [44:31<22:59:36,  1.38s/it]
  3%|▎         | 1948/61904 [44:32<22:38:43,  1.36s/it]
  3%|▎         | 1949/61904 [44:33<22:12:52,  1.33s/it]
  3%|▎         | 1950/61904 [44:35<22:23:41,  1.34s/it]
  3%|▎         | 1951/61904 [44:36<23:01:43,  1.38s/it]
  3%|▎         | 1952/61904 [44:38<23:04:40,  1.39s/it]
  3%|▎         | 1953/61904 [44:39<22:26:16,  1.35s/it]
  3%|▎         | 1954/61904 [44:40<23:01:57,  1.38s/it]
  3%|▎         | 1955/61904 [44:42<23:00:17,  1.38s/it]
  3%|▎         | 1956/61904 [44:43<22:39:21,  1.36s/it]
  3%|▎         | 1957/61904 [44:44<22:02:49,  1.32s/it]
  3%|▎         | 1958/61904 [44:46<22:29:13,  1.35s/it]
  3%|▎         | 1959/61904 [44:47<23:22:43,  1.40s/it]
  3%|▎         | 1960/61904 [44:49<22:50:02,  1.37s/it]
                                                       
{'loss': 3.1412, 'learning_rate': 1.9714767276027487e-07, 'epoch': 0.51}
+
  3%|▎         | 1960/61904 [44:49<22:50:02,  1.37s/it]
  3%|▎         | 1961/61904 [44:50<22:29:00,  1.35s/it]
  3%|▎         | 1962/61904 [44:51<22:44:01,  1.37s/it]
  3%|▎         | 1963/61904 [44:53<22:53:46,  1.38s/it]
  3%|▎         | 1964/61904 [44:54<22:36:34,  1.36s/it]
  3%|▎         | 1965/61904 [44:55<22:11:54,  1.33s/it]
  3%|▎         | 1966/61904 [44:57<22:24:33,  1.35s/it]
  3%|▎         | 1967/61904 [44:58<21:55:28,  1.32s/it]
  3%|▎         | 1968/61904 [44:59<21:53:00,  1.31s/it]
  3%|▎         | 1969/61904 [45:00<21:02:15,  1.26s/it]
  3%|▎         | 1970/61904 [45:02<21:34:50,  1.30s/it]
  3%|▎         | 1971/61904 [45:03<21:46:31,  1.31s/it]
  3%|▎         | 1972/61904 [45:04<22:25:28,  1.35s/it]
  3%|▎         | 1973/61904 [45:06<21:56:55,  1.32s/it]
  3%|▎         | 1974/61904 [45:07<22:39:23,  1.36s/it]
  3%|▎         | 1975/61904 [45:08<22:14:48,  1.34s/it]
  3%|▎         | 1976/61904 [45:10<21:50:09,  1.31s/it]
  3%|▎         | 1977/61904 [45:11<22:11:10,  1.33s/it]
  3%|▎         | 1978/61904 [45:12<21:17:15,  1.28s/it]
  3%|▎         | 1979/61904 [45:14<21:21:50,  1.28s/it]
  3%|▎         | 1980/61904 [45:15<22:24:41,  1.35s/it]
                                                       
{'loss': 3.1569, 'learning_rate': 1.971152599507325e-07, 'epoch': 0.51}
+
  3%|▎         | 1980/61904 [45:15<22:24:41,  1.35s/it]
  3%|▎         | 1981/61904 [45:16<21:53:20,  1.32s/it]
  3%|▎         | 1982/61904 [45:18<21:55:16,  1.32s/it]
  3%|▎         | 1983/61904 [45:19<21:59:18,  1.32s/it]
  3%|▎         | 1984/61904 [45:20<22:07:20,  1.33s/it]
  3%|▎         | 1985/61904 [45:22<22:55:22,  1.38s/it]
  3%|▎         | 1986/61904 [45:23<22:56:42,  1.38s/it]
  3%|▎         | 1987/61904 [45:25<23:48:09,  1.43s/it]
  3%|▎         | 1988/61904 [45:26<24:49:02,  1.49s/it]
  3%|▎         | 1989/61904 [45:28<24:04:30,  1.45s/it]
  3%|▎         | 1990/61904 [45:29<23:26:33,  1.41s/it]
  3%|▎         | 1991/61904 [45:30<23:11:16,  1.39s/it]
  3%|▎         | 1992/61904 [45:32<22:24:29,  1.35s/it]
  3%|▎         | 1993/61904 [45:33<22:45:54,  1.37s/it]
  3%|▎         | 1994/61904 [45:35<23:51:39,  1.43s/it]
  3%|▎         | 1995/61904 [45:36<23:02:45,  1.38s/it]
  3%|▎         | 1996/61904 [45:37<23:03:17,  1.39s/it]
  3%|▎         | 1997/61904 [45:38<22:20:07,  1.34s/it]
  3%|▎         | 1998/61904 [45:40<23:15:24,  1.40s/it]
  3%|▎         | 1999/61904 [45:41<22:50:17,  1.37s/it]
  3%|▎         | 2000/61904 [45:43<23:05:47,  1.39s/it]
                                                       
{'loss': 3.1235, 'learning_rate': 1.970828471411902e-07, 'epoch': 0.52}
+
  3%|▎         | 2000/61904 [45:43<23:05:47,  1.39s/it]
  3%|▎         | 2001/61904 [45:44<23:05:22,  1.39s/it]
  3%|▎         | 2002/61904 [45:46<23:06:10,  1.39s/it]
  3%|▎         | 2003/61904 [45:47<23:05:42,  1.39s/it]
  3%|▎         | 2004/61904 [45:48<22:33:06,  1.36s/it]
  3%|▎         | 2005/61904 [45:50<23:04:58,  1.39s/it]
  3%|▎         | 2006/61904 [45:51<22:18:44,  1.34s/it]
  3%|▎         | 2007/61904 [45:52<22:48:16,  1.37s/it]
  3%|▎         | 2008/61904 [45:54<23:01:25,  1.38s/it]
  3%|▎         | 2009/61904 [45:55<22:24:38,  1.35s/it]
  3%|▎         | 2010/61904 [45:56<22:24:42,  1.35s/it]
  3%|▎         | 2011/61904 [45:58<22:44:12,  1.37s/it]
  3%|▎         | 2012/61904 [45:59<21:58:52,  1.32s/it]
  3%|▎         | 2013/61904 [46:00<22:30:54,  1.35s/it]
  3%|▎         | 2014/61904 [46:02<22:41:11,  1.36s/it]
  3%|▎         | 2015/61904 [46:03<22:19:05,  1.34s/it]
  3%|▎         | 2016/61904 [46:04<22:29:27,  1.35s/it]
  3%|▎         | 2017/61904 [46:06<23:16:30,  1.40s/it]
  3%|▎         | 2018/61904 [46:07<23:00:11,  1.38s/it]
  3%|▎         | 2019/61904 [46:09<22:49:17,  1.37s/it]
  3%|▎         | 2020/61904 [46:10<22:48:48,  1.37s/it]
                                                       
{'loss': 3.0883, 'learning_rate': 1.9705043433164788e-07, 'epoch': 0.52}
+
  3%|▎         | 2020/61904 [46:10<22:48:48,  1.37s/it]
  3%|▎         | 2021/61904 [46:12<23:34:18,  1.42s/it]
  3%|▎         | 2022/61904 [46:13<22:39:36,  1.36s/it]
  3%|▎         | 2023/61904 [46:14<22:41:18,  1.36s/it]
  3%|▎         | 2024/61904 [46:16<22:51:36,  1.37s/it]
  3%|▎         | 2025/61904 [46:17<23:20:54,  1.40s/it]
  3%|▎         | 2026/61904 [46:18<22:40:01,  1.36s/it]
  3%|▎         | 2027/61904 [46:20<22:29:52,  1.35s/it]
  3%|▎         | 2028/61904 [46:21<22:04:34,  1.33s/it]
  3%|▎         | 2029/61904 [46:22<21:54:27,  1.32s/it]
  3%|▎         | 2030/61904 [46:23<21:48:43,  1.31s/it]
  3%|▎         | 2031/61904 [46:25<22:16:56,  1.34s/it]
  3%|▎         | 2032/61904 [46:26<22:47:25,  1.37s/it]
  3%|▎         | 2033/61904 [46:28<23:06:28,  1.39s/it]
  3%|▎         | 2034/61904 [46:29<23:40:05,  1.42s/it]
  3%|▎         | 2035/61904 [46:31<24:15:46,  1.46s/it]
  3%|▎         | 2036/61904 [46:32<24:11:09,  1.45s/it]
  3%|▎         | 2037/61904 [46:34<23:58:04,  1.44s/it]
  3%|▎         | 2038/61904 [46:35<23:07:05,  1.39s/it]
  3%|▎         | 2039/61904 [46:37<24:18:35,  1.46s/it]
  3%|▎         | 2040/61904 [46:38<23:28:58,  1.41s/it]
                                                       
{'loss': 3.0439, 'learning_rate': 1.9701802152210552e-07, 'epoch': 0.53}
+
  3%|▎         | 2040/61904 [46:38<23:28:58,  1.41s/it]
  3%|▎         | 2041/61904 [46:39<24:00:24,  1.44s/it]
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  3%|▎         | 2043/61904 [46:42<23:01:33,  1.38s/it]
  3%|▎         | 2044/61904 [46:43<23:11:27,  1.39s/it]
  3%|▎         | 2045/61904 [46:45<23:26:15,  1.41s/it]
  3%|▎         | 2046/61904 [46:46<23:24:22,  1.41s/it]
  3%|▎         | 2047/61904 [46:48<23:07:43,  1.39s/it]
  3%|▎         | 2048/61904 [46:49<22:46:23,  1.37s/it]
  3%|▎         | 2049/61904 [46:50<22:46:36,  1.37s/it]
  3%|▎         | 2050/61904 [46:52<23:07:29,  1.39s/it]
  3%|▎         | 2051/61904 [46:53<23:19:50,  1.40s/it]
  3%|▎         | 2052/61904 [46:55<24:02:50,  1.45s/it]
  3%|▎         | 2053/61904 [46:56<23:52:14,  1.44s/it]
  3%|▎         | 2054/61904 [46:58<23:59:45,  1.44s/it]
  3%|▎         | 2055/61904 [46:59<23:44:15,  1.43s/it]
  3%|▎         | 2056/61904 [47:00<23:18:29,  1.40s/it]
  3%|▎         | 2057/61904 [47:02<22:55:24,  1.38s/it]
  3%|▎         | 2058/61904 [47:03<23:26:54,  1.41s/it]
  3%|▎         | 2059/61904 [47:04<22:50:34,  1.37s/it]
  3%|▎         | 2060/61904 [47:06<22:50:20,  1.37s/it]
                                                       
{'loss': 3.0707, 'learning_rate': 1.969856087125632e-07, 'epoch': 0.53}
+
  3%|▎         | 2060/61904 [47:06<22:50:20,  1.37s/it]
  3%|▎         | 2061/61904 [47:07<22:56:13,  1.38s/it]
  3%|▎         | 2062/61904 [47:09<22:49:25,  1.37s/it]
  3%|▎         | 2063/61904 [47:10<23:21:11,  1.40s/it]
  3%|▎         | 2064/61904 [47:12<23:44:58,  1.43s/it]
  3%|▎         | 2065/61904 [47:13<23:50:58,  1.43s/it]
  3%|▎         | 2066/61904 [47:14<23:57:34,  1.44s/it]
  3%|▎         | 2067/61904 [47:16<23:25:51,  1.41s/it]
  3%|▎         | 2068/61904 [47:17<22:51:38,  1.38s/it]
  3%|▎         | 2069/61904 [47:18<22:25:59,  1.35s/it]
  3%|▎         | 2070/61904 [47:20<22:59:31,  1.38s/it]
  3%|▎         | 2071/61904 [47:21<22:01:47,  1.33s/it]
  3%|▎         | 2072/61904 [47:22<22:08:51,  1.33s/it]
  3%|▎         | 2073/61904 [47:24<21:42:15,  1.31s/it]
  3%|▎         | 2074/61904 [47:25<22:30:19,  1.35s/it]
  3%|▎         | 2075/61904 [47:27<23:11:12,  1.40s/it]
  3%|▎         | 2076/61904 [47:28<23:12:42,  1.40s/it]
  3%|▎         | 2077/61904 [47:29<22:37:44,  1.36s/it]
  3%|▎         | 2078/61904 [47:31<22:16:49,  1.34s/it]
  3%|▎         | 2079/61904 [47:32<22:31:29,  1.36s/it]
  3%|▎         | 2080/61904 [47:33<22:08:25,  1.33s/it]
                                                       
{'loss': 3.1062, 'learning_rate': 1.9695319590302087e-07, 'epoch': 0.54}
+
  3%|▎         | 2080/61904 [47:33<22:08:25,  1.33s/it]
  3%|▎         | 2081/61904 [47:35<22:06:48,  1.33s/it]
  3%|▎         | 2082/61904 [47:36<21:49:46,  1.31s/it]
  3%|▎         | 2083/61904 [47:37<22:22:12,  1.35s/it]
  3%|▎         | 2084/61904 [47:39<22:26:30,  1.35s/it]
  3%|▎         | 2085/61904 [47:40<22:14:07,  1.34s/it]
  3%|▎         | 2086/61904 [47:41<22:12:29,  1.34s/it]
  3%|▎         | 2087/61904 [47:43<21:53:15,  1.32s/it]
  3%|▎         | 2088/61904 [47:44<22:06:03,  1.33s/it]
  3%|▎         | 2089/61904 [47:45<22:06:31,  1.33s/it]
  3%|▎         | 2090/61904 [47:47<22:21:40,  1.35s/it]
  3%|▎         | 2091/61904 [47:48<22:19:39,  1.34s/it]
  3%|▎         | 2092/61904 [47:49<21:54:48,  1.32s/it]
  3%|▎         | 2093/61904 [47:51<22:00:08,  1.32s/it]
  3%|▎         | 2094/61904 [47:52<22:53:24,  1.38s/it]
  3%|▎         | 2095/61904 [47:53<22:09:16,  1.33s/it]
  3%|▎         | 2096/61904 [47:55<23:50:45,  1.44s/it]
  3%|▎         | 2097/61904 [47:56<23:55:17,  1.44s/it]
  3%|▎         | 2098/61904 [47:58<23:45:04,  1.43s/it]
  3%|▎         | 2099/61904 [47:59<23:17:26,  1.40s/it]
  3%|▎         | 2100/61904 [48:01<23:22:50,  1.41s/it]
                                                       
{'loss': 3.0252, 'learning_rate': 1.9692078309347853e-07, 'epoch': 0.54}
+
  3%|▎         | 2100/61904 [48:01<23:22:50,  1.41s/it]
  3%|▎         | 2101/61904 [48:02<23:22:58,  1.41s/it]
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  3%|▎         | 2103/61904 [48:05<23:23:30,  1.41s/it]
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  3%|▎         | 2107/61904 [48:11<24:13:39,  1.46s/it]
  3%|▎         | 2108/61904 [48:12<23:22:21,  1.41s/it]
  3%|▎         | 2109/61904 [48:13<23:10:07,  1.39s/it]
  3%|▎         | 2110/61904 [48:15<23:49:06,  1.43s/it]
  3%|▎         | 2111/61904 [48:16<23:01:04,  1.39s/it]
  3%|▎         | 2112/61904 [48:17<22:37:55,  1.36s/it]
  3%|▎         | 2113/61904 [48:19<23:19:42,  1.40s/it]
  3%|▎         | 2114/61904 [48:20<22:54:48,  1.38s/it]
  3%|▎         | 2115/61904 [48:22<22:45:32,  1.37s/it]
  3%|▎         | 2116/61904 [48:23<22:58:22,  1.38s/it]
  3%|▎         | 2117/61904 [48:24<22:09:31,  1.33s/it]
  3%|▎         | 2118/61904 [48:26<22:09:26,  1.33s/it]
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  3%|▎         | 2120/61904 [48:28<22:25:40,  1.35s/it]
                                                       
{'loss': 3.0621, 'learning_rate': 1.9688837028393622e-07, 'epoch': 0.55}
+
  3%|▎         | 2120/61904 [48:28<22:25:40,  1.35s/it]
  3%|▎         | 2121/61904 [48:30<22:34:44,  1.36s/it]
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  3%|▎         | 2140/61904 [48:56<22:40:03,  1.37s/it]
                                                       
{'loss': 3.1098, 'learning_rate': 1.9685595747439388e-07, 'epoch': 0.55}
+
  3%|▎         | 2140/61904 [48:56<22:40:03,  1.37s/it]
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{'loss': 3.0647, 'learning_rate': 1.9682354466485154e-07, 'epoch': 0.56}
+
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{'loss': 3.0435, 'learning_rate': 1.9679113185530923e-07, 'epoch': 0.56}
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{'loss': 3.0728, 'learning_rate': 1.9675871904576687e-07, 'epoch': 0.57}
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{'loss': 3.1617, 'learning_rate': 1.9672630623622455e-07, 'epoch': 0.57}
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{'loss': 3.1428, 'learning_rate': 1.9669389342668222e-07, 'epoch': 0.58}
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{'loss': 3.0638, 'learning_rate': 1.9666148061713988e-07, 'epoch': 0.58}
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{'loss': 3.0397, 'learning_rate': 1.9662906780759757e-07, 'epoch': 0.59}
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{'loss': 3.107, 'learning_rate': 1.9659665499805523e-07, 'epoch': 0.59}
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{'loss': 3.0346, 'learning_rate': 1.965642421885129e-07, 'epoch': 0.6}
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{'loss': 3.129, 'learning_rate': 1.9653182937897058e-07, 'epoch': 0.6}
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{'loss': 3.1397, 'learning_rate': 1.9649941656942821e-07, 'epoch': 0.61}
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{'loss': 3.1279, 'learning_rate': 1.964670037598859e-07, 'epoch': 0.62}
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{'loss': 3.0967, 'learning_rate': 1.9643459095034356e-07, 'epoch': 0.62}
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{'loss': 3.1463, 'learning_rate': 1.9640217814080123e-07, 'epoch': 0.63}
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{'loss': 3.1406, 'learning_rate': 1.9636976533125891e-07, 'epoch': 0.63}
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{'loss': 3.082, 'learning_rate': 1.9633735252171658e-07, 'epoch': 0.64}
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{'loss': 3.1255, 'learning_rate': 1.9630493971217424e-07, 'epoch': 0.64}
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{'loss': 3.1521, 'learning_rate': 1.9627252690263193e-07, 'epoch': 0.65}
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{'loss': 3.0601, 'learning_rate': 1.9624011409308956e-07, 'epoch': 0.65}
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{'loss': 3.0209, 'learning_rate': 1.9620770128354725e-07, 'epoch': 0.66}
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{'loss': 3.1306, 'learning_rate': 1.9617528847400494e-07, 'epoch': 0.66}
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{'loss': 3.0942, 'learning_rate': 1.9614287566446257e-07, 'epoch': 0.67}
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{'loss': 3.0927, 'learning_rate': 1.9611046285492026e-07, 'epoch': 0.67}
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{'loss': 3.0501, 'learning_rate': 1.9607805004537792e-07, 'epoch': 0.68}
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{'loss': 3.0339, 'learning_rate': 1.9604563723583559e-07, 'epoch': 0.68}
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{'loss': 3.03, 'learning_rate': 1.9601322442629327e-07, 'epoch': 0.69}
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{'loss': 3.0811, 'learning_rate': 1.9598081161675094e-07, 'epoch': 0.69}
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{'loss': 3.0863, 'learning_rate': 1.959483988072086e-07, 'epoch': 0.7}
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{'loss': 3.0027, 'learning_rate': 1.9591598599766629e-07, 'epoch': 0.7}
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{'loss': 3.0467, 'learning_rate': 1.9588357318812392e-07, 'epoch': 0.71}
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{'loss': 3.1305, 'learning_rate': 1.958511603785816e-07, 'epoch': 0.71}
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{'loss': 3.0583, 'learning_rate': 1.9581874756903927e-07, 'epoch': 0.72}
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{'loss': 3.0924, 'learning_rate': 1.9578633475949693e-07, 'epoch': 0.72}
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{'loss': 3.0459, 'learning_rate': 1.9575392194995462e-07, 'epoch': 0.73}
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{'loss': 3.0549, 'learning_rate': 1.9572150914041228e-07, 'epoch': 0.73}
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{'loss': 3.1416, 'learning_rate': 1.9568909633086995e-07, 'epoch': 0.74}
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{'loss': 3.084, 'learning_rate': 1.9565668352132763e-07, 'epoch': 0.74}
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{'loss': 3.1049, 'learning_rate': 1.9562427071178527e-07, 'epoch': 0.75}
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{'loss': 3.1135, 'learning_rate': 1.9559185790224296e-07, 'epoch': 0.75}
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{'loss': 3.0329, 'learning_rate': 1.9555944509270062e-07, 'epoch': 0.76}
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{'loss': 3.0465, 'learning_rate': 1.9552703228315828e-07, 'epoch': 0.76}
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{'loss': 3.0079, 'learning_rate': 1.9549461947361597e-07, 'epoch': 0.77}
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{'loss': 3.0706, 'learning_rate': 1.9546220666407363e-07, 'epoch': 0.78}
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{'loss': 2.9727, 'learning_rate': 1.954297938545313e-07, 'epoch': 0.78}
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{'loss': 3.0593, 'learning_rate': 1.9539738104498898e-07, 'epoch': 0.79}
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{'loss': 3.0734, 'learning_rate': 1.9536496823544662e-07, 'epoch': 0.79}
+
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{'loss': 3.0553, 'learning_rate': 1.953325554259043e-07, 'epoch': 0.8}
+
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{'loss': 3.0315, 'learning_rate': 1.9530014261636197e-07, 'epoch': 0.8}
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{'loss': 3.086, 'learning_rate': 1.9526772980681963e-07, 'epoch': 0.81}
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{'loss': 3.046, 'learning_rate': 1.9523531699727732e-07, 'epoch': 0.81}
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{'loss': 2.9981, 'learning_rate': 1.9520290418773498e-07, 'epoch': 0.82}
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{'loss': 3.0827, 'learning_rate': 1.9517049137819264e-07, 'epoch': 0.82}
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{'loss': 3.0582, 'learning_rate': 1.9513807856865033e-07, 'epoch': 0.83}
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{'loss': 3.0574, 'learning_rate': 1.95105665759108e-07, 'epoch': 0.83}
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{'loss': 3.0666, 'learning_rate': 1.9507325294956565e-07, 'epoch': 0.84}
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{'loss': 3.1107, 'learning_rate': 1.9504084014002334e-07, 'epoch': 0.84}
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{'loss': 2.9972, 'learning_rate': 1.9500842733048098e-07, 'epoch': 0.85}
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{'loss': 3.0628, 'learning_rate': 1.9497601452093867e-07, 'epoch': 0.85}
+
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{'loss': 3.0148, 'learning_rate': 1.9494360171139633e-07, 'epoch': 0.86}
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{'loss': 3.0553, 'learning_rate': 1.94911188901854e-07, 'epoch': 0.86}
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{'loss': 2.9124, 'learning_rate': 1.9487877609231168e-07, 'epoch': 0.87}
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{'loss': 2.9625, 'learning_rate': 1.9484636328276934e-07, 'epoch': 0.87}
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{'loss': 2.9542, 'learning_rate': 1.94813950473227e-07, 'epoch': 0.88}
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{'loss': 3.0042, 'learning_rate': 1.947815376636847e-07, 'epoch': 0.88}
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{'loss': 3.002, 'learning_rate': 1.9474912485414233e-07, 'epoch': 0.89}
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{'loss': 3.0223, 'learning_rate': 1.9471671204460001e-07, 'epoch': 0.89}
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{'loss': 3.0038, 'learning_rate': 1.946842992350577e-07, 'epoch': 0.9}
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{'loss': 3.0014, 'learning_rate': 1.9465188642551534e-07, 'epoch': 0.9}
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{'loss': 3.0553, 'learning_rate': 1.9461947361597303e-07, 'epoch': 0.91}
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{'loss': 3.0522, 'learning_rate': 1.945870608064307e-07, 'epoch': 0.91}
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{'loss': 3.0365, 'learning_rate': 1.9455464799688835e-07, 'epoch': 0.92}
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{'loss': 3.0001, 'learning_rate': 1.9452223518734604e-07, 'epoch': 0.93}
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{'loss': 3.0292, 'learning_rate': 1.944898223778037e-07, 'epoch': 0.93}
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{'loss': 3.1199, 'learning_rate': 1.9445740956826136e-07, 'epoch': 0.94}
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{'loss': 3.0552, 'learning_rate': 1.9442499675871905e-07, 'epoch': 0.94}
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{'loss': 3.0105, 'learning_rate': 1.9439258394917669e-07, 'epoch': 0.95}
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{'loss': 2.989, 'learning_rate': 1.9436017113963437e-07, 'epoch': 0.95}
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{'loss': 3.0091, 'learning_rate': 1.9432775833009204e-07, 'epoch': 0.96}
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{'loss': 3.0251, 'learning_rate': 1.942953455205497e-07, 'epoch': 0.96}
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{'loss': 3.0071, 'learning_rate': 1.9426293271100739e-07, 'epoch': 0.97}
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{'loss': 2.9836, 'learning_rate': 1.9423051990146505e-07, 'epoch': 0.97}
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{'loss': 2.9818, 'learning_rate': 1.941981070919227e-07, 'epoch': 0.98}
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{'loss': 3.1245, 'learning_rate': 1.941656942823804e-07, 'epoch': 0.98}
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{'loss': 3.0495, 'learning_rate': 1.9413328147283806e-07, 'epoch': 0.99}
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{'loss': 3.0594, 'learning_rate': 1.9410086866329572e-07, 'epoch': 0.99}
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{'loss': 3.0219, 'learning_rate': 1.940684558537534e-07, 'epoch': 1.0}
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+{'max_length': 384, 'max_gen_length': 380, 'num_beams': 5}
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100%|██████████| 861/861 [31:26<00:00,  2.85s/it]{'score': 0.07411019411915373, 'counts': [9147, 351, 100, 29], 'totals': [421736, 420014, 418292, 416570], 'precisions': [2.1688923876548363, 0.0835686429499969, 0.023906744570778307, 0.006961615094701971], 'bp': 1.0, 'sys_len': 421736, 'ref_len': 32555}
+
                                                         
+
                                                 
{'eval_loss': 2.979813814163208, 'eval_bleu': 0.0741, 'eval_gen_len': 114.8177, 'eval_runtime': 1993.3083, 'eval_samples_per_second': 0.864, 'eval_steps_per_second': 0.432, 'epoch': 1.0}
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                                                 [WARNING|configuration_utils.py:448] 2024-10-25 03:22:47,281 >> Some non-default generation parameters are set in the model config. These should go into a GenerationConfig file (https://huggingface.co/docs/transformers/generation_strategies#save-a-custom-decoding-strategy-with-your-model) instead. This warning will be raised to an exception in v4.41.
+Non-default generation parameters: {'max_length': 200, 'early_stopping': True, 'num_beams': 5, 'forced_eos_token_id': 2}
+/opt/conda/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.
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{'loss': 3.0219, 'learning_rate': 1.9403604304421105e-07, 'epoch': 1.0}
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{'loss': 3.0378, 'learning_rate': 1.9400363023466873e-07, 'epoch': 1.01}
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