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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()`.
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/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(
Generating train split: 0 examples [00:00, ? examples/s]
Generating train split: 1237 examples [00:00, 12341.15 examples/s]
Generating train split: 2264 examples [00:00, 14207.36 examples/s]
Generating validation split: 0 examples [00:00, ? examples/s]
Generating validation split: 30 examples [00:00, 8467.64 examples/s]
Running tokenizer on train dataset: 0%| | 0/2264 [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(
Running tokenizer on train dataset: 44%|████▍ | 1000/2264 [00:00<00:00, 2109.03 examples/s]
Running tokenizer on train dataset: 88%|████████▊ | 2000/2264 [00:01<00:00, 1672.35 examples/s]
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Running tokenizer on train dataset: 100%|██████████| 2264/2264 [00:01<00:00, 1679.44 examples/s]
Saving cached train data ...
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Saving cached validation data ...
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Saving the dataset (1/1 shards): 100%|██████████| 30/30 [00:00<00:00, 7422.67 examples/s]
Downloading builder script: 0%| | 0.00/8.15k [00:00<?, ?B/s]
Downloading builder script: 100%|██████████| 8.15k/8.15k [00:00<00:00, 17.6MB/s]
/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 57600 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 0.18.3 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/working/amr-tst-indo/AMRBART-id/fine-tune/wandb/run-20241002_065356-1wo93oi5
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run /kaggle/working/amr-tst-indo/AMRBART-id/fine-tune/../outputs/mbart-en-id-smaller-fted-amr-generation-v2-fted
wandb: ⭐️ View project at https://wandb.ai/abdiharyadi/amr-tst
wandb: 🚀 View run at https://wandb.ai/abdiharyadi/amr-tst/runs/1wo93oi5
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self.pid = os.fork()
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{'loss': 1.9858, 'learning_rate': 9.996864111498258e-07, 'epoch': 2.43}
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{'loss': 1.9373, 'learning_rate': 9.993728222996515e-07, 'epoch': 2.65}
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{'loss': 1.9146, 'learning_rate': 9.990592334494773e-07, 'epoch': 2.87}
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{'loss': 1.8665, 'learning_rate': 9.987456445993032e-07, 'epoch': 3.09}
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{'loss': 1.9419, 'learning_rate': 9.98432055749129e-07, 'epoch': 3.31}
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{'loss': 1.8332, 'learning_rate': 9.981184668989545e-07, 'epoch': 3.53}
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{'loss': 1.8876, 'learning_rate': 9.96864111498258e-07, 'epoch': 4.42}
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{'loss': 1.8593, 'learning_rate': 9.965505226480835e-07, 'epoch': 4.64}
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{'loss': 1.8878, 'learning_rate': 9.95609756097561e-07, 'epoch': 5.3}
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{'loss': 1.8985, 'learning_rate': 9.952961672473868e-07, 'epoch': 5.52}
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{'loss': 1.84, 'learning_rate': 9.949825783972126e-07, 'epoch': 5.74}
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{'loss': 1.8468, 'learning_rate': 9.946689895470383e-07, 'epoch': 5.96}
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{'loss': 1.8469, 'learning_rate': 9.94355400696864e-07, 'epoch': 6.18}
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{'loss': 1.8528, 'learning_rate': 9.940418118466898e-07, 'epoch': 6.4}
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{'loss': 1.8269, 'learning_rate': 9.937282229965156e-07, 'epoch': 6.62}
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{'loss': 1.8324, 'learning_rate': 9.934146341463415e-07, 'epoch': 6.84}
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{'loss': 1.808, 'learning_rate': 9.931010452961673e-07, 'epoch': 7.06}
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{'loss': 1.9003, 'learning_rate': 9.92787456445993e-07, 'epoch': 7.28}
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{'loss': 1.908, 'learning_rate': 9.924738675958186e-07, 'epoch': 7.51}
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{'loss': 1.8165, 'learning_rate': 9.921602787456445e-07, 'epoch': 7.73}
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{'loss': 1.7924, 'learning_rate': 9.918466898954704e-07, 'epoch': 7.95}
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{'loss': 1.8275, 'learning_rate': 9.915331010452962e-07, 'epoch': 8.17}
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{'loss': 1.8033, 'learning_rate': 9.912195121951219e-07, 'epoch': 8.39}
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{'loss': 1.8283, 'learning_rate': 9.909059233449477e-07, 'epoch': 8.61}
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{'loss': 1.8361, 'learning_rate': 9.905923344947734e-07, 'epoch': 8.83}
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{'loss': 1.8264, 'learning_rate': 9.902787456445992e-07, 'epoch': 9.05}
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{'loss': 1.8157, 'learning_rate': 9.89965156794425e-07, 'epoch': 9.27}
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{'loss': 1.8038, 'learning_rate': 9.89651567944251e-07, 'epoch': 9.49}
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{'loss': 1.8495, 'learning_rate': 9.893379790940768e-07, 'epoch': 9.71}
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{'loss': 1.8379, 'learning_rate': 9.890243902439024e-07, 'epoch': 9.93}
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{'loss': 1.807, 'learning_rate': 9.88710801393728e-07, 'epoch': 10.15}
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{'loss': 1.7354, 'learning_rate': 9.88397212543554e-07, 'epoch': 10.38}
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{'loss': 1.7909, 'learning_rate': 9.880836236933798e-07, 'epoch': 10.6}
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{'loss': 1.8504, 'learning_rate': 9.877700348432054e-07, 'epoch': 10.82}
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{'loss': 1.7565, 'learning_rate': 9.874564459930313e-07, 'epoch': 11.04}
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{'loss': 1.8464, 'learning_rate': 9.871428571428572e-07, 'epoch': 11.26}
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{'loss': 1.7463, 'learning_rate': 9.868292682926828e-07, 'epoch': 11.48}
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{'loss': 1.8286, 'learning_rate': 9.865156794425087e-07, 'epoch': 11.7}
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{'loss': 1.7821, 'learning_rate': 9.862020905923345e-07, 'epoch': 11.92}
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{'loss': 1.8069, 'learning_rate': 9.858885017421604e-07, 'epoch': 12.14}
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{'loss': 1.7943, 'learning_rate': 9.85574912891986e-07, 'epoch': 12.36}
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{'loss': 1.7535, 'learning_rate': 9.852613240418117e-07, 'epoch': 12.58}
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{'loss': 1.7881, 'learning_rate': 9.849477351916375e-07, 'epoch': 12.8}
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{'loss': 1.815, 'learning_rate': 9.846341463414634e-07, 'epoch': 13.02}
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{'loss': 1.8258, 'learning_rate': 9.843205574912892e-07, 'epoch': 13.25}
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{'loss': 1.7228, 'learning_rate': 9.840069686411149e-07, 'epoch': 13.47}
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{'loss': 1.7866, 'learning_rate': 9.836933797909407e-07, 'epoch': 13.69}
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{'loss': 1.7871, 'learning_rate': 9.833797909407664e-07, 'epoch': 13.91}
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{'loss': 1.7238, 'learning_rate': 9.830662020905923e-07, 'epoch': 14.13}
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{'loss': 1.805, 'learning_rate': 9.827526132404181e-07, 'epoch': 14.35}
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{'loss': 1.7909, 'learning_rate': 9.82439024390244e-07, 'epoch': 14.57}
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{'loss': 1.7861, 'learning_rate': 9.821254355400698e-07, 'epoch': 14.79}
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{'loss': 1.7651, 'learning_rate': 9.818118466898953e-07, 'epoch': 15.01}
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{'loss': 1.7666, 'learning_rate': 9.814982578397211e-07, 'epoch': 15.23}
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{'loss': 1.7159, 'learning_rate': 9.81184668989547e-07, 'epoch': 15.45}
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{'loss': 1.7701, 'learning_rate': 9.808710801393728e-07, 'epoch': 15.67}
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{'loss': 1.7805, 'learning_rate': 9.802439024390243e-07, 'epoch': 16.11}
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{'loss': 1.7711, 'learning_rate': 9.799303135888502e-07, 'epoch': 16.34}
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{'loss': 1.6767, 'learning_rate': 9.796167247386758e-07, 'epoch': 16.56}
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{'loss': 1.722, 'learning_rate': 9.793031358885017e-07, 'epoch': 16.78}
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{'loss': 1.783, 'learning_rate': 9.789895470383276e-07, 'epoch': 17.0}
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{'loss': 1.7233, 'learning_rate': 9.786759581881534e-07, 'epoch': 17.22}
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{'loss': 1.7209, 'learning_rate': 9.78362369337979e-07, 'epoch': 17.44}
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{'loss': 1.6968, 'learning_rate': 9.780487804878047e-07, 'epoch': 17.66}
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{'loss': 1.6818, 'learning_rate': 9.777351916376306e-07, 'epoch': 17.88}
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{'loss': 1.7819, 'learning_rate': 9.774216027874564e-07, 'epoch': 18.1}
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{'loss': 1.7445, 'learning_rate': 9.771080139372823e-07, 'epoch': 18.32}
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{'loss': 1.6888, 'learning_rate': 9.76794425087108e-07, 'epoch': 18.54}
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{'loss': 1.7562, 'learning_rate': 9.764808362369338e-07, 'epoch': 18.76}
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{'loss': 1.7368, 'learning_rate': 9.761672473867594e-07, 'epoch': 18.98}
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{'loss': 1.6816, 'learning_rate': 9.758536585365853e-07, 'epoch': 19.21}
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{'loss': 1.7307, 'learning_rate': 9.755400696864111e-07, 'epoch': 19.43}
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{'loss': 1.7313, 'learning_rate': 9.75226480836237e-07, 'epoch': 19.65}
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{'loss': 1.7407, 'learning_rate': 9.749128919860627e-07, 'epoch': 19.87}
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{'loss': 1.6885, 'learning_rate': 9.745993031358883e-07, 'epoch': 20.09}
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{'loss': 1.6741, 'learning_rate': 9.742857142857142e-07, 'epoch': 20.31}
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{'loss': 1.6958, 'learning_rate': 9.7397212543554e-07, 'epoch': 20.53}
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{'loss': 1.7036, 'learning_rate': 9.736585365853659e-07, 'epoch': 20.75}
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{'loss': 1.7489, 'learning_rate': 9.733449477351917e-07, 'epoch': 20.97}
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3%|▎ | 1907/57600 [20:02<9:37:41, 1.61it/s]
3%|▎ | 1908/57600 [20:02<9:44:55, 1.59it/s]
3%|▎ | 1909/57600 [20:03<9:29:36, 1.63it/s]
3%|▎ | 1910/57600 [20:03<9:26:43, 1.64it/s]
3%|▎ | 1911/57600 [20:04<9:26:15, 1.64it/s]
3%|▎ | 1912/57600 [20:05<10:01:59, 1.54it/s]
3%|▎ | 1913/57600 [20:05<10:04:48, 1.53it/s]
3%|▎ | 1914/57600 [20:06<9:57:18, 1.55it/s]
3%|▎ | 1915/57600 [20:07<9:49:46, 1.57it/s]
3%|▎ | 1916/57600 [20:07<9:45:16, 1.59it/s]
3%|▎ | 1917/57600 [20:08<9:51:03, 1.57it/s]
3%|▎ | 1918/57600 [20:09<10:00:37, 1.55it/s]
3%|▎ | 1919/57600 [20:09<9:44:17, 1.59it/s]
3%|▎ | 1920/57600 [20:10<9:41:07, 1.60it/s]
{'loss': 1.7719, 'learning_rate': 9.730313588850174e-07, 'epoch': 21.19}
3%|▎ | 1920/57600 [20:10<9:41:07, 1.60it/s]
3%|▎ | 1921/57600 [20:10<9:59:45, 1.55it/s]
3%|▎ | 1922/57600 [20:11<10:09:12, 1.52it/s]
3%|▎ | 1923/57600 [20:12<10:02:41, 1.54it/s]
3%|▎ | 1924/57600 [20:12<10:02:38, 1.54it/s]
3%|▎ | 1925/57600 [20:13<9:52:08, 1.57it/s]
3%|▎ | 1926/57600 [20:14<9:46:19, 1.58it/s]
3%|▎ | 1927/57600 [20:14<9:55:55, 1.56it/s]
3%|▎ | 1928/57600 [20:15<9:52:57, 1.56it/s]
3%|▎ | 1929/57600 [20:16<9:34:03, 1.62it/s]
3%|▎ | 1930/57600 [20:16<10:12:25, 1.52it/s]
3%|▎ | 1931/57600 [20:17<9:56:33, 1.56it/s]
3%|▎ | 1932/57600 [20:17<9:41:28, 1.60it/s]
3%|▎ | 1933/57600 [20:18<9:34:26, 1.62it/s]
3%|▎ | 1934/57600 [20:19<9:19:34, 1.66it/s]
3%|▎ | 1935/57600 [20:19<9:19:11, 1.66it/s]
3%|▎ | 1936/57600 [20:20<9:31:40, 1.62it/s]
3%|▎ | 1937/57600 [20:21<9:41:02, 1.60it/s]
3%|▎ | 1938/57600 [20:21<9:31:10, 1.62it/s]
3%|▎ | 1939/57600 [20:22<9:53:08, 1.56it/s]
3%|▎ | 1940/57600 [20:22<9:37:15, 1.61it/s]
{'loss': 1.7401, 'learning_rate': 9.72717770034843e-07, 'epoch': 21.41}
3%|▎ | 1940/57600 [20:22<9:37:15, 1.61it/s]
3%|▎ | 1941/57600 [20:23<9:21:46, 1.65it/s]
3%|▎ | 1942/57600 [20:24<9:36:46, 1.61it/s]
3%|▎ | 1943/57600 [20:24<9:43:24, 1.59it/s]
3%|▎ | 1944/57600 [20:25<9:35:00, 1.61it/s]
3%|▎ | 1945/57600 [20:25<9:30:46, 1.63it/s]
3%|▎ | 1946/57600 [20:26<9:39:19, 1.60it/s]
3%|▎ | 1947/57600 [20:27<9:52:01, 1.57it/s]
3%|▎ | 1948/57600 [20:27<9:47:55, 1.58it/s]
3%|▎ | 1949/57600 [20:28<10:02:29, 1.54it/s]
3%|▎ | 1950/57600 [20:29<9:40:27, 1.60it/s]
3%|▎ | 1951/57600 [20:29<9:49:05, 1.57it/s]
3%|▎ | 1952/57600 [20:30<9:31:40, 1.62it/s]
3%|▎ | 1953/57600 [20:31<10:01:15, 1.54it/s]
3%|▎ | 1954/57600 [20:31<10:03:12, 1.54it/s]
3%|▎ | 1955/57600 [20:32<9:46:03, 1.58it/s]
3%|▎ | 1956/57600 [20:33<9:43:26, 1.59it/s]
3%|▎ | 1957/57600 [20:33<10:02:13, 1.54it/s]
3%|▎ | 1958/57600 [20:34<9:57:31, 1.55it/s]
3%|▎ | 1959/57600 [20:34<9:51:08, 1.57it/s]
3%|▎ | 1960/57600 [20:35<9:48:59, 1.57it/s]
{'loss': 1.7247, 'learning_rate': 9.724041811846689e-07, 'epoch': 21.63}
3%|▎ | 1960/57600 [20:35<9:48:59, 1.57it/s]
3%|▎ | 1961/57600 [20:36<9:53:42, 1.56it/s]
3%|▎ | 1962/57600 [20:36<9:51:20, 1.57it/s]
3%|▎ | 1963/57600 [20:37<10:26:12, 1.48it/s]
3%|▎ | 1964/57600 [20:38<9:59:12, 1.55it/s]
3%|▎ | 1965/57600 [20:38<10:01:37, 1.54it/s]
3%|▎ | 1966/57600 [20:39<9:51:49, 1.57it/s]
3%|▎ | 1967/57600 [20:40<9:38:18, 1.60it/s]
3%|▎ | 1968/57600 [20:40<9:33:48, 1.62it/s]
3%|▎ | 1969/57600 [20:41<9:19:28, 1.66it/s]
3%|▎ | 1970/57600 [20:41<9:20:56, 1.65it/s]
3%|▎ | 1971/57600 [20:42<9:36:53, 1.61it/s]
3%|▎ | 1972/57600 [20:43<9:42:33, 1.59it/s]
3%|▎ | 1973/57600 [20:43<9:27:01, 1.64it/s]
3%|▎ | 1974/57600 [20:44<9:18:21, 1.66it/s]
3%|▎ | 1975/57600 [20:44<9:09:24, 1.69it/s]
3%|▎ | 1976/57600 [20:45<9:26:32, 1.64it/s]
3%|▎ | 1977/57600 [20:46<9:29:43, 1.63it/s]
3%|▎ | 1978/57600 [20:46<9:30:33, 1.62it/s]
3%|▎ | 1979/57600 [20:47<9:49:06, 1.57it/s]
3%|▎ | 1980/57600 [20:48<9:37:45, 1.60it/s]
{'loss': 1.7175, 'learning_rate': 9.720905923344947e-07, 'epoch': 21.85}
3%|▎ | 1980/57600 [20:48<9:37:45, 1.60it/s]
3%|▎ | 1981/57600 [20:48<9:24:23, 1.64it/s]
3%|▎ | 1982/57600 [20:49<9:33:06, 1.62it/s]
3%|▎ | 1983/57600 [20:49<9:49:48, 1.57it/s]
3%|▎ | 1984/57600 [20:50<10:04:48, 1.53it/s]
3%|▎ | 1985/57600 [20:51<9:47:17, 1.58it/s]
3%|▎ | 1986/57600 [20:51<9:33:20, 1.62it/s]
3%|▎ | 1987/57600 [20:52<9:27:01, 1.63it/s]
3%|▎ | 1988/57600 [20:53<9:37:40, 1.60it/s]
3%|▎ | 1989/57600 [20:53<9:26:25, 1.64it/s]
3%|▎ | 1990/57600 [20:54<9:23:21, 1.65it/s]
3%|▎ | 1991/57600 [20:54<9:22:29, 1.65it/s]
3%|▎ | 1992/57600 [20:55<9:26:22, 1.64it/s]
3%|▎ | 1993/57600 [20:56<9:48:02, 1.58it/s]
3%|▎ | 1994/57600 [20:56<10:16:04, 1.50it/s]
3%|▎ | 1995/57600 [20:57<10:06:36, 1.53it/s]
3%|▎ | 1996/57600 [20:58<9:53:30, 1.56it/s]
3%|▎ | 1997/57600 [20:58<9:42:54, 1.59it/s]
3%|▎ | 1998/57600 [20:59<9:47:49, 1.58it/s]
3%|▎ | 1999/57600 [20:59<9:38:57, 1.60it/s]
3%|▎ | 2000/57600 [21:00<9:29:47, 1.63it/s]
{'loss': 1.7288, 'learning_rate': 9.717770034843206e-07, 'epoch': 22.08}
3%|▎ | 2000/57600 [21:00<9:29:47, 1.63it/s]
3%|▎ | 2001/57600 [21:01<9:30:31, 1.62it/s]
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3%|▎ | 2008/57600 [21:05<9:36:32, 1.61it/s]
3%|▎ | 2009/57600 [21:06<9:37:29, 1.60it/s]
3%|▎ | 2010/57600 [21:06<9:41:48, 1.59it/s]
3%|▎ | 2011/57600 [21:07<9:44:05, 1.59it/s]
3%|▎ | 2012/57600 [21:08<9:43:25, 1.59it/s]
3%|▎ | 2013/57600 [21:08<9:51:40, 1.57it/s]
3%|▎ | 2014/57600 [21:09<9:56:25, 1.55it/s]
3%|▎ | 2015/57600 [21:10<9:33:49, 1.61it/s]
4%|▎ | 2016/57600 [21:10<9:32:07, 1.62it/s]
4%|▎ | 2017/57600 [21:11<9:22:31, 1.65it/s]
4%|▎ | 2018/57600 [21:11<9:32:52, 1.62it/s]
4%|▎ | 2019/57600 [21:12<9:27:09, 1.63it/s]
4%|▎ | 2020/57600 [21:13<9:36:13, 1.61it/s]
{'loss': 1.7068, 'learning_rate': 9.714634146341462e-07, 'epoch': 22.3}
4%|▎ | 2020/57600 [21:13<9:36:13, 1.61it/s]
4%|▎ | 2021/57600 [21:13<9:25:06, 1.64it/s]
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4%|▎ | 2030/57600 [21:19<9:45:45, 1.58it/s]
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{'loss': 1.6812, 'learning_rate': 9.71149825783972e-07, 'epoch': 22.52}
4%|▎ | 2040/57600 [21:25<9:37:09, 1.60it/s]
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{'loss': 1.6772, 'learning_rate': 9.708362369337977e-07, 'epoch': 22.74}
4%|▎ | 2060/57600 [21:38<9:48:02, 1.57it/s]
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{'loss': 1.7103, 'learning_rate': 9.705226480836236e-07, 'epoch': 22.96}
4%|▎ | 2080/57600 [21:51<10:02:24, 1.54it/s]
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{'loss': 1.6984, 'learning_rate': 9.702090592334495e-07, 'epoch': 23.18}
4%|▎ | 2100/57600 [22:04<9:49:48, 1.57it/s]
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{'loss': 1.7281, 'learning_rate': 9.698954703832753e-07, 'epoch': 23.4}
4%|▎ | 2120/57600 [22:17<10:02:49, 1.53it/s]
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{'loss': 1.7123, 'learning_rate': 9.695818815331012e-07, 'epoch': 23.62}
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{'loss': 1.7174, 'learning_rate': 9.692682926829266e-07, 'epoch': 23.84}
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{'loss': 1.7281, 'learning_rate': 9.689547038327525e-07, 'epoch': 24.06}
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{'loss': 1.7189, 'learning_rate': 9.686411149825783e-07, 'epoch': 24.28}
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{'loss': 1.6272, 'learning_rate': 9.683275261324042e-07, 'epoch': 24.5}
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{'loss': 1.6588, 'learning_rate': 9.6801393728223e-07, 'epoch': 24.72}
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{'loss': 1.6627, 'learning_rate': 9.677003484320557e-07, 'epoch': 24.94}
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{'loss': 1.6504, 'learning_rate': 9.673867595818815e-07, 'epoch': 25.17}
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{'loss': 1.6847, 'learning_rate': 9.670731707317072e-07, 'epoch': 25.39}
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{'loss': 1.6793, 'learning_rate': 9.66759581881533e-07, 'epoch': 25.61}
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{'loss': 1.7009, 'learning_rate': 9.66445993031359e-07, 'epoch': 25.83}
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{'loss': 1.6581, 'learning_rate': 9.661324041811848e-07, 'epoch': 26.05}
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{'loss': 1.6648, 'learning_rate': 9.658188153310104e-07, 'epoch': 26.27}
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{'loss': 1.6303, 'learning_rate': 9.65505226480836e-07, 'epoch': 26.49}
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{'loss': 1.7222, 'learning_rate': 9.65191637630662e-07, 'epoch': 26.71}
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{'loss': 1.7164, 'learning_rate': 9.648780487804878e-07, 'epoch': 26.93}
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{'loss': 1.6894, 'learning_rate': 9.645644599303136e-07, 'epoch': 27.15}
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{'loss': 1.6904, 'learning_rate': 9.642508710801393e-07, 'epoch': 27.37}
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{'loss': 1.6133, 'learning_rate': 9.639372822299651e-07, 'epoch': 27.59}
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{'loss': 1.6445, 'learning_rate': 9.636236933797908e-07, 'epoch': 27.81}
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{'loss': 1.6421, 'learning_rate': 9.633101045296166e-07, 'epoch': 28.04}
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{'loss': 1.672, 'learning_rate': 9.629965156794425e-07, 'epoch': 28.26}
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{'loss': 1.6592, 'learning_rate': 9.626829268292684e-07, 'epoch': 28.48}
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{'loss': 1.649, 'learning_rate': 9.62369337979094e-07, 'epoch': 28.7}
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{'loss': 1.6667, 'learning_rate': 9.620557491289199e-07, 'epoch': 28.92}
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{'loss': 1.6172, 'learning_rate': 9.617421602787455e-07, 'epoch': 29.14}
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{'loss': 1.6696, 'learning_rate': 9.614285714285714e-07, 'epoch': 29.36}
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{'loss': 1.7091, 'learning_rate': 9.611149825783972e-07, 'epoch': 29.58}
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{'loss': 1.6111, 'learning_rate': 9.60801393728223e-07, 'epoch': 29.8}
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{'loss': 1.6274, 'learning_rate': 9.604878048780487e-07, 'epoch': 30.02}
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{'loss': 1.6186, 'learning_rate': 9.601742160278746e-07, 'epoch': 30.24}
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{'loss': 1.6336, 'learning_rate': 9.598606271777002e-07, 'epoch': 30.46}
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{'loss': 1.6414, 'learning_rate': 9.59547038327526e-07, 'epoch': 30.68}
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{'loss': 1.6628, 'learning_rate': 9.59233449477352e-07, 'epoch': 30.91}
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{'loss': 1.6092, 'learning_rate': 9.589198606271776e-07, 'epoch': 31.13}
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{'loss': 1.669, 'learning_rate': 9.586062717770034e-07, 'epoch': 31.35}
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{'loss': 1.6597, 'learning_rate': 9.58292682926829e-07, 'epoch': 31.57}
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{'loss': 1.5658, 'learning_rate': 9.57979094076655e-07, 'epoch': 31.79}
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{'loss': 1.6662, 'learning_rate': 9.576655052264808e-07, 'epoch': 32.01}
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{'loss': 1.6241, 'learning_rate': 9.573519163763067e-07, 'epoch': 32.23}
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{'loss': 1.6181, 'learning_rate': 9.570383275261325e-07, 'epoch': 32.45}
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{'loss': 1.6561, 'learning_rate': 9.567247386759582e-07, 'epoch': 32.67}
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{'loss': 1.6176, 'learning_rate': 9.564111498257838e-07, 'epoch': 32.89}
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{'loss': 1.686, 'learning_rate': 9.560975609756097e-07, 'epoch': 33.11}
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{'loss': 1.5938, 'learning_rate': 9.557839721254355e-07, 'epoch': 33.33}
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{'loss': 1.5952, 'learning_rate': 9.554703832752614e-07, 'epoch': 33.55}
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{'loss': 1.5772, 'learning_rate': 9.55156794425087e-07, 'epoch': 33.77}
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{'loss': 1.5889, 'learning_rate': 9.548432055749129e-07, 'epoch': 34.0}
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{'loss': 1.5953, 'learning_rate': 9.545296167247385e-07, 'epoch': 34.22}
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{'loss': 1.6379, 'learning_rate': 9.542160278745644e-07, 'epoch': 34.44}
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{'loss': 1.6252, 'learning_rate': 9.539024390243903e-07, 'epoch': 34.66}
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{'loss': 1.628, 'learning_rate': 9.53588850174216e-07, 'epoch': 34.88}
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{'loss': 1.6547, 'learning_rate': 9.532752613240419e-07, 'epoch': 35.1}
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{'loss': 1.6308, 'learning_rate': 9.529616724738675e-07, 'epoch': 35.32}
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{'loss': 1.599, 'learning_rate': 9.526480836236935e-07, 'epoch': 35.54}
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{'loss': 1.6198, 'learning_rate': 9.523344947735191e-07, 'epoch': 35.76}
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{'loss': 1.6248, 'learning_rate': 9.520209059233449e-07, 'epoch': 35.98}
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{'loss': 1.576, 'learning_rate': 9.517073170731706e-07, 'epoch': 36.2}
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{'loss': 1.6648, 'learning_rate': 9.513937282229965e-07, 'epoch': 36.42}
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{'loss': 1.6035, 'learning_rate': 9.510801393728223e-07, 'epoch': 36.64}
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{'loss': 1.6051, 'learning_rate': 9.50766550522648e-07, 'epoch': 36.87}
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{'loss': 1.6052, 'learning_rate': 9.504529616724738e-07, 'epoch': 37.09}
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{'loss': 1.6081, 'learning_rate': 9.501393728222996e-07, 'epoch': 37.31}
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{'loss': 1.6066, 'learning_rate': 9.498257839721255e-07, 'epoch': 37.53}
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{'loss': 1.5763, 'learning_rate': 9.495121951219511e-07, 'epoch': 37.75}
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{'loss': 1.6039, 'learning_rate': 9.49198606271777e-07, 'epoch': 37.97}
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{'loss': 1.6203, 'learning_rate': 9.488850174216028e-07, 'epoch': 38.19}
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{'loss': 1.619, 'learning_rate': 9.485714285714285e-07, 'epoch': 38.41}
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{'loss': 1.6055, 'learning_rate': 9.482578397212543e-07, 'epoch': 38.63}
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{'loss': 1.6037, 'learning_rate': 9.479442508710801e-07, 'epoch': 38.85}
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{'loss': 1.6259, 'learning_rate': 9.476306620209059e-07, 'epoch': 39.07}
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{'loss': 1.5917, 'learning_rate': 9.473170731707316e-07, 'epoch': 39.29}
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{'loss': 1.5364, 'learning_rate': 9.466898954703833e-07, 'epoch': 39.74}
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{'max_length': 384, 'max_gen_length': 380, 'num_beams': 5}
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100%|██████████| 6/6 [00:01<00:00, 2.62it/s][A{'score': 41.135867840420225, 'counts': [147, 94, 64, 45], 'totals': [241, 211, 181, 151], 'precisions': [60.995850622406635, 44.54976303317535, 35.35911602209945, 29.801324503311257], 'bp': 1.0, 'sys_len': 241, 'ref_len': 207}
[A{'eval_loss': 2.0473527908325195, 'eval_bleu': 41.1359, 'eval_gen_len': 9.5667, 'eval_runtime': 3.9497, 'eval_samples_per_second': 7.596, 'eval_steps_per_second': 1.519, 'epoch': 39.74}
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100%|██████████| 6/6 [00:03<00:00, 2.62it/s][A
[A[WARNING|configuration_utils.py:448] 2024-10-02 07:32:11,487 >> 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}
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self.pid = os.fork()
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