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/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(
2024-10-28 15:22:21.126391: 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
2024-10-28 15:22:21.126494: 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
2024-10-28 15:22:21.332900: 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(
last_checkpoint=None

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  warnings.warn(

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training_args.use_lang_prefix=True

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[WARNING|trainer.py:2764] 2024-10-28 15:25:16,687 >> There were missing keys in the checkpoint model loaded: ['model.encoder.embed_tokens.weight', 'model.decoder.embed_tokens.weight', 'lm_head.weight'].
/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
/opt/conda/lib/python3.10/site-packages/transformers/trainer.py:3108: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  torch.load(os.path.join(checkpoint, OPTIMIZER_NAME), map_location=map_location)
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-20241028_152523-2rzpheht
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/2rzpheht

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  self.pid = os.fork()
/opt/conda/lib/python3.10/site-packages/transformers/trainer.py:2843: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.
  checkpoint_rng_state = torch.load(rng_file)

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{'loss': 2.4441, 'learning_rate': 1.3131725657980034e-07, 'epoch': 11.01}

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{'loss': 2.5027, 'learning_rate': 1.31284843770258e-07, 'epoch': 11.01}

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{'loss': 2.4709, 'learning_rate': 1.3125243096071566e-07, 'epoch': 11.02}

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{'loss': 2.4576, 'learning_rate': 1.3122001815117335e-07, 'epoch': 11.02}

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{'loss': 2.4395, 'learning_rate': 1.31187605341631e-07, 'epoch': 11.03}

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{'loss': 2.504, 'learning_rate': 1.3115519253208867e-07, 'epoch': 11.03}

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{'loss': 2.5521, 'learning_rate': 1.3112277972254634e-07, 'epoch': 11.04}

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{'loss': 2.5074, 'learning_rate': 1.31090366913004e-07, 'epoch': 11.04}

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{'loss': 2.5079, 'learning_rate': 1.3105795410346169e-07, 'epoch': 11.05}

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{'loss': 2.5337, 'learning_rate': 1.3102554129391935e-07, 'epoch': 11.05}

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{'loss': 2.4815, 'learning_rate': 1.30993128484377e-07, 'epoch': 11.06}

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{'loss': 2.4897, 'learning_rate': 1.309607156748347e-07, 'epoch': 11.06}

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{'loss': 2.5184, 'learning_rate': 1.3092830286529236e-07, 'epoch': 11.07}

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{'loss': 2.459, 'learning_rate': 1.3089589005575002e-07, 'epoch': 11.07}

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{'loss': 2.4623, 'learning_rate': 1.308634772462077e-07, 'epoch': 11.08}

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{'loss': 2.4422, 'learning_rate': 1.3083106443666535e-07, 'epoch': 11.08}

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{'loss': 2.5212, 'learning_rate': 1.3079865162712303e-07, 'epoch': 11.09}

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{'loss': 2.4923, 'learning_rate': 1.307662388175807e-07, 'epoch': 11.09}

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{'loss': 2.5076, 'learning_rate': 1.3073382600803836e-07, 'epoch': 11.1}

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{'loss': 2.5291, 'learning_rate': 1.3070141319849605e-07, 'epoch': 11.1}

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{'loss': 2.4459, 'learning_rate': 1.306690003889537e-07, 'epoch': 11.11}

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{'loss': 2.4522, 'learning_rate': 1.3063658757941137e-07, 'epoch': 11.11}

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{'loss': 2.4752, 'learning_rate': 1.3060417476986906e-07, 'epoch': 11.12}

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{'loss': 2.4877, 'learning_rate': 1.3057176196032672e-07, 'epoch': 11.12}

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{'loss': 2.4969, 'learning_rate': 1.3053934915078438e-07, 'epoch': 11.13}

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{'loss': 2.5331, 'learning_rate': 1.3050693634124207e-07, 'epoch': 11.13}

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{'loss': 2.4214, 'learning_rate': 1.304745235316997e-07, 'epoch': 11.14}

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{'loss': 2.4795, 'learning_rate': 1.304421107221574e-07, 'epoch': 11.14}

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{'loss': 2.491, 'learning_rate': 1.3040969791261506e-07, 'epoch': 11.15}

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{'loss': 2.4823, 'learning_rate': 1.3037728510307272e-07, 'epoch': 11.16}

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{'loss': 2.5129, 'learning_rate': 1.303448722935304e-07, 'epoch': 11.16}

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{'loss': 2.4543, 'learning_rate': 1.3031245948398807e-07, 'epoch': 11.17}

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{'loss': 2.4735, 'learning_rate': 1.3028004667444573e-07, 'epoch': 11.17}

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{'loss': 2.5127, 'learning_rate': 1.3024763386490342e-07, 'epoch': 11.18}

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{'loss': 2.4045, 'learning_rate': 1.3021522105536105e-07, 'epoch': 11.18}

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{'loss': 2.4599, 'learning_rate': 1.3018280824581874e-07, 'epoch': 11.19}

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{'loss': 2.4875, 'learning_rate': 1.301503954362764e-07, 'epoch': 11.19}

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{'loss': 2.4523, 'learning_rate': 1.3011798262673407e-07, 'epoch': 11.2}

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{'loss': 2.47, 'learning_rate': 1.3008556981719175e-07, 'epoch': 11.2}

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{'loss': 2.464, 'learning_rate': 1.3005315700764942e-07, 'epoch': 11.21}

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{'loss': 2.5223, 'learning_rate': 1.3002074419810708e-07, 'epoch': 11.21}

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{'loss': 2.4749, 'learning_rate': 1.2998833138856477e-07, 'epoch': 11.22}

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{'loss': 2.5268, 'learning_rate': 1.2995591857902243e-07, 'epoch': 11.22}

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{'loss': 2.4837, 'learning_rate': 1.299235057694801e-07, 'epoch': 11.23}

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{'loss': 2.4285, 'learning_rate': 1.2989109295993778e-07, 'epoch': 11.23}

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{'loss': 2.4918, 'learning_rate': 1.2985868015039541e-07, 'epoch': 11.24}

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{'loss': 2.4585, 'learning_rate': 1.298262673408531e-07, 'epoch': 11.24}

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{'loss': 2.5156, 'learning_rate': 1.2979385453131076e-07, 'epoch': 11.25}

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{'loss': 2.5031, 'learning_rate': 1.2976144172176843e-07, 'epoch': 11.25}

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{'loss': 2.456, 'learning_rate': 1.2972902891222611e-07, 'epoch': 11.26}

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{'loss': 2.5083, 'learning_rate': 1.2969661610268378e-07, 'epoch': 11.26}

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{'loss': 2.5241, 'learning_rate': 1.2966420329314144e-07, 'epoch': 11.27}

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{'loss': 2.4751, 'learning_rate': 1.2963179048359913e-07, 'epoch': 11.27}

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{'loss': 2.4695, 'learning_rate': 1.295993776740568e-07, 'epoch': 11.28}

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{'loss': 2.5578, 'learning_rate': 1.2956696486451445e-07, 'epoch': 11.28}

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{'loss': 2.4649, 'learning_rate': 1.2953455205497214e-07, 'epoch': 11.29}

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{'loss': 2.4747, 'learning_rate': 1.2950213924542977e-07, 'epoch': 11.29}

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{'loss': 2.4679, 'learning_rate': 1.2946972643588746e-07, 'epoch': 11.3}

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{'loss': 2.4683, 'learning_rate': 1.2943731362634512e-07, 'epoch': 11.31}

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{'loss': 2.4857, 'learning_rate': 1.2940490081680279e-07, 'epoch': 11.31}

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{'loss': 2.426, 'learning_rate': 1.2937248800726047e-07, 'epoch': 11.32}

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{'loss': 2.4733, 'learning_rate': 1.2934007519771814e-07, 'epoch': 11.32}

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{'loss': 2.4812, 'learning_rate': 1.293076623881758e-07, 'epoch': 11.33}

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{'loss': 2.4528, 'learning_rate': 1.2927524957863349e-07, 'epoch': 11.33}

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{'loss': 2.494, 'learning_rate': 1.2924283676909112e-07, 'epoch': 11.34}

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{'loss': 2.4795, 'learning_rate': 1.292104239595488e-07, 'epoch': 11.34}

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{'loss': 2.4816, 'learning_rate': 1.2917801115000647e-07, 'epoch': 11.35}

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{'loss': 2.5282, 'learning_rate': 1.2914559834046413e-07, 'epoch': 11.35}

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{'loss': 2.4849, 'learning_rate': 1.2911318553092182e-07, 'epoch': 11.36}

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{'loss': 2.4246, 'learning_rate': 1.2908077272137948e-07, 'epoch': 11.36}

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{'loss': 2.5491, 'learning_rate': 1.2904835991183715e-07, 'epoch': 11.37}

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{'loss': 2.4584, 'learning_rate': 1.2901594710229483e-07, 'epoch': 11.37}

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{'loss': 2.5351, 'learning_rate': 1.289835342927525e-07, 'epoch': 11.38}

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{'loss': 2.5108, 'learning_rate': 1.2895112148321016e-07, 'epoch': 11.38}

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{'loss': 2.5328, 'learning_rate': 1.2891870867366782e-07, 'epoch': 11.39}

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{'loss': 2.4501, 'learning_rate': 1.2888629586412548e-07, 'epoch': 11.39}

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{'loss': 2.434, 'learning_rate': 1.2885388305458317e-07, 'epoch': 11.4}

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{'loss': 2.4713, 'learning_rate': 1.2882147024504083e-07, 'epoch': 11.4}

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{'loss': 2.5093, 'learning_rate': 1.287890574354985e-07, 'epoch': 11.41}

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{'loss': 2.5031, 'learning_rate': 1.2875664462595618e-07, 'epoch': 11.41}

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{'loss': 2.4712, 'learning_rate': 1.2872423181641384e-07, 'epoch': 11.42}

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{'loss': 2.4963, 'learning_rate': 1.286918190068715e-07, 'epoch': 11.42}

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{'loss': 2.4866, 'learning_rate': 1.2865940619732917e-07, 'epoch': 11.43}

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{'loss': 2.4665, 'learning_rate': 1.2862699338778686e-07, 'epoch': 11.43}

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{'loss': 2.4525, 'learning_rate': 1.2859458057824452e-07, 'epoch': 11.44}

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{'loss': 2.5271, 'learning_rate': 1.2856216776870218e-07, 'epoch': 11.44}

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{'loss': 2.4676, 'learning_rate': 1.2852975495915984e-07, 'epoch': 11.45}

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{'loss': 2.4996, 'learning_rate': 1.2849734214961753e-07, 'epoch': 11.46}

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{'loss': 2.4901, 'learning_rate': 1.284649293400752e-07, 'epoch': 11.46}

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{'loss': 2.411, 'learning_rate': 1.2843251653053285e-07, 'epoch': 11.47}

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{'loss': 2.4688, 'learning_rate': 1.2840010372099054e-07, 'epoch': 11.47}

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{'loss': 2.4458, 'learning_rate': 1.283676909114482e-07, 'epoch': 11.48}

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{'loss': 2.4871, 'learning_rate': 1.2833527810190587e-07, 'epoch': 11.48}

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{'loss': 2.4476, 'learning_rate': 1.2830286529236353e-07, 'epoch': 11.49}

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{'loss': 2.4647, 'learning_rate': 1.282704524828212e-07, 'epoch': 11.49}

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{'loss': 2.4887, 'learning_rate': 1.2823803967327888e-07, 'epoch': 11.5}

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{'loss': 2.4835, 'learning_rate': 1.2820562686373654e-07, 'epoch': 11.5}

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{'loss': 2.4435, 'learning_rate': 1.281732140541942e-07, 'epoch': 11.51}

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{'loss': 2.4716, 'learning_rate': 1.281408012446519e-07, 'epoch': 11.51}

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{'loss': 2.457, 'learning_rate': 1.2810838843510955e-07, 'epoch': 11.52}

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{'loss': 2.4465, 'learning_rate': 1.2807597562556721e-07, 'epoch': 11.52}

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{'loss': 2.4804, 'learning_rate': 1.2804356281602488e-07, 'epoch': 11.53}

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{'loss': 2.5168, 'learning_rate': 1.2801115000648256e-07, 'epoch': 11.53}

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{'loss': 2.4759, 'learning_rate': 1.2797873719694023e-07, 'epoch': 11.54}

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{'loss': 2.4929, 'learning_rate': 1.279463243873979e-07, 'epoch': 11.54}

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{'loss': 2.4136, 'learning_rate': 1.2791391157785555e-07, 'epoch': 11.55}

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{'loss': 2.4497, 'learning_rate': 1.2788149876831324e-07, 'epoch': 11.55}

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{'loss': 2.4912, 'learning_rate': 1.278490859587709e-07, 'epoch': 11.56}

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{'loss': 2.5089, 'learning_rate': 1.2781667314922856e-07, 'epoch': 11.56}

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{'loss': 2.4516, 'learning_rate': 1.2778426033968622e-07, 'epoch': 11.57}

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{'loss': 2.5023, 'learning_rate': 1.277518475301439e-07, 'epoch': 11.57}

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{'loss': 2.4883, 'learning_rate': 1.2771943472060157e-07, 'epoch': 11.58}

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{'loss': 2.4885, 'learning_rate': 1.2768702191105924e-07, 'epoch': 11.58}

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{'loss': 2.5421, 'learning_rate': 1.2765460910151692e-07, 'epoch': 11.59}

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{'loss': 2.4938, 'learning_rate': 1.2762219629197459e-07, 'epoch': 11.59}

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{'loss': 2.4712, 'learning_rate': 1.2758978348243225e-07, 'epoch': 11.6}

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{'loss': 2.5227, 'learning_rate': 1.275573706728899e-07, 'epoch': 11.6}

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{'loss': 2.4463, 'learning_rate': 1.275249578633476e-07, 'epoch': 11.61}

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{'loss': 2.4526, 'learning_rate': 1.2749254505380526e-07, 'epoch': 11.62}

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{'loss': 2.4763, 'learning_rate': 1.2746013224426292e-07, 'epoch': 11.62}

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{'loss': 2.5575, 'learning_rate': 1.2742771943472058e-07, 'epoch': 11.63}

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{'loss': 2.5079, 'learning_rate': 1.2739530662517827e-07, 'epoch': 11.63}

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{'loss': 2.4868, 'learning_rate': 1.2736289381563593e-07, 'epoch': 11.64}

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{'loss': 2.4547, 'learning_rate': 1.273304810060936e-07, 'epoch': 11.64}

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{'loss': 2.4594, 'learning_rate': 1.2729806819655126e-07, 'epoch': 11.65}

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{'loss': 2.495, 'learning_rate': 1.2726565538700895e-07, 'epoch': 11.65}

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{'loss': 2.5174, 'learning_rate': 1.272332425774666e-07, 'epoch': 11.66}

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{'loss': 2.4223, 'learning_rate': 1.2720082976792427e-07, 'epoch': 11.66}

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{'loss': 2.4523, 'learning_rate': 1.2716841695838196e-07, 'epoch': 11.67}

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{'loss': 2.4271, 'learning_rate': 1.2713600414883962e-07, 'epoch': 11.67}

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{'loss': 2.5235, 'learning_rate': 1.2710359133929728e-07, 'epoch': 11.68}

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{'loss': 2.4666, 'learning_rate': 1.2707117852975494e-07, 'epoch': 11.68}

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{'loss': 2.4413, 'learning_rate': 1.2703876572021263e-07, 'epoch': 11.69}

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{'loss': 2.422, 'learning_rate': 1.270063529106703e-07, 'epoch': 11.69}

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{'loss': 2.5057, 'learning_rate': 1.2697394010112796e-07, 'epoch': 11.7}

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{'loss': 2.5153, 'learning_rate': 1.2694152729158562e-07, 'epoch': 11.7}

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{'loss': 2.4894, 'learning_rate': 1.269091144820433e-07, 'epoch': 11.71}

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{'loss': 2.5211, 'learning_rate': 1.2687670167250097e-07, 'epoch': 11.71}

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{'loss': 2.4449, 'learning_rate': 1.2684428886295863e-07, 'epoch': 11.72}

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{'loss': 2.4859, 'learning_rate': 1.268118760534163e-07, 'epoch': 11.72}

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{'loss': 2.4578, 'learning_rate': 1.2677946324387398e-07, 'epoch': 11.73}

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{'loss': 2.4715, 'learning_rate': 1.2674705043433164e-07, 'epoch': 11.73}

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{'loss': 2.4258, 'learning_rate': 1.267146376247893e-07, 'epoch': 11.74}

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{'loss': 2.4635, 'learning_rate': 1.26682224815247e-07, 'epoch': 11.74}

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{'loss': 2.4803, 'learning_rate': 1.2664981200570465e-07, 'epoch': 11.75}

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{'loss': 2.4333, 'learning_rate': 1.2661739919616232e-07, 'epoch': 11.75}

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{'loss': 2.5486, 'learning_rate': 1.2658498638661998e-07, 'epoch': 11.76}

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{'loss': 2.4803, 'learning_rate': 1.2655257357707767e-07, 'epoch': 11.77}

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{'loss': 2.4958, 'learning_rate': 1.2652016076753533e-07, 'epoch': 11.77}

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{'loss': 2.4918, 'learning_rate': 1.26487747957993e-07, 'epoch': 11.78}

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{'loss': 2.513, 'learning_rate': 1.2645533514845065e-07, 'epoch': 11.78}

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{'loss': 2.4897, 'learning_rate': 1.2642292233890834e-07, 'epoch': 11.79}

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{'loss': 2.4468, 'learning_rate': 1.26390509529366e-07, 'epoch': 11.79}

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{'loss': 2.4928, 'learning_rate': 1.2635809671982366e-07, 'epoch': 11.8}

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{'loss': 2.4793, 'learning_rate': 1.2632568391028132e-07, 'epoch': 11.8}

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{'loss': 2.4788, 'learning_rate': 1.26293271100739e-07, 'epoch': 11.81}

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{'loss': 2.4913, 'learning_rate': 1.2626085829119668e-07, 'epoch': 11.81}

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{'loss': 2.4966, 'learning_rate': 1.2622844548165434e-07, 'epoch': 11.82}

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{'loss': 2.4954, 'learning_rate': 1.2619603267211203e-07, 'epoch': 11.82}

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{'loss': 2.4608, 'learning_rate': 1.261636198625697e-07, 'epoch': 11.83}

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{'loss': 2.4901, 'learning_rate': 1.2613120705302735e-07, 'epoch': 11.83}

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{'loss': 2.5462, 'learning_rate': 1.26098794243485e-07, 'epoch': 11.84}

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{'loss': 2.5217, 'learning_rate': 1.260663814339427e-07, 'epoch': 11.84}

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{'loss': 2.4889, 'learning_rate': 1.2603396862440036e-07, 'epoch': 11.85}

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{'loss': 2.5021, 'learning_rate': 1.2600155581485802e-07, 'epoch': 11.85}

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{'loss': 2.5027, 'learning_rate': 1.2596914300531568e-07, 'epoch': 11.86}

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{'loss': 2.4681, 'learning_rate': 1.2593673019577337e-07, 'epoch': 11.86}

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{'loss': 2.409, 'learning_rate': 1.2590431738623104e-07, 'epoch': 11.87}

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{'loss': 2.4426, 'learning_rate': 1.258719045766887e-07, 'epoch': 11.87}

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{'loss': 2.4285, 'learning_rate': 1.2583949176714636e-07, 'epoch': 11.88}

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{'loss': 2.4452, 'learning_rate': 1.2580707895760405e-07, 'epoch': 11.88}

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{'loss': 2.4816, 'learning_rate': 1.257746661480617e-07, 'epoch': 11.89}

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{'loss': 2.4585, 'learning_rate': 1.2574225333851937e-07, 'epoch': 11.89}

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{'loss': 2.4171, 'learning_rate': 1.2570984052897706e-07, 'epoch': 11.9}

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{'loss': 2.4597, 'learning_rate': 1.2567742771943472e-07, 'epoch': 11.9}

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{'loss': 2.4195, 'learning_rate': 1.2564501490989238e-07, 'epoch': 11.91}

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{'loss': 2.5034, 'learning_rate': 1.2561260210035004e-07, 'epoch': 11.92}

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{'loss': 2.4407, 'learning_rate': 1.2558018929080773e-07, 'epoch': 11.92}

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{'loss': 2.4813, 'learning_rate': 1.255477764812654e-07, 'epoch': 11.93}

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{'loss': 2.4599, 'learning_rate': 1.2551536367172306e-07, 'epoch': 11.93}

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{'loss': 2.4641, 'learning_rate': 1.2548295086218072e-07, 'epoch': 11.94}

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{'loss': 2.5262, 'learning_rate': 1.254505380526384e-07, 'epoch': 11.94}

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{'loss': 2.4518, 'learning_rate': 1.2541812524309607e-07, 'epoch': 11.95}

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{'loss': 2.5018, 'learning_rate': 1.2538571243355373e-07, 'epoch': 11.95}

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{'loss': 2.4634, 'learning_rate': 1.253532996240114e-07, 'epoch': 11.96}

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{'loss': 2.4889, 'learning_rate': 1.2532088681446908e-07, 'epoch': 11.96}

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{'loss': 2.4577, 'learning_rate': 1.2528847400492674e-07, 'epoch': 11.97}

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{'loss': 2.4812, 'learning_rate': 1.252560611953844e-07, 'epoch': 11.97}

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{'loss': 2.4448, 'learning_rate': 1.252236483858421e-07, 'epoch': 11.98}

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{'loss': 2.4594, 'learning_rate': 1.2519123557629975e-07, 'epoch': 11.98}

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{'loss': 2.4473, 'learning_rate': 1.2515882276675742e-07, 'epoch': 11.99}

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{'loss': 2.4831, 'learning_rate': 1.2512640995721508e-07, 'epoch': 11.99}

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{'loss': 2.4706, 'learning_rate': 1.2509399714767277e-07, 'epoch': 12.0}

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{'max_length': 384, 'max_gen_length': 380, 'num_beams': 5}


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100%|██████████| 861/861 [11:49<00:00,  1.05it/s]{'score': 12.509961638161249, 'counts': [18011, 8302, 4221, 2162], 'totals': [51205, 49483, 47761, 46039], 'precisions': [35.1742993848257, 16.777479134248125, 8.83775465337828, 4.696018592932079], 'bp': 1.0, 'sys_len': 51205, 'ref_len': 32555}

                                                         

                                                 
{'eval_loss': 2.4478776454925537, 'eval_bleu': 12.51, 'eval_gen_len': 37.4146, 'eval_runtime': 807.7318, 'eval_samples_per_second': 2.132, 'eval_steps_per_second': 1.066, 'epoch': 12.0}

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                                                 [WARNING|configuration_utils.py:448] 2024-10-28 17:08:07,139 >> 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.
  self.pid = os.fork()

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{'loss': 2.4527, 'learning_rate': 1.2506158433813043e-07, 'epoch': 12.0}

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{'loss': 2.436, 'learning_rate': 1.250291715285881e-07, 'epoch': 12.01}

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{'loss': 2.4514, 'learning_rate': 1.2499675871904575e-07, 'epoch': 12.01}

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