nohup: ignoring input
/home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/transformers/utils/generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
  _torch_pytree._register_pytree_node(
2025-06-09 22:06:45.462638: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-06-09 22:06:45.474774: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
E0000 00:00:1749528405.488251  122593 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
E0000 00:00:1749528405.492096  122593 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
W0000 00:00:1749528405.503161  122593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1749528405.503176  122593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1749528405.503177  122593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
W0000 00:00:1749528405.503178  122593 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
2025-06-09 22:06:45.506438: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX512_FP16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/transformers/utils/generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.
  _torch_pytree._register_pytree_node(
/home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/huggingface_hub/file_download.py:896: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.
  warnings.warn(
/home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/transformers/convert_slow_tokenizer.py:473: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.
  warnings.warn(
➡️  Tokenizing snli …

Map (num_proc=128):   0%|          | 0/550152 [00:00<?, ? examples/s]
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Some weights of DebertaV2ForSequenceClassification were not initialized from the model checkpoint at /home/student_no_backup/sakumar/PHMC/contradiction_detection/pretrain_deberta_large_covid_with_val/ and are newly initialized: ['classifier.weight', 'classifier.bias', 'pooler.dense.weight', 'pooler.dense.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches']). Please pass an `accelerate.DataLoaderConfiguration` instead: 
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False)
  warnings.warn(
➡️  Loading model from /home/student_no_backup/sakumar/PHMC/contradiction_detection/pretrain_deberta_large_covid_with_val/ …
➡️  Starting fine-tuning on snli …

  0%|          | 0/128955 [00:00<?, ?it/s]You're using a DebertaV2TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.
/home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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  1%|          | 1000/128955 [05:11<12:06:38,  2.93it/s]
                                                        

  1%|          | 1000/128955 [05:11<12:06:38,  2.93it/s]
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  2%|▏         | 1967/128955 [10:10<10:45:50,  3.28it/s]
  2%|▏         | 1968/128955 [10:11<12:40:31,  2.78it/s]
  2%|▏         | 1969/128955 [10:11<12:06:07,  2.91it/s]
  2%|▏         | 1970/128955 [10:12<11:41:51,  3.02it/s]
  2%|▏         | 1971/128955 [10:12<11:25:09,  3.09it/s]
  2%|▏         | 1972/128955 [10:12<11:13:15,  3.14it/s]
  2%|▏         | 1973/128955 [10:12<11:04:57,  3.18it/s]
  2%|▏         | 1974/128955 [10:13<10:59:32,  3.21it/s]
  2%|▏         | 1975/128955 [10:13<10:55:31,  3.23it/s]
  2%|▏         | 1976/128955 [10:13<10:52:25,  3.24it/s]
  2%|▏         | 1977/128955 [10:14<10:50:18,  3.25it/s]
  2%|▏         | 1978/128955 [10:14<10:48:49,  3.26it/s]
  2%|▏         | 1979/128955 [10:14<10:47:44,  3.27it/s]
  2%|▏         | 1980/128955 [10:15<10:46:43,  3.27it/s]
  2%|▏         | 1981/128955 [10:15<10:46:16,  3.27it/s]
  2%|▏         | 1982/128955 [10:15<10:45:51,  3.28it/s]
  2%|▏         | 1983/128955 [10:16<10:45:57,  3.28it/s]
  2%|▏         | 1984/128955 [10:16<10:45:19,  3.28it/s]
  2%|▏         | 1985/128955 [10:16<10:45:09,  3.28it/s]
  2%|▏         | 1986/128955 [10:16<10:45:19,  3.28it/s]
  2%|▏         | 1987/128955 [10:17<10:45:35,  3.28it/s]
  2%|▏         | 1988/128955 [10:17<10:45:01,  3.28it/s]
  2%|▏         | 1989/128955 [10:17<10:45:08,  3.28it/s]
  2%|▏         | 1990/128955 [10:18<10:45:48,  3.28it/s]
  2%|▏         | 1991/128955 [10:18<10:45:50,  3.28it/s]
  2%|▏         | 1992/128955 [10:18<10:46:02,  3.28it/s]
  2%|▏         | 1993/128955 [10:19<10:45:51,  3.28it/s]
  2%|▏         | 1994/128955 [10:19<10:45:59,  3.28it/s]
  2%|▏         | 1995/128955 [10:19<10:45:44,  3.28it/s]
  2%|▏         | 1996/128955 [10:19<10:45:30,  3.28it/s]
  2%|▏         | 1997/128955 [10:20<10:45:25,  3.28it/s]
  2%|▏         | 1998/128955 [10:20<10:46:08,  3.27it/s]
  2%|▏         | 1999/128955 [10:20<10:45:27,  3.28it/s]
  2%|▏         | 2000/128955 [10:21<10:45:15,  3.28it/s]
                                                        

  2%|▏         | 2000/128955 [10:21<10:45:15,  3.28it/s]
  2%|▏         | 2001/128955 [10:21<10:45:45,  3.28it/s]
  2%|▏         | 2002/128955 [10:21<10:45:55,  3.28it/s]
  2%|▏         | 2003/128955 [10:22<10:45:44,  3.28it/s]
  2%|▏         | 2004/128955 [10:22<12:32:25,  2.81it/s]
  2%|▏         | 2005/128955 [10:22<12:01:01,  2.93it/s]
  2%|▏         | 2006/128955 [10:23<11:38:18,  3.03it/s]
  2%|▏         | 2007/128955 [10:23<11:22:53,  3.10it/s]
  2%|▏         | 2008/128955 [10:23<11:11:40,  3.15it/s]
  2%|▏         | 2009/128955 [10:24<11:03:38,  3.19it/s]
  2%|▏         | 2010/128955 [10:24<10:57:33,  3.22it/s]
  2%|▏         | 2011/128955 [10:24<10:54:39,  3.23it/s]
  2%|▏         | 2012/128955 [10:25<10:52:10,  3.24it/s]
  2%|▏         | 2013/128955 [10:25<10:49:31,  3.26it/s]
  2%|▏         | 2014/128955 [10:25<10:47:41,  3.27it/s]
  2%|▏         | 2015/128955 [10:25<10:46:49,  3.27it/s]
  2%|▏         | 2016/128955 [10:26<10:46:34,  3.27it/s]
  2%|▏         | 2017/128955 [10:26<10:46:25,  3.27it/s]
  2%|▏         | 2018/128955 [10:26<10:46:29,  3.27it/s]
  2%|▏         | 2019/128955 [10:27<10:46:29,  3.27it/s]
  2%|▏         | 2020/128955 [10:27<10:46:20,  3.27it/s]
  2%|▏         | 2021/128955 [10:27<10:46:05,  3.27it/s]
  2%|▏         | 2022/128955 [10:28<10:45:30,  3.28it/s]
  2%|▏         | 2023/128955 [10:28<10:45:43,  3.28it/s]
  2%|▏         | 2024/128955 [10:28<10:45:28,  3.28it/s]
  2%|▏         | 2025/128955 [10:28<10:45:29,  3.28it/s]
  2%|▏         | 2026/128955 [10:29<10:45:01,  3.28it/s]
  2%|▏         | 2027/128955 [10:29<10:44:48,  3.28it/s]
  2%|▏         | 2028/128955 [10:29<10:44:57,  3.28it/s]
  2%|▏         | 2029/128955 [10:30<10:44:55,  3.28it/s]
  2%|▏         | 2030/128955 [10:30<10:45:06,  3.28it/s]
  2%|▏         | 2031/128955 [10:30<10:45:29,  3.28it/s]
  2%|▏         | 2032/128955 [10:31<10:45:19,  3.28it/s]
  2%|▏         | 2033/128955 [10:31<10:44:46,  3.28it/s]
  2%|▏         | 2034/128955 [10:31<10:44:31,  3.28it/s]
  2%|▏         | 2035/128955 [10:32<12:51:41,  2.74it/s]
  2%|▏         | 2036/128955 [10:32<12:14:27,  2.88it/s]
  2%|▏         | 2037/128955 [10:32<11:47:43,  2.99it/s]
  2%|▏         | 2038/128955 [10:33<11:29:41,  3.07it/s]
  2%|▏         | 2039/128955 [10:33<11:16:28,  3.13it/s]
  2%|▏         | 2040/128955 [10:33<11:06:59,  3.17it/s]
  2%|▏         | 2041/128955 [10:34<11:00:40,  3.20it/s]
  2%|▏         | 2042/128955 [10:34<10:56:14,  3.22it/s]
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  2%|▏         | 2048/128955 [10:36<10:46:50,  3.27it/s]
  2%|▏         | 2049/128955 [10:36<10:46:01,  3.27it/s]
  2%|▏         | 2050/128955 [10:36<10:45:21,  3.28it/s]
  2%|▏         | 2051/128955 [10:37<10:45:19,  3.28it/s]
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  2%|▏         | 2061/128955 [10:40<10:45:39,  3.28it/s]
  2%|▏         | 2062/128955 [10:40<10:46:14,  3.27it/s]
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  2%|▏         | 2067/128955 [10:42<10:45:33,  3.28it/s]
  2%|▏         | 2068/128955 [10:42<10:45:13,  3.28it/s]
  2%|▏         | 2069/128955 [10:42<10:45:16,  3.28it/s]
  2%|▏         | 2070/128955 [10:42<10:45:22,  3.28it/s]
  2%|▏         | 2071/128955 [10:43<12:43:03,  2.77it/s]
  2%|▏         | 2072/128955 [10:43<12:07:57,  2.90it/s]
  2%|▏         | 2073/128955 [10:44<11:43:27,  3.01it/s]
  2%|▏         | 2074/128955 [10:44<11:26:01,  3.08it/s]
  2%|▏         | 2075/128955 [10:44<11:13:05,  3.14it/s]
  2%|▏         | 2076/128955 [10:44<11:04:06,  3.18it/s]
  2%|▏         | 2077/128955 [10:45<10:58:44,  3.21it/s]
  2%|▏         | 2078/128955 [10:45<10:55:18,  3.23it/s]
  2%|▏         | 2079/128955 [10:45<10:52:20,  3.24it/s]
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  2%|▏         | 2086/128955 [10:47<10:45:36,  3.28it/s]
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  4%|▍         | 5000/128955 [25:51<10:29:47,  3.28it/s]{'loss': 0.427, 'learning_rate': 2.976736070722345e-05, 'epoch': 0.12}
{'loss': 0.3386, 'learning_rate': 2.9534721414446903e-05, 'epoch': 0.23}
{'loss': 0.3274, 'learning_rate': 2.9302082121670353e-05, 'epoch': 0.35}
{'loss': 0.3053, 'learning_rate': 2.90694428288938e-05, 'epoch': 0.47}
{'loss': 0.3044, 'learning_rate': 2.883680353611725e-05, 'epoch': 0.58}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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  8%|▊         | 10000/128955 [52:04<10:12:23,  3.24it/s]{'eval_loss': 0.24768945574760437, 'eval_accuracy': 0.9016, 'eval_f1': 0.8945933984658765, 'eval_runtime': 15.8672, 'eval_samples_per_second': 630.232, 'eval_steps_per_second': 4.979, 'epoch': 0.58}
{'loss': 0.2982, 'learning_rate': 2.8604164243340702e-05, 'epoch': 0.7}
{'loss': 0.2907, 'learning_rate': 2.837152495056415e-05, 'epoch': 0.81}
{'loss': 0.2866, 'learning_rate': 2.81388856577876e-05, 'epoch': 0.93}
{'loss': 0.2588, 'learning_rate': 2.790624636501105e-05, 'epoch': 1.05}
{'loss': 0.2188, 'learning_rate': 2.7673607072234503e-05, 'epoch': 1.16}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 12%|█▏        | 15000/128955 [1:18:19<9:38:28,  3.28it/s]{'eval_loss': 0.2434004694223404, 'eval_accuracy': 0.9036, 'eval_f1': 0.8962843672679712, 'eval_runtime': 15.3053, 'eval_samples_per_second': 653.368, 'eval_steps_per_second': 5.162, 'epoch': 1.16}
{'loss': 0.2225, 'learning_rate': 2.7440967779457953e-05, 'epoch': 1.28}
{'loss': 0.2228, 'learning_rate': 2.72083284866814e-05, 'epoch': 1.4}
{'loss': 0.225, 'learning_rate': 2.697568919390485e-05, 'epoch': 1.51}
{'loss': 0.2274, 'learning_rate': 2.6743049901128302e-05, 'epoch': 1.63}
{'loss': 0.2268, 'learning_rate': 2.651041060835175e-05, 'epoch': 1.74}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 16%|█▌        | 20000/128955 [1:44:35<9:14:26,  3.28it/s]{'eval_loss': 0.2607056200504303, 'eval_accuracy': 0.9007, 'eval_f1': 0.893407550916413, 'eval_runtime': 15.6406, 'eval_samples_per_second': 639.362, 'eval_steps_per_second': 5.051, 'epoch': 1.74}
{'loss': 0.2244, 'learning_rate': 2.62777713155752e-05, 'epoch': 1.86}
{'loss': 0.2284, 'learning_rate': 2.604513202279865e-05, 'epoch': 1.98}
{'loss': 0.1697, 'learning_rate': 2.5812492730022104e-05, 'epoch': 2.09}
{'loss': 0.1597, 'learning_rate': 2.5579853437245553e-05, 'epoch': 2.21}
{'loss': 0.1664, 'learning_rate': 2.5347214144469e-05, 'epoch': 2.33}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 19%|█▉        | 25000/128955 [2:10:49<9:50:14,  2.94it/s]{'eval_loss': 0.26950567960739136, 'eval_accuracy': 0.9033, 'eval_f1': 0.8961953192533397, 'eval_runtime': 15.2886, 'eval_samples_per_second': 654.081, 'eval_steps_per_second': 5.167, 'epoch': 2.33}
{'loss': 0.1704, 'learning_rate': 2.511457485169245e-05, 'epoch': 2.44}
{'loss': 0.1691, 'learning_rate': 2.4881935558915902e-05, 'epoch': 2.56}
{'loss': 0.1744, 'learning_rate': 2.464929626613935e-05, 'epoch': 2.68}
{'loss': 0.1753, 'learning_rate': 2.44166569733628e-05, 'epoch': 2.79}
{'loss': 0.1754, 'learning_rate': 2.418401768058625e-05, 'epoch': 2.91}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 23%|██▎       | 30000/128955 [2:37:05<8:21:59,  3.29it/s]{'eval_loss': 0.2803029716014862, 'eval_accuracy': 0.904, 'eval_f1': 0.8969573769544051, 'eval_runtime': 15.4369, 'eval_samples_per_second': 647.797, 'eval_steps_per_second': 5.118, 'epoch': 2.91}
{'loss': 0.1631, 'learning_rate': 2.3951378387809704e-05, 'epoch': 3.02}
{'loss': 0.1252, 'learning_rate': 2.3718739095033153e-05, 'epoch': 3.14}
{'loss': 0.1282, 'learning_rate': 2.34860998022566e-05, 'epoch': 3.26}
{'loss': 0.1297, 'learning_rate': 2.3253460509480053e-05, 'epoch': 3.37}
{'loss': 0.1317, 'learning_rate': 2.3020821216703502e-05, 'epoch': 3.49}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 27%|██▋       | 35000/128955 [3:03:20<7:56:53,  3.28it/s]{'eval_loss': 0.3193525969982147, 'eval_accuracy': 0.9035, 'eval_f1': 0.8961520668206642, 'eval_runtime': 15.6093, 'eval_samples_per_second': 640.642, 'eval_steps_per_second': 5.061, 'epoch': 3.49}
{'loss': 0.1305, 'learning_rate': 2.2788181923926952e-05, 'epoch': 3.61}
{'loss': 0.1343, 'learning_rate': 2.25555426311504e-05, 'epoch': 3.72}
{'loss': 0.1367, 'learning_rate': 2.2322903338373854e-05, 'epoch': 3.84}
{'loss': 0.1352, 'learning_rate': 2.2090264045597304e-05, 'epoch': 3.95}
{'loss': 0.1131, 'learning_rate': 2.185762475282075e-05, 'epoch': 4.07}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 31%|███       | 40000/128955 [3:29:34<8:08:24,  3.04it/s]{'eval_loss': 0.32377445697784424, 'eval_accuracy': 0.9036, 'eval_f1': 0.8965935976759448, 'eval_runtime': 15.3049, 'eval_samples_per_second': 653.386, 'eval_steps_per_second': 5.162, 'epoch': 4.07}
{'loss': 0.1026, 'learning_rate': 2.16249854600442e-05, 'epoch': 4.19}
{'loss': 0.1031, 'learning_rate': 2.1392346167267653e-05, 'epoch': 4.3}
{'loss': 0.0995, 'learning_rate': 2.1159706874491102e-05, 'epoch': 4.42}
{'loss': 0.1078, 'learning_rate': 2.0927067581714552e-05, 'epoch': 4.54}
{'loss': 0.1066, 'learning_rate': 2.0694428288938e-05, 'epoch': 4.65}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 35%|███▍      | 45000/128955 [3:55:44<7:06:03,  3.28it/s]{'eval_loss': 0.3146192133426666, 'eval_accuracy': 0.9053, 'eval_f1': 0.898518100560904, 'eval_runtime': 15.3105, 'eval_samples_per_second': 653.146, 'eval_steps_per_second': 5.16, 'epoch': 4.65}
{'loss': 0.1019, 'learning_rate': 2.0461788996161454e-05, 'epoch': 4.77}
{'loss': 0.1091, 'learning_rate': 2.0229149703384904e-05, 'epoch': 4.89}
{'loss': 0.1094, 'learning_rate': 1.999651041060835e-05, 'epoch': 5.0}
{'loss': 0.0732, 'learning_rate': 1.97638711178318e-05, 'epoch': 5.12}
{'loss': 0.0809, 'learning_rate': 1.9531231825055253e-05, 'epoch': 5.23}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 39%|███▉      | 50000/128955 [4:21:53<7:28:29,  2.93it/s]{'eval_loss': 0.3676794469356537, 'eval_accuracy': 0.9026, 'eval_f1': 0.8955769262786002, 'eval_runtime': 15.4443, 'eval_samples_per_second': 647.489, 'eval_steps_per_second': 5.115, 'epoch': 5.23}
{'loss': 0.0799, 'learning_rate': 1.9298592532278702e-05, 'epoch': 5.35}
{'loss': 0.0835, 'learning_rate': 1.9065953239502152e-05, 'epoch': 5.47}
{'loss': 0.0854, 'learning_rate': 1.88333139467256e-05, 'epoch': 5.58}
{'loss': 0.0842, 'learning_rate': 1.8600674653949055e-05, 'epoch': 5.7}
{'loss': 0.086, 'learning_rate': 1.8368035361172504e-05, 'epoch': 5.82}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 43%|████▎     | 55000/128955 [4:48:05<6:15:06,  3.29it/s]{'eval_loss': 0.3419972062110901, 'eval_accuracy': 0.905, 'eval_f1': 0.8979300837498616, 'eval_runtime': 15.4336, 'eval_samples_per_second': 647.935, 'eval_steps_per_second': 5.119, 'epoch': 5.82}
{'loss': 0.0853, 'learning_rate': 1.813539606839595e-05, 'epoch': 5.93}
{'loss': 0.0777, 'learning_rate': 1.79027567756194e-05, 'epoch': 6.05}
{'loss': 0.0599, 'learning_rate': 1.7670117482842853e-05, 'epoch': 6.16}
{'loss': 0.0624, 'learning_rate': 1.7437478190066303e-05, 'epoch': 6.28}
{'loss': 0.0661, 'learning_rate': 1.7204838897289752e-05, 'epoch': 6.4}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 47%|████▋     | 60000/128955 [5:14:12<5:51:40,  3.27it/s]{'eval_loss': 0.42000171542167664, 'eval_accuracy': 0.9016, 'eval_f1': 0.8948338430782733, 'eval_runtime': 15.6057, 'eval_samples_per_second': 640.793, 'eval_steps_per_second': 5.062, 'epoch': 6.4}
{'loss': 0.0658, 'learning_rate': 1.6972199604513202e-05, 'epoch': 6.51}
{'loss': 0.0673, 'learning_rate': 1.6739560311736655e-05, 'epoch': 6.63}
{'loss': 0.0667, 'learning_rate': 1.6506921018960104e-05, 'epoch': 6.75}
{'loss': 0.0677, 'learning_rate': 1.627428172618355e-05, 'epoch': 6.86}
{'loss': 0.0684, 'learning_rate': 1.6041642433407004e-05, 'epoch': 6.98}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0495, 'learning_rate': 1.5809003140630453e-05, 'epoch': 7.1}
{'loss': 0.0462, 'learning_rate': 1.5576363847853903e-05, 'epoch': 7.21}
{'loss': 0.05, 'learning_rate': 1.5343724555077352e-05, 'epoch': 7.33}
{'loss': 0.0504, 'learning_rate': 1.5111085262300805e-05, 'epoch': 7.44}
{'loss': 0.0494, 'learning_rate': 1.4878445969524253e-05, 'epoch': 7.56}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 54%|█████▍    | 70000/128955 [6:06:28<5:21:12,  3.06it/s]{'eval_loss': 0.5047815442085266, 'eval_accuracy': 0.899, 'eval_f1': 0.8919559925853721, 'eval_runtime': 15.4473, 'eval_samples_per_second': 647.364, 'eval_steps_per_second': 5.114, 'epoch': 7.56}
{'loss': 0.0518, 'learning_rate': 1.4645806676747703e-05, 'epoch': 7.68}
{'loss': 0.054, 'learning_rate': 1.4413167383971154e-05, 'epoch': 7.79}
{'loss': 0.054, 'learning_rate': 1.4180528091194604e-05, 'epoch': 7.91}
{'loss': 0.0468, 'learning_rate': 1.3947888798418053e-05, 'epoch': 8.03}
{'loss': 0.037, 'learning_rate': 1.3715249505641503e-05, 'epoch': 8.14}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0372, 'learning_rate': 1.3482610212864954e-05, 'epoch': 8.26}
{'loss': 0.0379, 'learning_rate': 1.3249970920088402e-05, 'epoch': 8.38}
{'loss': 0.042, 'learning_rate': 1.3017331627311853e-05, 'epoch': 8.49}
{'loss': 0.0402, 'learning_rate': 1.2784692334535303e-05, 'epoch': 8.61}
{'loss': 0.0401, 'learning_rate': 1.2552053041758754e-05, 'epoch': 8.72}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 62%|██████▏   | 80000/128955 [6:58:43<4:22:08,  3.11it/s]{'eval_loss': 0.4759764075279236, 'eval_accuracy': 0.9019, 'eval_f1': 0.8948102065119649, 'eval_runtime': 15.5935, 'eval_samples_per_second': 641.291, 'eval_steps_per_second': 5.066, 'epoch': 8.72}
{'loss': 0.0405, 'learning_rate': 1.2319413748982202e-05, 'epoch': 8.84}
{'loss': 0.039, 'learning_rate': 1.2086774456205653e-05, 'epoch': 8.96}
{'loss': 0.031, 'learning_rate': 1.1854135163429103e-05, 'epoch': 9.07}
{'loss': 0.0279, 'learning_rate': 1.1621495870652554e-05, 'epoch': 9.19}
{'loss': 0.0287, 'learning_rate': 1.1388856577876002e-05, 'epoch': 9.31}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0288, 'learning_rate': 1.1156217285099453e-05, 'epoch': 9.42}
{'loss': 0.0297, 'learning_rate': 1.0923577992322903e-05, 'epoch': 9.54}
{'loss': 0.0308, 'learning_rate': 1.0690938699546354e-05, 'epoch': 9.65}
{'loss': 0.0303, 'learning_rate': 1.0458299406769802e-05, 'epoch': 9.77}
{'loss': 0.0307, 'learning_rate': 1.0225660113993254e-05, 'epoch': 9.89}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0319, 'learning_rate': 9.993020821216705e-06, 'epoch': 10.0}
{'loss': 0.0191, 'learning_rate': 9.760381528440154e-06, 'epoch': 10.12}
{'loss': 0.0197, 'learning_rate': 9.527742235663604e-06, 'epoch': 10.24}
{'loss': 0.0214, 'learning_rate': 9.295102942887054e-06, 'epoch': 10.35}
{'loss': 0.021, 'learning_rate': 9.062463650110505e-06, 'epoch': 10.47}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 74%|███████▎  | 95000/128955 [8:17:05<2:53:09,  3.27it/s]{'eval_loss': 0.5743264555931091, 'eval_accuracy': 0.9029, 'eval_f1': 0.8959989537387059, 'eval_runtime': 15.4284, 'eval_samples_per_second': 648.156, 'eval_steps_per_second': 5.12, 'epoch': 10.47}
{'loss': 0.0221, 'learning_rate': 8.829824357333954e-06, 'epoch': 10.59}
{'loss': 0.0217, 'learning_rate': 8.597185064557404e-06, 'epoch': 10.7}
{'loss': 0.0224, 'learning_rate': 8.364545771780854e-06, 'epoch': 10.82}
{'loss': 0.0216, 'learning_rate': 8.131906479004305e-06, 'epoch': 10.93}
{'loss': 0.0182, 'learning_rate': 7.899267186227755e-06, 'epoch': 11.05}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 78%|███████▊  | 100000/128955 [8:43:12<2:26:45,  3.29it/s]{'eval_loss': 0.5826521515846252, 'eval_accuracy': 0.9081, 'eval_f1': 0.901186155576394, 'eval_runtime': 15.4299, 'eval_samples_per_second': 648.091, 'eval_steps_per_second': 5.12, 'epoch': 11.05}
{'loss': 0.0156, 'learning_rate': 7.666627893451204e-06, 'epoch': 11.17}
{'loss': 0.016, 'learning_rate': 7.433988600674654e-06, 'epoch': 11.28}
{'loss': 0.0142, 'learning_rate': 7.201349307898104e-06, 'epoch': 11.4}
{'loss': 0.0173, 'learning_rate': 6.968710015121554e-06, 'epoch': 11.52}
{'loss': 0.0149, 'learning_rate': 6.736070722345005e-06, 'epoch': 11.63}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 81%|████████▏ | 105000/128955 [9:09:21<2:22:10,  2.81it/s]{'eval_loss': 0.5455776453018188, 'eval_accuracy': 0.9081, 'eval_f1': 0.901132759043981, 'eval_runtime': 15.5932, 'eval_samples_per_second': 641.307, 'eval_steps_per_second': 5.066, 'epoch': 11.63}
{'loss': 0.0155, 'learning_rate': 6.503431429568455e-06, 'epoch': 11.75}
{'loss': 0.0151, 'learning_rate': 6.270792136791904e-06, 'epoch': 11.86}
{'loss': 0.0152, 'learning_rate': 6.038152844015355e-06, 'epoch': 11.98}
{'loss': 0.0113, 'learning_rate': 5.805513551238804e-06, 'epoch': 12.1}
{'loss': 0.011, 'learning_rate': 5.572874258462255e-06, 'epoch': 12.21}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0112, 'learning_rate': 5.340234965685704e-06, 'epoch': 12.33}
{'loss': 0.0092, 'learning_rate': 5.107595672909155e-06, 'epoch': 12.45}
{'loss': 0.0105, 'learning_rate': 4.874956380132604e-06, 'epoch': 12.56}
{'loss': 0.0122, 'learning_rate': 4.642317087356055e-06, 'epoch': 12.68}
{'loss': 0.0097, 'learning_rate': 4.409677794579504e-06, 'epoch': 12.8}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0098, 'learning_rate': 4.177038501802955e-06, 'epoch': 12.91}
{'loss': 0.0106, 'learning_rate': 3.9443992090264044e-06, 'epoch': 13.03}
{'loss': 0.0065, 'learning_rate': 3.711759916249855e-06, 'epoch': 13.14}
{'loss': 0.0069, 'learning_rate': 3.479120623473305e-06, 'epoch': 13.26}
{'loss': 0.0076, 'learning_rate': 3.246481330696755e-06, 'epoch': 13.38}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 93%|█████████▎| 120000/128955 [10:27:44<45:30,  3.28it/s]{'eval_loss': 0.6616904139518738, 'eval_accuracy': 0.9058, 'eval_f1': 0.898831543503887, 'eval_runtime': 15.4296, 'eval_samples_per_second': 648.104, 'eval_steps_per_second': 5.12, 'epoch': 13.38}
{'loss': 0.0067, 'learning_rate': 3.013842037920205e-06, 'epoch': 13.49}
{'loss': 0.0056, 'learning_rate': 2.781202745143655e-06, 'epoch': 13.61}
{'loss': 0.0071, 'learning_rate': 2.548563452367105e-06, 'epoch': 13.73}
{'loss': 0.0077, 'learning_rate': 2.3159241595905545e-06, 'epoch': 13.84}
{'loss': 0.0069, 'learning_rate': 2.0832848668140046e-06, 'epoch': 13.96}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0055, 'learning_rate': 1.850645574037455e-06, 'epoch': 14.07}
{'loss': 0.0041, 'learning_rate': 1.618006281260905e-06, 'epoch': 14.19}
{'loss': 0.0058, 'learning_rate': 1.385366988484355e-06, 'epoch': 14.31}
{'loss': 0.0048, 'learning_rate': 1.152727695707805e-06, 'epoch': 14.42}
{'loss': 0.004, 'learning_rate': 9.200884029312551e-07, 'epoch': 14.54}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'eval_loss': 0.7196105718612671, 'eval_accuracy': 0.9078, 'eval_f1': 0.900769588543479, 'eval_runtime': 15.4293, 'eval_samples_per_second': 648.116, 'eval_steps_per_second': 5.12, 'epoch': 14.54}
{'loss': 0.0052, 'learning_rate': 6.874491101547051e-07, 'epoch': 14.66}
{'loss': 0.005, 'learning_rate': 4.5480981737815516e-07, 'epoch': 14.77}
{'loss': 0.0033, 'learning_rate': 2.2217052460160524e-07, 'epoch': 14.89}
{'train_runtime': 40477.5398, 'train_samples_per_second': 203.873, 'train_steps_per_second': 3.186, 'train_loss': 0.08499502860572125, 'epoch': 15.0}
➡️  Saving best model to ./deberta_v3_large_snli …
➡️  Tokenizing multi_nli …

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/home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches']). Please pass an `accelerate.DataLoaderConfiguration` instead: 
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False)
  warnings.warn(
➡️  Loading model from ./deberta_v3_large_snli …
➡️  Starting fine-tuning on multi_nli …

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  warnings.warn('Was asked to gather along dimension 0, but all '

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  1%|          | 555/92040 [02:52<7:45:21,  3.28it/s]
  1%|          | 556/92040 [02:52<7:45:19,  3.28it/s]
  1%|          | 557/92040 [02:53<7:45:12,  3.28it/s]
  1%|          | 558/92040 [02:53<7:44:56,  3.28it/s]
  1%|          | 559/92040 [02:53<7:45:23,  3.28it/s]
  1%|          | 560/92040 [02:54<7:45:18,  3.28it/s]
  1%|          | 561/92040 [02:54<7:44:56,  3.28it/s]
  1%|          | 562/92040 [02:54<7:45:27,  3.28it/s]
  1%|          | 563/92040 [02:55<7:45:24,  3.28it/s]
  1%|          | 564/92040 [02:55<7:45:20,  3.28it/s]
  1%|          | 565/92040 [02:55<7:45:33,  3.27it/s]
  1%|          | 566/92040 [02:56<7:45:41,  3.27it/s]
  1%|          | 567/92040 [02:56<7:45:50,  3.27it/s]
  1%|          | 568/92040 [02:56<7:45:33,  3.27it/s]
  1%|          | 569/92040 [02:56<7:45:16,  3.28it/s]
  1%|          | 570/92040 [02:57<8:42:34,  2.92it/s]
  1%|          | 571/92040 [02:57<8:25:46,  3.01it/s]
  1%|          | 572/92040 [02:57<8:13:45,  3.09it/s]
  1%|          | 573/92040 [02:58<8:05:05,  3.14it/s]
  1%|          | 574/92040 [02:58<7:59:05,  3.18it/s]
  1%|          | 575/92040 [02:58<7:54:45,  3.21it/s]
  1%|          | 576/92040 [02:59<7:51:49,  3.23it/s]
  1%|          | 577/92040 [02:59<7:49:57,  3.24it/s]
  1%|          | 578/92040 [02:59<7:48:40,  3.25it/s]
  1%|          | 579/92040 [03:00<7:47:51,  3.26it/s]
  1%|          | 580/92040 [03:00<7:47:25,  3.26it/s]
  1%|          | 581/92040 [03:00<7:47:03,  3.26it/s]
  1%|          | 582/92040 [03:01<7:46:20,  3.27it/s]
  1%|          | 583/92040 [03:01<7:45:55,  3.27it/s]
  1%|          | 584/92040 [03:01<7:45:53,  3.27it/s]
  1%|          | 585/92040 [03:01<7:45:56,  3.27it/s]
  1%|          | 586/92040 [03:02<7:45:38,  3.27it/s]
  1%|          | 587/92040 [03:02<7:45:46,  3.27it/s]
  1%|          | 588/92040 [03:02<7:45:33,  3.27it/s]
  1%|          | 589/92040 [03:03<7:44:58,  3.28it/s]
  1%|          | 590/92040 [03:03<7:45:14,  3.28it/s]
  1%|          | 591/92040 [03:03<7:45:12,  3.28it/s]
  1%|          | 592/92040 [03:04<7:45:12,  3.28it/s]
  1%|          | 593/92040 [03:04<7:45:14,  3.28it/s]
  1%|          | 594/92040 [03:04<7:45:01,  3.28it/s]
  1%|          | 595/92040 [03:04<7:45:23,  3.27it/s]
  1%|          | 596/92040 [03:05<7:45:29,  3.27it/s]
  1%|          | 597/92040 [03:05<7:45:29,  3.27it/s]
  1%|          | 598/92040 [03:05<7:45:19,  3.28it/s]
  1%|          | 599/92040 [03:06<7:45:31,  3.27it/s]
  1%|          | 600/92040 [03:06<7:45:19,  3.28it/s]
  1%|          | 601/92040 [03:06<8:50:46,  2.87it/s]
  1%|          | 602/92040 [03:07<8:31:28,  2.98it/s]
  1%|          | 603/92040 [03:07<8:17:20,  3.06it/s]
  1%|          | 604/92040 [03:07<8:07:11,  3.13it/s]
  1%|          | 605/92040 [03:08<8:00:18,  3.17it/s]
  1%|          | 606/92040 [03:08<7:55:55,  3.20it/s]
  1%|          | 607/92040 [03:08<7:52:22,  3.23it/s]
  1%|          | 608/92040 [03:09<7:50:20,  3.24it/s]
  1%|          | 609/92040 [03:09<7:48:41,  3.25it/s]
  1%|          | 610/92040 [03:09<7:47:57,  3.26it/s]
  1%|          | 611/92040 [03:10<7:47:07,  3.26it/s]
  1%|          | 612/92040 [03:10<7:46:18,  3.27it/s]
  1%|          | 613/92040 [03:10<7:45:47,  3.27it/s]
  1%|          | 614/92040 [03:10<7:45:50,  3.27it/s]
  1%|          | 615/92040 [03:11<7:45:50,  3.27it/s]
  1%|          | 616/92040 [03:11<7:45:22,  3.27it/s]
  1%|          | 617/92040 [03:11<7:45:15,  3.28it/s]
  1%|          | 618/92040 [03:12<7:45:26,  3.27it/s]
  1%|          | 619/92040 [03:12<7:45:03,  3.28it/s]
  1%|          | 620/92040 [03:12<7:45:17,  3.27it/s]
  1%|          | 621/92040 [03:13<7:45:10,  3.28it/s]
  1%|          | 622/92040 [03:13<7:45:13,  3.27it/s]
  1%|          | 623/92040 [03:13<7:45:04,  3.28it/s]
  1%|          | 624/92040 [03:13<7:44:43,  3.28it/s]
  1%|          | 625/92040 [03:14<7:45:03,  3.28it/s]
  1%|          | 626/92040 [03:14<7:45:26,  3.27it/s]
  1%|          | 627/92040 [03:14<7:45:36,  3.27it/s]
  1%|          | 628/92040 [03:15<7:45:22,  3.27it/s]
  1%|          | 629/92040 [03:15<7:45:12,  3.27it/s]
  1%|          | 630/92040 [03:15<7:45:13,  3.27it/s]
  1%|          | 631/92040 [03:16<7:45:05,  3.28it/s]
  1%|          | 632/92040 [03:16<7:45:40,  3.27it/s]
  1%|          | 633/92040 [03:16<7:45:26,  3.27it/s]
  1%|          | 634/92040 [03:17<7:45:15,  3.27it/s]
  1%|          | 635/92040 [03:17<7:44:45,  3.28it/s]
  1%|          | 636/92040 [03:17<7:45:16,  3.27it/s]
  1%|          | 637/92040 [03:18<8:41:51,  2.92it/s]
  1%|          | 638/92040 [03:18<8:24:25,  3.02it/s]
  1%|          | 639/92040 [03:18<8:12:38,  3.09it/s]
  1%|          | 640/92040 [03:19<8:04:38,  3.14it/s]
  1%|          | 641/92040 [03:19<7:58:38,  3.18it/s]
  1%|          | 642/92040 [03:19<7:54:24,  3.21it/s]
  1%|          | 643/92040 [03:19<7:51:39,  3.23it/s]
  1%|          | 644/92040 [03:20<7:49:46,  3.24it/s]
  1%|          | 645/92040 [03:20<7:48:16,  3.25it/s]
  1%|          | 646/92040 [03:20<7:47:09,  3.26it/s]
  1%|          | 647/92040 [03:21<7:46:59,  3.26it/s]
  1%|          | 648/92040 [03:21<7:46:42,  3.26it/s]
  1%|          | 649/92040 [03:21<7:45:43,  3.27it/s]
  1%|          | 650/92040 [03:22<7:45:11,  3.27it/s]
  1%|          | 651/92040 [03:22<7:45:22,  3.27it/s]
  1%|          | 652/92040 [03:22<7:45:15,  3.27it/s]
  1%|          | 653/92040 [03:22<7:44:52,  3.28it/s]
  1%|          | 654/92040 [03:23<7:44:23,  3.28it/s]
  1%|          | 655/92040 [03:23<7:44:38,  3.28it/s]
  1%|          | 656/92040 [03:23<7:44:26,  3.28it/s]
  1%|          | 657/92040 [03:24<7:44:03,  3.28it/s]
  1%|          | 658/92040 [03:24<7:44:13,  3.28it/s]
  1%|          | 659/92040 [03:24<7:44:35,  3.28it/s]
  1%|          | 660/92040 [03:25<7:44:55,  3.28it/s]
  1%|          | 661/92040 [03:25<7:45:33,  3.27it/s]
  1%|          | 662/92040 [03:25<7:45:26,  3.27it/s]
  1%|          | 663/92040 [03:26<7:45:34,  3.27it/s]
  1%|          | 664/92040 [03:26<7:45:19,  3.27it/s]
  1%|          | 665/92040 [03:26<7:44:54,  3.28it/s]
  1%|          | 666/92040 [03:26<7:44:54,  3.28it/s]
  1%|          | 667/92040 [03:27<7:44:47,  3.28it/s]
  1%|          | 668/92040 [03:27<8:49:34,  2.88it/s]
  1%|          | 669/92040 [03:27<8:30:59,  2.98it/s]
  1%|          | 670/92040 [03:28<8:17:26,  3.06it/s]
  1%|          | 671/92040 [03:28<8:07:36,  3.12it/s]
  1%|          | 672/92040 [03:28<8:00:51,  3.17it/s]
  1%|          | 673/92040 [03:29<7:56:07,  3.20it/s]
  1%|          | 674/92040 [03:29<7:52:49,  3.22it/s]
  1%|          | 675/92040 [03:29<7:50:28,  3.24it/s]
  1%|          | 676/92040 [03:30<7:48:49,  3.25it/s]
  1%|          | 677/92040 [03:30<7:47:43,  3.26it/s]
  1%|          | 678/92040 [03:30<7:47:09,  3.26it/s]
  1%|          | 679/92040 [03:31<7:46:03,  3.27it/s]
  1%|          | 680/92040 [03:31<7:45:57,  3.27it/s]
  1%|          | 681/92040 [03:31<7:45:39,  3.27it/s]
  1%|          | 682/92040 [03:31<7:45:36,  3.27it/s]
  1%|          | 683/92040 [03:32<7:45:14,  3.27it/s]
  1%|          | 684/92040 [03:32<7:45:12,  3.27it/s]
  1%|          | 685/92040 [03:32<7:45:10,  3.27it/s]
  1%|          | 686/92040 [03:33<7:44:48,  3.28it/s]
  1%|          | 687/92040 [03:33<7:44:25,  3.28it/s]
  1%|          | 688/92040 [03:33<7:44:27,  3.28it/s]
  1%|          | 689/92040 [03:34<7:44:35,  3.28it/s]
  1%|          | 690/92040 [03:34<7:44:43,  3.28it/s]
  1%|          | 691/92040 [03:34<7:44:23,  3.28it/s]
  1%|          | 692/92040 [03:35<7:44:31,  3.28it/s]
  1%|          | 693/92040 [03:35<7:44:45,  3.28it/s]
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  1%|          | 695/92040 [03:35<7:44:30,  3.28it/s]
  1%|          | 696/92040 [03:36<7:44:26,  3.28it/s]
  1%|          | 697/92040 [03:36<7:44:20,  3.28it/s]
  1%|          | 698/92040 [03:36<7:44:09,  3.28it/s]
  1%|          | 699/92040 [03:37<7:44:20,  3.28it/s]
  1%|          | 700/92040 [03:37<7:44:31,  3.28it/s]
  1%|          | 701/92040 [03:37<7:44:18,  3.28it/s]
  1%|          | 702/92040 [03:38<7:44:25,  3.28it/s]
  1%|          | 703/92040 [03:38<7:44:06,  3.28it/s]
  1%|          | 704/92040 [03:38<8:41:36,  2.92it/s]
  1%|          | 705/92040 [03:39<8:25:00,  3.01it/s]
  1%|          | 706/92040 [03:39<8:12:45,  3.09it/s]
  1%|          | 707/92040 [03:39<8:04:31,  3.14it/s]
  1%|          | 708/92040 [03:40<7:58:23,  3.18it/s]
  1%|          | 709/92040 [03:40<7:54:04,  3.21it/s]
  1%|          | 710/92040 [03:40<7:51:07,  3.23it/s]
  1%|          | 711/92040 [03:40<7:48:58,  3.25it/s]
  1%|          | 712/92040 [03:41<7:47:30,  3.26it/s]
  1%|          | 713/92040 [03:41<7:46:28,  3.26it/s]
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  1%|          | 715/92040 [03:42<7:45:50,  3.27it/s]
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  1%|          | 720/92040 [03:43<7:44:33,  3.28it/s]
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  1%|          | 768/92040 [03:58<7:44:28,  3.28it/s]
  1%|          | 769/92040 [03:58<7:44:12,  3.28it/s]
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  1%|          | 777/92040 [04:01<7:51:21,  3.23it/s]
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  1%|          | 780/92040 [04:02<7:46:50,  3.26it/s]
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  1%|          | 811/92040 [04:11<7:46:33,  3.26it/s]
  1%|          | 812/92040 [04:12<7:45:34,  3.27it/s]
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  1%|          | 841/92040 [04:21<8:03:49,  3.14it/s]
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  1%|          | 873/92040 [04:31<7:59:38,  3.17it/s]
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  1%|          | 888/92040 [04:35<7:43:29,  3.28it/s]
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  1%|          | 911/92040 [04:42<7:49:42,  3.23it/s]
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  1%|          | 922/92040 [04:46<7:43:26,  3.28it/s]
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  1%|          | 924/92040 [04:46<7:43:22,  3.28it/s]
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  1%|          | 1000/92040 [05:10<7:43:12,  3.28it/s]
                                                      

  1%|          | 1000/92040 [05:10<7:43:12,  3.28it/s]
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  2%|▏         | 1840/92040 [09:30<7:38:29,  3.28it/s]
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  2%|▏         | 1847/92040 [09:32<7:52:33,  3.18it/s]
  2%|▏         | 1848/92040 [09:32<7:48:37,  3.21it/s]
  2%|▏         | 1849/92040 [09:32<7:45:26,  3.23it/s]
  2%|▏         | 1850/92040 [09:33<7:42:46,  3.25it/s]
  2%|▏         | 1851/92040 [09:33<7:41:28,  3.26it/s]
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  2%|▏         | 1859/92040 [09:35<7:38:36,  3.28it/s]
  2%|▏         | 1860/92040 [09:36<7:38:59,  3.27it/s]
  2%|▏         | 1861/92040 [09:36<7:38:41,  3.28it/s]
  2%|▏         | 1862/92040 [09:36<7:38:17,  3.28it/s]
  2%|▏         | 1863/92040 [09:37<7:38:45,  3.28it/s]
  2%|▏         | 1864/92040 [09:37<7:38:43,  3.28it/s]
  2%|▏         | 1865/92040 [09:37<7:38:25,  3.28it/s]
  2%|▏         | 1866/92040 [09:38<7:38:15,  3.28it/s]
  2%|▏         | 1867/92040 [09:38<7:38:40,  3.28it/s]
  2%|▏         | 1868/92040 [09:38<7:38:14,  3.28it/s]
  2%|▏         | 1869/92040 [09:38<7:37:54,  3.28it/s]
  2%|▏         | 1870/92040 [09:39<7:37:58,  3.28it/s]
  2%|▏         | 1871/92040 [09:39<7:38:01,  3.28it/s]
  2%|▏         | 1872/92040 [09:39<7:38:45,  3.28it/s]
  2%|▏         | 1873/92040 [09:40<7:38:52,  3.27it/s]
  2%|▏         | 1874/92040 [09:40<8:44:03,  2.87it/s]
  2%|▏         | 1875/92040 [09:40<8:24:46,  2.98it/s]
  2%|▏         | 1876/92040 [09:41<8:10:51,  3.06it/s]
  2%|▏         | 1877/92040 [09:41<8:01:21,  3.12it/s]
  2%|▏         | 1878/92040 [09:41<7:54:41,  3.17it/s]
  2%|▏         | 1879/92040 [09:42<7:49:55,  3.20it/s]
  2%|▏         | 1880/92040 [09:42<7:46:14,  3.22it/s]
  2%|▏         | 1881/92040 [09:42<7:44:11,  3.24it/s]
  2%|▏         | 1882/92040 [09:43<7:42:24,  3.25it/s]
  2%|▏         | 1883/92040 [09:43<7:40:57,  3.26it/s]
  2%|▏         | 1884/92040 [09:43<7:39:53,  3.27it/s]
  2%|▏         | 1885/92040 [09:44<7:39:37,  3.27it/s]
  2%|▏         | 1886/92040 [09:44<7:39:26,  3.27it/s]
  2%|▏         | 1887/92040 [09:44<7:39:11,  3.27it/s]
  2%|▏         | 1888/92040 [09:44<7:38:57,  3.27it/s]
  2%|▏         | 1889/92040 [09:45<7:38:44,  3.28it/s]
  2%|▏         | 1890/92040 [09:45<7:38:44,  3.28it/s]
  2%|▏         | 1891/92040 [09:45<7:38:34,  3.28it/s]
  2%|▏         | 1892/92040 [09:46<7:38:05,  3.28it/s]
  2%|▏         | 1893/92040 [09:46<7:38:24,  3.28it/s]
  2%|▏         | 1894/92040 [09:46<7:38:54,  3.27it/s]
  2%|▏         | 1895/92040 [09:47<7:38:36,  3.28it/s]
  2%|▏         | 1896/92040 [09:47<7:38:22,  3.28it/s]
  2%|▏         | 1897/92040 [09:47<7:38:31,  3.28it/s]
  2%|▏         | 1898/92040 [09:47<7:38:30,  3.28it/s]
  2%|▏         | 1899/92040 [09:48<7:38:32,  3.28it/s]
  2%|▏         | 1900/92040 [09:48<7:38:06,  3.28it/s]
  2%|▏         | 1901/92040 [09:48<7:38:01,  3.28it/s]
  2%|▏         | 1902/92040 [09:49<7:38:16,  3.28it/s]
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  2%|▏         | 1905/92040 [09:50<7:38:39,  3.28it/s]
  2%|▏         | 1906/92040 [09:50<7:38:41,  3.28it/s]
  2%|▏         | 1907/92040 [09:50<7:38:35,  3.28it/s]
  2%|▏         | 1908/92040 [09:51<7:38:53,  3.27it/s]
  2%|▏         | 1909/92040 [09:51<7:38:41,  3.27it/s]
  2%|▏         | 1910/92040 [09:51<8:34:24,  2.92it/s]
  2%|▏         | 1911/92040 [09:52<8:17:56,  3.02it/s]
  2%|▏         | 1912/92040 [09:52<8:06:25,  3.09it/s]
  2%|▏         | 1913/92040 [09:52<7:58:08,  3.14it/s]
  2%|▏         | 1914/92040 [09:52<7:51:51,  3.18it/s]
  2%|▏         | 1915/92040 [09:53<7:47:45,  3.21it/s]
  2%|▏         | 1916/92040 [09:53<7:45:05,  3.23it/s]
  2%|▏         | 1917/92040 [09:53<7:43:06,  3.24it/s]
  2%|▏         | 1918/92040 [09:54<7:42:11,  3.25it/s]
  2%|▏         | 1919/92040 [09:54<7:41:24,  3.26it/s]
  2%|▏         | 1920/92040 [09:54<7:40:49,  3.26it/s]
  2%|▏         | 1921/92040 [09:55<7:40:00,  3.27it/s]
  2%|▏         | 1922/92040 [09:55<7:39:26,  3.27it/s]
  2%|▏         | 1923/92040 [09:55<7:38:59,  3.27it/s]
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  2%|▏         | 1927/92040 [09:56<7:38:47,  3.27it/s]
  2%|▏         | 1928/92040 [09:57<7:38:39,  3.27it/s]
  2%|▏         | 1929/92040 [09:57<7:38:06,  3.28it/s]
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  2%|▏         | 1931/92040 [09:58<7:38:22,  3.28it/s]
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  2%|▏         | 1941/92040 [10:01<8:42:51,  2.87it/s]
  2%|▏         | 1942/92040 [10:01<8:23:58,  2.98it/s]
  2%|▏         | 1943/92040 [10:01<8:10:20,  3.06it/s]
  2%|▏         | 1944/92040 [10:02<8:00:06,  3.13it/s]
  2%|▏         | 1945/92040 [10:02<7:53:25,  3.17it/s]
  2%|▏         | 1946/92040 [10:02<7:49:10,  3.20it/s]
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  2%|▏         | 1949/92040 [10:03<7:43:05,  3.24it/s]
  2%|▏         | 1950/92040 [10:04<7:41:35,  3.25it/s]
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  2%|▏         | 1952/92040 [10:04<7:39:48,  3.27it/s]
  2%|▏         | 1953/92040 [10:05<7:39:29,  3.27it/s]
  2%|▏         | 1954/92040 [10:05<7:39:22,  3.27it/s]
  2%|▏         | 1955/92040 [10:05<7:38:48,  3.27it/s]
  2%|▏         | 1956/92040 [10:05<7:38:39,  3.27it/s]
  2%|▏         | 1957/92040 [10:06<7:38:21,  3.28it/s]
  2%|▏         | 1958/92040 [10:06<7:38:10,  3.28it/s]
  2%|▏         | 1959/92040 [10:06<7:38:11,  3.28it/s]
  2%|▏         | 1960/92040 [10:07<7:37:57,  3.28it/s]
  2%|▏         | 1961/92040 [10:07<7:38:12,  3.28it/s]
  2%|▏         | 1962/92040 [10:07<7:37:57,  3.28it/s]
  2%|▏         | 1963/92040 [10:08<7:37:52,  3.28it/s]
  2%|▏         | 1964/92040 [10:08<7:37:59,  3.28it/s]
  2%|▏         | 1965/92040 [10:08<7:38:08,  3.28it/s]
  2%|▏         | 1966/92040 [10:09<7:38:17,  3.28it/s]
  2%|▏         | 1967/92040 [10:09<7:38:30,  3.27it/s]
  2%|▏         | 1968/92040 [10:09<7:38:34,  3.27it/s]
  2%|▏         | 1969/92040 [10:09<7:38:36,  3.27it/s]
  2%|▏         | 1970/92040 [10:10<7:38:18,  3.28it/s]
  2%|▏         | 1971/92040 [10:10<7:38:03,  3.28it/s]
  2%|▏         | 1972/92040 [10:10<7:38:10,  3.28it/s]
  2%|▏         | 1973/92040 [10:11<7:38:23,  3.27it/s]
  2%|▏         | 1974/92040 [10:11<7:38:03,  3.28it/s]
  2%|▏         | 1975/92040 [10:11<7:38:22,  3.27it/s]
  2%|▏         | 1976/92040 [10:12<7:38:25,  3.27it/s]
  2%|▏         | 1977/92040 [10:12<8:34:00,  2.92it/s]
  2%|▏         | 1978/92040 [10:12<8:17:21,  3.02it/s]
  2%|▏         | 1979/92040 [10:13<8:06:00,  3.09it/s]
  2%|▏         | 1980/92040 [10:13<7:57:32,  3.14it/s]
  2%|▏         | 1981/92040 [10:13<7:51:51,  3.18it/s]
  2%|▏         | 1982/92040 [10:14<7:47:39,  3.21it/s]
  2%|▏         | 1983/92040 [10:14<7:44:55,  3.23it/s]
  2%|▏         | 1984/92040 [10:14<7:42:58,  3.24it/s]
  2%|▏         | 1985/92040 [10:14<7:41:35,  3.25it/s]
  2%|▏         | 1986/92040 [10:15<7:41:00,  3.26it/s]
  2%|▏         | 1987/92040 [10:15<7:40:05,  3.26it/s]
  2%|▏         | 1988/92040 [10:15<7:39:12,  3.27it/s]
  2%|▏         | 1989/92040 [10:16<7:39:01,  3.27it/s]
  2%|▏         | 1990/92040 [10:16<7:38:41,  3.27it/s]
  2%|▏         | 1991/92040 [10:16<7:38:32,  3.27it/s]
  2%|▏         | 1992/92040 [10:17<7:38:29,  3.27it/s]
  2%|▏         | 1993/92040 [10:17<7:38:31,  3.27it/s]
  2%|▏         | 1994/92040 [10:17<7:38:31,  3.27it/s]
  2%|▏         | 1995/92040 [10:17<7:38:06,  3.28it/s]
  2%|▏         | 1996/92040 [10:18<7:37:51,  3.28it/s]
  2%|▏         | 1997/92040 [10:18<7:38:12,  3.28it/s]
  2%|▏         | 1998/92040 [10:18<7:38:20,  3.27it/s]
  2%|▏         | 1999/92040 [10:19<7:38:25,  3.27it/s]
  2%|▏         | 2000/92040 [10:19<7:38:37,  3.27it/s]
                                                      

  2%|▏         | 2000/92040 [10:19<7:38:37,  3.27it/s]
  2%|▏         | 2001/92040 [10:19<7:38:46,  3.27it/s]
  2%|▏         | 2002/92040 [10:20<7:38:30,  3.27it/s]
  2%|▏         | 2003/92040 [10:20<7:38:34,  3.27it/s]
  2%|▏         | 2004/92040 [10:20<7:38:27,  3.27it/s]
  2%|▏         | 2005/92040 [10:21<7:38:27,  3.27it/s]
  2%|▏         | 2006/92040 [10:21<7:38:07,  3.28it/s]
  2%|▏         | 2007/92040 [10:21<7:37:42,  3.28it/s]
  2%|▏         | 2008/92040 [10:22<8:43:07,  2.87it/s]
  2%|▏         | 2009/92040 [10:22<8:23:55,  2.98it/s]
  2%|▏         | 2010/92040 [10:22<8:09:47,  3.06it/s]
  2%|▏         | 2011/92040 [10:23<8:00:26,  3.12it/s]
  2%|▏         | 2012/92040 [10:23<7:53:35,  3.17it/s]
  2%|▏         | 2013/92040 [10:23<7:48:43,  3.20it/s]
  2%|▏         | 2014/92040 [10:23<7:45:31,  3.22it/s]
  2%|▏         | 2015/92040 [10:24<7:43:29,  3.24it/s]
  2%|▏         | 2016/92040 [10:24<7:41:54,  3.25it/s]
  2%|▏         | 2017/92040 [10:24<7:40:34,  3.26it/s]
  2%|▏         | 2018/92040 [10:25<7:39:57,  3.26it/s]
  2%|▏         | 2019/92040 [10:25<7:39:13,  3.27it/s]
  2%|▏         | 2020/92040 [10:25<7:38:55,  3.27it/s]
  2%|▏         | 2021/92040 [10:26<7:38:33,  3.27it/s]
  2%|▏         | 2022/92040 [10:26<7:38:19,  3.27it/s]
  2%|▏         | 2023/92040 [10:26<7:37:56,  3.28it/s]
  2%|▏         | 2024/92040 [10:26<7:37:48,  3.28it/s]
  2%|▏         | 2025/92040 [10:27<7:37:30,  3.28it/s]
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  5%|▌         | 5000/92040 [25:47<7:26:15,  3.25it/s]{'loss': 0.4321, 'learning_rate': 1.978270317253368e-05, 'epoch': 0.16}
{'loss': 0.3732, 'learning_rate': 1.9565406345067362e-05, 'epoch': 0.33}
{'loss': 0.3554, 'learning_rate': 1.9348109517601044e-05, 'epoch': 0.49}
{'loss': 0.3428, 'learning_rate': 1.9130812690134726e-05, 'epoch': 0.65}
{'loss': 0.3374, 'learning_rate': 1.8913515862668405e-05, 'epoch': 0.81}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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  8%|▊         | 7658/92040 [39:58<8:24:57,  2.79it/s]
  8%|▊         | 7659/92040 [39:59<8:02:00,  2.92it/s]
  8%|▊         | 7660/92040 [39:59<7:46:01,  3.02it/s]
  8%|▊         | 7661/92040 [39:59<7:35:02,  3.09it/s]
  8%|▊         | 7662/92040 [39:59<7:27:19,  3.14it/s]
  8%|▊         | 7663/92040 [40:00<7:21:44,  3.18it/s]
  8%|▊         | 7664/92040 [40:00<7:17:55,  3.21it/s]
  8%|▊         | 7665/92040 [40:00<7:15:20,  3.23it/s]
  8%|▊         | 7666/92040 [40:01<7:13:33,  3.24it/s]
  8%|▊         | 7667/92040 [40:01<7:12:08,  3.25it/s]
  8%|▊         | 7668/92040 [40:01<7:11:10,  3.26it/s]
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  8%|▊         | 7670/92040 [40:02<7:10:22,  3.27it/s]
  8%|▊         | 7671/92040 [40:02<7:09:57,  3.27it/s]
  8%|▊         | 7672/92040 [40:02<7:09:47,  3.27it/s]
  8%|▊         | 7673/92040 [40:03<7:09:36,  3.27it/s]
  8%|▊         | 7674/92040 [40:03<7:09:16,  3.28it/s]
  8%|▊         | 7675/92040 [40:03<7:09:26,  3.27it/s]
  8%|▊         | 7676/92040 [40:04<7:09:21,  3.27it/s]
  8%|▊         | 7677/92040 [40:04<7:09:18,  3.28it/s]
  8%|▊         | 7678/92040 [40:04<7:08:59,  3.28it/s]
  8%|▊         | 7679/92040 [40:05<7:08:47,  3.28it/s]
  8%|▊         | 7680/92040 [40:05<7:08:55,  3.28it/s]
  8%|▊         | 7681/92040 [40:05<7:09:18,  3.28it/s]
  8%|▊         | 7682/92040 [40:06<7:09:22,  3.27it/s]
  8%|▊         | 7683/92040 [40:06<7:08:57,  3.28it/s]
  8%|▊         | 7684/92040 [40:06<7:08:52,  3.28it/s]
  8%|▊         | 7685/92040 [40:06<7:09:02,  3.28it/s]
  8%|▊         | 7686/92040 [40:07<7:08:58,  3.28it/s]
  8%|▊         | 7687/92040 [40:07<7:09:18,  3.27it/s]
  8%|▊         | 7688/92040 [40:07<7:09:23,  3.27it/s]
  8%|▊         | 7689/92040 [40:08<7:09:40,  3.27it/s]
  8%|▊         | 7690/92040 [40:08<7:09:04,  3.28it/s]
  8%|▊         | 7691/92040 [40:08<7:08:41,  3.28it/s]
  8%|▊         | 7692/92040 [40:09<7:08:41,  3.28it/s]
  8%|▊         | 7693/92040 [40:09<7:09:14,  3.28it/s]
  8%|▊         | 7694/92040 [40:09<8:24:47,  2.78it/s]
  8%|▊         | 7695/92040 [40:10<8:02:03,  2.92it/s]
  8%|▊         | 7696/92040 [40:10<7:46:18,  3.01it/s]
  8%|▊         | 7697/92040 [40:10<7:35:08,  3.09it/s]
  8%|▊         | 7698/92040 [40:11<7:27:13,  3.14it/s]
  8%|▊         | 7699/92040 [40:11<7:22:22,  3.18it/s]
  8%|▊         | 7700/92040 [40:11<7:18:13,  3.21it/s]
  8%|▊         | 7701/92040 [40:12<7:15:34,  3.23it/s]
  8%|▊         | 7702/92040 [40:12<7:13:19,  3.24it/s]
  8%|▊         | 7703/92040 [40:12<7:11:56,  3.25it/s]
  8%|▊         | 7704/92040 [40:12<7:11:10,  3.26it/s]
  8%|▊         | 7705/92040 [40:13<7:10:33,  3.26it/s]
  8%|▊         | 7706/92040 [40:13<7:10:21,  3.27it/s]
  8%|▊         | 7707/92040 [40:13<7:09:54,  3.27it/s]
  8%|▊         | 7708/92040 [40:14<7:09:45,  3.27it/s]
  8%|▊         | 7709/92040 [40:14<7:09:22,  3.27it/s]
  8%|▊         | 7710/92040 [40:14<7:09:08,  3.28it/s]
  8%|▊         | 7711/92040 [40:15<7:09:08,  3.28it/s]
  8%|▊         | 7712/92040 [40:15<7:09:13,  3.27it/s]
  8%|▊         | 7713/92040 [40:15<7:08:57,  3.28it/s]
  8%|▊         | 7714/92040 [40:15<7:09:06,  3.28it/s]
  8%|▊         | 7715/92040 [40:16<7:09:06,  3.28it/s]
  8%|▊         | 7716/92040 [40:16<7:09:09,  3.27it/s]
  8%|▊         | 7717/92040 [40:16<7:08:54,  3.28it/s]
  8%|▊         | 7718/92040 [40:17<7:09:14,  3.27it/s]
  8%|▊         | 7719/92040 [40:17<7:09:01,  3.28it/s]
  8%|▊         | 7720/92040 [40:17<7:08:54,  3.28it/s]
  8%|▊         | 7721/92040 [40:18<7:08:43,  3.28it/s]
  8%|▊         | 7722/92040 [40:18<7:08:58,  3.28it/s]
  8%|▊         | 7723/92040 [40:18<7:09:15,  3.27it/s]
  8%|▊         | 7724/92040 [40:19<7:09:14,  3.27it/s]
  8%|▊         | 7725/92040 [40:19<8:24:12,  2.79it/s]
  8%|▊         | 7726/92040 [40:19<8:01:35,  2.92it/s]
  8%|▊         | 7727/92040 [40:20<7:45:51,  3.02it/s]
  8%|▊         | 7728/92040 [40:20<7:34:36,  3.09it/s]
  8%|▊         | 7729/92040 [40:20<7:26:59,  3.14it/s]
  8%|▊         | 7730/92040 [40:21<7:21:53,  3.18it/s]
  8%|▊         | 7731/92040 [40:21<7:18:05,  3.21it/s]
  8%|▊         | 7732/92040 [40:21<7:15:04,  3.23it/s]
  8%|▊         | 7733/92040 [40:21<7:13:03,  3.24it/s]
  8%|▊         | 7734/92040 [40:22<7:11:35,  3.26it/s]
  8%|▊         | 7735/92040 [40:22<7:11:01,  3.26it/s]
  8%|▊         | 7736/92040 [40:22<7:10:08,  3.27it/s]
  8%|▊         | 7737/92040 [40:23<7:09:22,  3.27it/s]
  8%|▊         | 7738/92040 [40:23<7:09:38,  3.27it/s]
  8%|▊         | 7739/92040 [40:23<7:09:22,  3.27it/s]
  8%|▊         | 7740/92040 [40:24<7:09:15,  3.27it/s]
  8%|▊         | 7741/92040 [40:24<7:08:59,  3.28it/s]
  8%|▊         | 7742/92040 [40:24<7:09:11,  3.27it/s]
  8%|▊         | 7743/92040 [40:25<7:09:08,  3.27it/s]
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  8%|▊         | 7747/92040 [40:26<7:08:57,  3.28it/s]
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  8%|▊         | 7750/92040 [40:27<7:09:01,  3.27it/s]
  8%|▊         | 7751/92040 [40:27<7:08:51,  3.28it/s]
  8%|▊         | 7752/92040 [40:27<7:08:38,  3.28it/s]
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  8%|▊         | 7757/92040 [40:29<7:09:10,  3.27it/s]
  8%|▊         | 7758/92040 [40:29<7:08:50,  3.28it/s]
  8%|▊         | 7759/92040 [40:29<7:08:32,  3.28it/s]
  8%|▊         | 7760/92040 [40:30<7:09:15,  3.27it/s]
  8%|▊         | 7761/92040 [40:30<8:24:58,  2.78it/s]
  8%|▊         | 7762/92040 [40:30<8:02:04,  2.91it/s]
  8%|▊         | 7763/92040 [40:31<7:45:40,  3.02it/s]
  8%|▊         | 7764/92040 [40:31<7:34:28,  3.09it/s]
  8%|▊         | 7765/92040 [40:31<7:26:49,  3.14it/s]
  8%|▊         | 7766/92040 [40:32<7:20:58,  3.19it/s]
  8%|▊         | 7767/92040 [40:32<7:16:50,  3.22it/s]
  8%|▊         | 7768/92040 [40:32<7:14:40,  3.23it/s]
  8%|▊         | 7769/92040 [40:33<7:13:07,  3.24it/s]
  8%|▊         | 7770/92040 [40:33<7:12:00,  3.25it/s]
  8%|▊         | 7771/92040 [40:33<7:10:46,  3.26it/s]
  8%|▊         | 7772/92040 [40:34<7:10:28,  3.26it/s]
  8%|▊         | 7773/92040 [40:34<7:10:05,  3.27it/s]
  8%|▊         | 7774/92040 [40:34<7:09:39,  3.27it/s]
  8%|▊         | 7775/92040 [40:34<7:09:20,  3.27it/s]
  8%|▊         | 7776/92040 [40:35<7:09:32,  3.27it/s]
  8%|▊         | 7777/92040 [40:35<7:09:19,  3.27it/s]
  8%|▊         | 7778/92040 [40:35<7:08:44,  3.28it/s]
  8%|▊         | 7779/92040 [40:36<7:09:08,  3.27it/s]
  8%|▊         | 7780/92040 [40:36<7:09:01,  3.27it/s]
  8%|▊         | 7781/92040 [40:36<7:08:42,  3.28it/s]
  8%|▊         | 7782/92040 [40:37<7:08:43,  3.28it/s]
  8%|▊         | 7783/92040 [40:37<7:08:31,  3.28it/s]
  8%|▊         | 7784/92040 [40:37<7:08:32,  3.28it/s]
  8%|▊         | 7785/92040 [40:38<7:08:41,  3.28it/s]
  8%|▊         | 7786/92040 [40:38<7:08:31,  3.28it/s]
  8%|▊         | 7787/92040 [40:38<7:08:44,  3.28it/s]
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  8%|▊         | 7789/92040 [40:39<7:08:48,  3.27it/s]
  8%|▊         | 7790/92040 [40:39<7:08:32,  3.28it/s]
  8%|▊         | 7791/92040 [40:39<7:08:44,  3.28it/s]
  8%|▊         | 7792/92040 [40:40<8:23:46,  2.79it/s]
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  8%|▊         | 7794/92040 [40:40<7:45:37,  3.02it/s]
  8%|▊         | 7795/92040 [40:41<7:34:53,  3.09it/s]
  8%|▊         | 7796/92040 [40:41<7:26:50,  3.14it/s]
  8%|▊         | 7797/92040 [40:41<7:21:39,  3.18it/s]
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  8%|▊         | 7799/92040 [40:42<7:14:59,  3.23it/s]
  8%|▊         | 7800/92040 [40:42<7:12:54,  3.24it/s]
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  8%|▊         | 7809/92040 [40:45<7:08:13,  3.28it/s]
  8%|▊         | 7810/92040 [40:45<7:08:25,  3.28it/s]
  8%|▊         | 7811/92040 [40:46<7:08:39,  3.27it/s]
  8%|▊         | 7812/92040 [40:46<7:08:23,  3.28it/s]
  8%|▊         | 7813/92040 [40:46<7:08:34,  3.28it/s]
  8%|▊         | 7814/92040 [40:47<7:08:43,  3.27it/s]
  8%|▊         | 7815/92040 [40:47<7:08:44,  3.27it/s]
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  8%|▊         | 7818/92040 [40:48<7:08:52,  3.27it/s]
  8%|▊         | 7819/92040 [40:48<7:08:28,  3.28it/s]
  8%|▊         | 7820/92040 [40:48<7:08:20,  3.28it/s]
  8%|▊         | 7821/92040 [40:49<7:08:32,  3.28it/s]
  8%|▊         | 7822/92040 [40:49<7:08:42,  3.27it/s]
  8%|▊         | 7823/92040 [40:49<7:08:32,  3.28it/s]
  9%|▊         | 7824/92040 [40:50<7:08:14,  3.28it/s]
  9%|▊         | 7825/92040 [40:50<7:08:18,  3.28it/s]
  9%|▊         | 7826/92040 [40:50<7:08:17,  3.28it/s]
  9%|▊         | 7827/92040 [40:51<7:08:02,  3.28it/s]
  9%|▊         | 7828/92040 [40:51<8:23:03,  2.79it/s]
  9%|▊         | 7829/92040 [40:51<8:00:48,  2.92it/s]
  9%|▊         | 7830/92040 [40:52<7:45:13,  3.02it/s]
  9%|▊         | 7831/92040 [40:52<7:33:55,  3.09it/s]
  9%|▊         | 7832/92040 [40:52<7:25:47,  3.15it/s]
  9%|▊         | 7833/92040 [40:53<7:20:51,  3.18it/s]
  9%|▊         | 7834/92040 [40:53<7:17:14,  3.21it/s]
  9%|▊         | 7835/92040 [40:53<7:14:28,  3.23it/s]
  9%|▊         | 7836/92040 [40:53<7:12:46,  3.24it/s]
  9%|▊         | 7837/92040 [40:54<7:11:36,  3.25it/s]
  9%|▊         | 7838/92040 [40:54<7:10:24,  3.26it/s]
  9%|▊         | 7839/92040 [40:54<7:09:46,  3.27it/s]
  9%|▊         | 7840/92040 [40:55<7:09:34,  3.27it/s]
  9%|▊         | 7841/92040 [40:55<7:09:03,  3.27it/s]
  9%|▊         | 7842/92040 [40:55<7:08:50,  3.27it/s]
  9%|▊         | 7843/92040 [40:56<7:08:30,  3.27it/s]
  9%|▊         | 7844/92040 [40:56<7:08:11,  3.28it/s]
  9%|▊         | 7845/92040 [40:56<7:08:30,  3.27it/s]
  9%|▊         | 7846/92040 [40:56<7:08:42,  3.27it/s]
  9%|▊         | 7847/92040 [40:57<7:08:16,  3.28it/s]
  9%|▊         | 7848/92040 [40:57<7:07:47,  3.28it/s]
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  9%|▊         | 7851/92040 [40:58<7:08:03,  3.28it/s]
  9%|▊         | 7852/92040 [40:58<7:08:15,  3.28it/s]
  9%|▊         | 7853/92040 [40:59<7:08:32,  3.27it/s]
  9%|▊         | 7854/92040 [40:59<7:08:36,  3.27it/s]
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  9%|▊         | 7857/92040 [41:00<7:08:13,  3.28it/s]
  9%|▊         | 7858/92040 [41:00<7:08:23,  3.28it/s]
  9%|▊         | 7859/92040 [41:01<8:22:56,  2.79it/s]
  9%|▊         | 7860/92040 [41:01<8:00:52,  2.92it/s]
  9%|▊         | 7861/92040 [41:01<7:45:12,  3.02it/s]
  9%|▊         | 7862/92040 [41:02<7:34:10,  3.09it/s]
  9%|▊         | 7863/92040 [41:02<7:26:23,  3.14it/s]
  9%|▊         | 7864/92040 [41:02<7:21:18,  3.18it/s]
  9%|▊         | 7865/92040 [41:02<7:17:34,  3.21it/s]
  9%|▊         | 7866/92040 [41:03<7:14:34,  3.23it/s]
  9%|▊         | 7867/92040 [41:03<7:12:27,  3.24it/s]
  9%|▊         | 7868/92040 [41:03<7:10:52,  3.26it/s]
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  9%|▊         | 7870/92040 [41:04<7:09:28,  3.27it/s]
  9%|▊         | 7871/92040 [41:04<7:09:01,  3.27it/s]
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  9%|▊         | 7874/92040 [41:05<7:08:10,  3.28it/s]
  9%|▊         | 7875/92040 [41:06<7:08:37,  3.27it/s]
  9%|▊         | 7876/92040 [41:06<7:08:40,  3.27it/s]
  9%|▊         | 7877/92040 [41:06<7:08:36,  3.27it/s]
  9%|▊         | 7878/92040 [41:06<7:08:34,  3.27it/s]
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  9%|▊         | 7880/92040 [41:07<7:08:27,  3.27it/s]
  9%|▊         | 7881/92040 [41:07<7:08:10,  3.28it/s]
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 11%|█         | 10000/92040 [52:05<6:57:13,  3.28it/s]{'eval_loss': 0.3118935823440552, 'eval_accuracy': 0.8835455934793683, 'eval_f1': 0.8837898427357328, 'eval_runtime': 15.291, 'eval_samples_per_second': 641.881, 'eval_steps_per_second': 5.036, 'epoch': 0.81}
{'loss': 0.3225, 'learning_rate': 1.8696219035202087e-05, 'epoch': 0.98}
{'loss': 0.2083, 'learning_rate': 1.847892220773577e-05, 'epoch': 1.14}
{'loss': 0.1947, 'learning_rate': 1.826162538026945e-05, 'epoch': 1.3}
{'loss': 0.2019, 'learning_rate': 1.804432855280313e-05, 'epoch': 1.47}
{'loss': 0.1996, 'learning_rate': 1.7827031725336812e-05, 'epoch': 1.63}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 16%|█▋        | 15000/92040 [1:18:15<6:31:10,  3.28it/s]{'eval_loss': 0.3696001470088959, 'eval_accuracy': 0.8875191034131431, 'eval_f1': 0.8876502826421026, 'eval_runtime': 15.2729, 'eval_samples_per_second': 642.643, 'eval_steps_per_second': 5.042, 'epoch': 1.63}
{'loss': 0.2057, 'learning_rate': 1.760973489787049e-05, 'epoch': 1.79}
{'loss': 0.206, 'learning_rate': 1.7392438070404173e-05, 'epoch': 1.96}
{'loss': 0.1288, 'learning_rate': 1.7175141242937855e-05, 'epoch': 2.12}
{'loss': 0.1068, 'learning_rate': 1.6957844415471537e-05, 'epoch': 2.28}
{'loss': 0.111, 'learning_rate': 1.6740547588005215e-05, 'epoch': 2.44}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 22%|██▏       | 20000/92040 [1:44:31<6:10:48,  3.24it/s]{'eval_loss': 0.4819681644439697, 'eval_accuracy': 0.8809984717269486, 'eval_f1': 0.8817145010430567, 'eval_runtime': 15.2444, 'eval_samples_per_second': 643.845, 'eval_steps_per_second': 5.051, 'epoch': 2.44}
{'loss': 0.1097, 'learning_rate': 1.6523250760538898e-05, 'epoch': 2.61}
{'loss': 0.117, 'learning_rate': 1.630595393307258e-05, 'epoch': 2.77}
{'loss': 0.1197, 'learning_rate': 1.608865710560626e-05, 'epoch': 2.93}
{'loss': 0.085, 'learning_rate': 1.587136027813994e-05, 'epoch': 3.1}
{'loss': 0.0685, 'learning_rate': 1.5654063450673622e-05, 'epoch': 3.26}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 27%|██▋       | 25000/92040 [2:10:39<5:40:11,  3.28it/s]{'eval_loss': 0.5799844861030579, 'eval_accuracy': 0.8799796230259807, 'eval_f1': 0.8805390034945557, 'eval_runtime': 15.2392, 'eval_samples_per_second': 644.062, 'eval_steps_per_second': 5.053, 'epoch': 3.26}
{'loss': 0.0714, 'learning_rate': 1.54367666232073e-05, 'epoch': 3.42}
{'loss': 0.0725, 'learning_rate': 1.5219469795740985e-05, 'epoch': 3.59}
{'loss': 0.0743, 'learning_rate': 1.5002172968274663e-05, 'epoch': 3.75}
{'loss': 0.0801, 'learning_rate': 1.4784876140808346e-05, 'epoch': 3.91}
{'loss': 0.0598, 'learning_rate': 1.4567579313342026e-05, 'epoch': 4.07}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 33%|███▎      | 30000/92040 [2:36:49<5:33:16,  3.10it/s]{'eval_loss': 0.6595990657806396, 'eval_accuracy': 0.8825267447784004, 'eval_f1': 0.8829319189085586, 'eval_runtime': 15.4056, 'eval_samples_per_second': 637.107, 'eval_steps_per_second': 4.998, 'epoch': 4.07}
{'loss': 0.0484, 'learning_rate': 1.4350282485875708e-05, 'epoch': 4.24}
{'loss': 0.0522, 'learning_rate': 1.4132985658409388e-05, 'epoch': 4.4}
{'loss': 0.0509, 'learning_rate': 1.391568883094307e-05, 'epoch': 4.56}
{'loss': 0.0541, 'learning_rate': 1.369839200347675e-05, 'epoch': 4.73}
{'loss': 0.056, 'learning_rate': 1.3481095176010431e-05, 'epoch': 4.89}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0489, 'learning_rate': 1.3263798348544113e-05, 'epoch': 5.05}
{'loss': 0.0377, 'learning_rate': 1.3046501521077792e-05, 'epoch': 5.22}
{'loss': 0.0381, 'learning_rate': 1.2829204693611474e-05, 'epoch': 5.38}
{'loss': 0.0433, 'learning_rate': 1.2611907866145154e-05, 'epoch': 5.54}
{'loss': 0.042, 'learning_rate': 1.2394611038678836e-05, 'epoch': 5.7}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 43%|████▎     | 40000/92040 [3:29:08<4:24:55,  3.27it/s]{'eval_loss': 0.664587140083313, 'eval_accuracy': 0.8836474783494651, 'eval_f1': 0.8840673477804971, 'eval_runtime': 15.4082, 'eval_samples_per_second': 637.0, 'eval_steps_per_second': 4.997, 'epoch': 5.7}
{'loss': 0.04, 'learning_rate': 1.2177314211212517e-05, 'epoch': 5.87}
{'loss': 0.0393, 'learning_rate': 1.1960017383746199e-05, 'epoch': 6.03}
{'loss': 0.0284, 'learning_rate': 1.1742720556279879e-05, 'epoch': 6.19}
{'loss': 0.0301, 'learning_rate': 1.1525423728813561e-05, 'epoch': 6.36}
{'loss': 0.0311, 'learning_rate': 1.130812690134724e-05, 'epoch': 6.52}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 49%|████▉     | 45000/92040 [3:55:18<3:58:28,  3.29it/s]{'eval_loss': 0.7419875860214233, 'eval_accuracy': 0.8817116658176261, 'eval_f1': 0.8821460361168072, 'eval_runtime': 15.4044, 'eval_samples_per_second': 637.157, 'eval_steps_per_second': 4.999, 'epoch': 6.52}
{'loss': 0.0326, 'learning_rate': 1.1090830073880923e-05, 'epoch': 6.68}
{'loss': 0.0322, 'learning_rate': 1.0873533246414602e-05, 'epoch': 6.84}
{'loss': 0.0334, 'learning_rate': 1.0656236418948284e-05, 'epoch': 7.01}
{'loss': 0.0208, 'learning_rate': 1.0438939591481965e-05, 'epoch': 7.17}
{'loss': 0.0231, 'learning_rate': 1.0221642764015647e-05, 'epoch': 7.33}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0242, 'learning_rate': 1.0004345936549327e-05, 'epoch': 7.5}
{'loss': 0.0245, 'learning_rate': 9.787049109083007e-06, 'epoch': 7.66}
{'loss': 0.0235, 'learning_rate': 9.56975228161669e-06, 'epoch': 7.82}
{'loss': 0.0263, 'learning_rate': 9.35245545415037e-06, 'epoch': 7.99}
{'loss': 0.0153, 'learning_rate': 9.13515862668405e-06, 'epoch': 8.15}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 60%|█████▉    | 55000/92040 [4:47:37<3:08:07,  3.28it/s]{'eval_loss': 0.9248143434524536, 'eval_accuracy': 0.8830361691288844, 'eval_f1': 0.883734984670827, 'eval_runtime': 15.2775, 'eval_samples_per_second': 642.449, 'eval_steps_per_second': 5.04, 'epoch': 8.15}
{'loss': 0.0176, 'learning_rate': 8.917861799217732e-06, 'epoch': 8.31}
{'loss': 0.0188, 'learning_rate': 8.700564971751413e-06, 'epoch': 8.47}
{'loss': 0.0179, 'learning_rate': 8.483268144285095e-06, 'epoch': 8.64}
{'loss': 0.0212, 'learning_rate': 8.265971316818775e-06, 'epoch': 8.8}
{'loss': 0.0184, 'learning_rate': 8.048674489352455e-06, 'epoch': 8.96}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 65%|██████▌   | 60000/92040 [5:13:47<2:46:43,  3.20it/s]{'eval_loss': 0.8769911527633667, 'eval_accuracy': 0.8860927152317881, 'eval_f1': 0.8864049981221, 'eval_runtime': 15.2577, 'eval_samples_per_second': 643.282, 'eval_steps_per_second': 5.047, 'epoch': 8.96}
{'loss': 0.0149, 'learning_rate': 7.831377661886137e-06, 'epoch': 9.13}
{'loss': 0.0146, 'learning_rate': 7.614080834419818e-06, 'epoch': 9.29}
{'loss': 0.0143, 'learning_rate': 7.396784006953499e-06, 'epoch': 9.45}
{'loss': 0.0147, 'learning_rate': 7.17948717948718e-06, 'epoch': 9.62}
{'loss': 0.0153, 'learning_rate': 6.962190352020861e-06, 'epoch': 9.78}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 71%|███████   | 65000/92040 [5:39:57<2:17:25,  3.28it/s]{'eval_loss': 0.8489966988563538, 'eval_accuracy': 0.8867040244523688, 'eval_f1': 0.8872135300037466, 'eval_runtime': 15.0929, 'eval_samples_per_second': 650.305, 'eval_steps_per_second': 5.102, 'epoch': 9.78}
{'loss': 0.016, 'learning_rate': 6.7448935245545425e-06, 'epoch': 9.94}
{'loss': 0.0116, 'learning_rate': 6.527596697088223e-06, 'epoch': 10.1}
{'loss': 0.0098, 'learning_rate': 6.310299869621904e-06, 'epoch': 10.27}
{'loss': 0.0114, 'learning_rate': 6.093003042155585e-06, 'epoch': 10.43}
{'loss': 0.0117, 'learning_rate': 5.8757062146892665e-06, 'epoch': 10.59}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0085, 'learning_rate': 5.658409387222948e-06, 'epoch': 10.76}
{'loss': 0.0108, 'learning_rate': 5.441112559756628e-06, 'epoch': 10.92}
{'loss': 0.0087, 'learning_rate': 5.223815732290309e-06, 'epoch': 11.08}
{'loss': 0.0077, 'learning_rate': 5.0065189048239905e-06, 'epoch': 11.25}
{'loss': 0.0082, 'learning_rate': 4.789222077357671e-06, 'epoch': 11.41}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 81%|████████▏ | 75000/92040 [6:32:49<1:29:34,  3.17it/s]{'eval_loss': 1.021010160446167, 'eval_accuracy': 0.8842587875700458, 'eval_f1': 0.8848599074279394, 'eval_runtime': 15.4268, 'eval_samples_per_second': 636.23, 'eval_steps_per_second': 4.991, 'epoch': 11.41}
{'loss': 0.0094, 'learning_rate': 4.571925249891352e-06, 'epoch': 11.57}
{'loss': 0.0091, 'learning_rate': 4.354628422425033e-06, 'epoch': 11.73}
{'loss': 0.0078, 'learning_rate': 4.137331594958714e-06, 'epoch': 11.9}
{'loss': 0.008, 'learning_rate': 3.920034767492395e-06, 'epoch': 12.06}
{'loss': 0.0051, 'learning_rate': 3.702737940026076e-06, 'epoch': 12.22}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 87%|████████▋ | 80000/92040 [6:59:19<1:02:35,  3.21it/s]{'eval_loss': 1.0511906147003174, 'eval_accuracy': 0.8857870606214977, 'eval_f1': 0.8863066314389447, 'eval_runtime': 15.4296, 'eval_samples_per_second': 636.115, 'eval_steps_per_second': 4.99, 'epoch': 12.22}
{'loss': 0.0051, 'learning_rate': 3.4854411125597572e-06, 'epoch': 12.39}
{'loss': 0.0073, 'learning_rate': 3.268144285093438e-06, 'epoch': 12.55}
{'loss': 0.0051, 'learning_rate': 3.0508474576271192e-06, 'epoch': 12.71}
{'loss': 0.0065, 'learning_rate': 2.8335506301608e-06, 'epoch': 12.87}
{'loss': 0.005, 'learning_rate': 2.6162538026944812e-06, 'epoch': 13.04}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 92%|█████████▏| 85000/92040 [7:25:46<35:57,  3.26it/s]{'eval_loss': 1.1302615404129028, 'eval_accuracy': 0.8847682119205298, 'eval_f1': 0.8853688525781016, 'eval_runtime': 15.3495, 'eval_samples_per_second': 639.436, 'eval_steps_per_second': 5.016, 'epoch': 13.04}
{'loss': 0.0056, 'learning_rate': 2.398956975228162e-06, 'epoch': 13.2}
{'loss': 0.0045, 'learning_rate': 2.1816601477618428e-06, 'epoch': 13.36}
{'loss': 0.0049, 'learning_rate': 1.964363320295524e-06, 'epoch': 13.53}
{'loss': 0.0051, 'learning_rate': 1.7470664928292048e-06, 'epoch': 13.69}
{'loss': 0.0038, 'learning_rate': 1.5297696653628858e-06, 'epoch': 13.85}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0038, 'learning_rate': 1.3124728378965668e-06, 'epoch': 14.02}
{'loss': 0.0034, 'learning_rate': 1.0951760104302478e-06, 'epoch': 14.18}
{'loss': 0.0032, 'learning_rate': 8.778791829639289e-07, 'epoch': 14.34}
{'loss': 0.0029, 'learning_rate': 6.605823554976099e-07, 'epoch': 14.5}
{'loss': 0.0034, 'learning_rate': 4.432855280312908e-07, 'epoch': 14.67}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'eval_loss': 1.1526310443878174, 'eval_accuracy': 0.8873153336729496, 'eval_f1': 0.8878356537419584, 'eval_runtime': 15.4246, 'eval_samples_per_second': 636.319, 'eval_steps_per_second': 4.992, 'epoch': 14.67}
{'loss': 0.0036, 'learning_rate': 2.2598870056497177e-07, 'epoch': 14.83}
{'loss': 0.0035, 'learning_rate': 8.691873098652761e-09, 'epoch': 14.99}
{'train_runtime': 28994.3923, 'train_samples_per_second': 203.161, 'train_steps_per_second': 3.174, 'train_loss': 0.06376564154212809, 'epoch': 15.0}
➡️  Saving best model to ./deberta_v3_large_snli_mnli …
➡️  Tokenizing ANLI (all rounds) …

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/home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/accelerate/accelerator.py:436: FutureWarning: Passing the following arguments to `Accelerator` is deprecated and will be removed in version 1.0 of Accelerate: dict_keys(['dispatch_batches', 'split_batches']). Please pass an `accelerate.DataLoaderConfiguration` instead: 
dataloader_config = DataLoaderConfiguration(dispatch_batches=None, split_batches=False)
  warnings.warn(
➡️  Loading model from MNLI checkpoint for ANLI …
➡️  Starting fine-tuning on ANLI …

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  warnings.warn('Was asked to gather along dimension 0, but all '

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  5%|▌         | 2000/38175 [10:31<3:06:50,  3.23it/s]{'loss': 0.5474, 'learning_rate': 9.895219384413884e-06, 'epoch': 0.16}
{'loss': 0.4248, 'learning_rate': 9.790438768827768e-06, 'epoch': 0.31}
{'loss': 0.3967, 'learning_rate': 9.685658153241651e-06, 'epoch': 0.47}
{'loss': 0.3788, 'learning_rate': 9.580877537655534e-06, 'epoch': 0.63}
{'loss': 0.3742, 'learning_rate': 9.476096922069417e-06, 'epoch': 0.79}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 10%|█         | 4000/38175 [21:40<3:23:18,  2.80it/s]{'eval_loss': 1.198674201965332, 'eval_accuracy': 0.5271875, 'eval_f1': 0.5242485711223912, 'eval_runtime': 5.1249, 'eval_samples_per_second': 624.401, 'eval_steps_per_second': 4.878, 'epoch': 0.79}
{'loss': 0.3563, 'learning_rate': 9.371316306483302e-06, 'epoch': 0.94}
{'loss': 0.2679, 'learning_rate': 9.266535690897185e-06, 'epoch': 1.1}
{'loss': 0.2175, 'learning_rate': 9.161755075311068e-06, 'epoch': 1.26}
{'loss': 0.2172, 'learning_rate': 9.056974459724952e-06, 'epoch': 1.41}
{'loss': 0.221, 'learning_rate': 8.952193844138835e-06, 'epoch': 1.57}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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 16%|█▌        | 6000/38175 [34:06<3:55:07,  2.28it/s]{'eval_loss': 1.3090920448303223, 'eval_accuracy': 0.5671875, 'eval_f1': 0.5647004723044072, 'eval_runtime': 5.8612, 'eval_samples_per_second': 545.964, 'eval_steps_per_second': 4.265, 'epoch': 1.57}
{'loss': 0.2181, 'learning_rate': 8.847413228552718e-06, 'epoch': 1.73}
{'loss': 0.2231, 'learning_rate': 8.742632612966601e-06, 'epoch': 1.89}
{'loss': 0.1913, 'learning_rate': 8.637851997380486e-06, 'epoch': 2.04}
{'loss': 0.1182, 'learning_rate': 8.533071381794369e-06, 'epoch': 2.2}
{'loss': 0.1191, 'learning_rate': 8.428290766208253e-06, 'epoch': 2.36}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.1199, 'learning_rate': 8.323510150622136e-06, 'epoch': 2.51}
{'loss': 0.1213, 'learning_rate': 8.218729535036019e-06, 'epoch': 2.67}
{'loss': 0.1252, 'learning_rate': 8.113948919449902e-06, 'epoch': 2.83}
{'loss': 0.1212, 'learning_rate': 8.009168303863785e-06, 'epoch': 2.99}
{'loss': 0.0741, 'learning_rate': 7.90438768827767e-06, 'epoch': 3.14}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0732, 'learning_rate': 7.799607072691552e-06, 'epoch': 3.3}
{'loss': 0.0723, 'learning_rate': 7.694826457105437e-06, 'epoch': 3.46}
{'loss': 0.0754, 'learning_rate': 7.590045841519319e-06, 'epoch': 3.61}
{'loss': 0.078, 'learning_rate': 7.485265225933203e-06, 'epoch': 3.77}
{'loss': 0.0752, 'learning_rate': 7.380484610347086e-06, 'epoch': 3.93}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0633, 'learning_rate': 7.27570399476097e-06, 'epoch': 4.09}
{'loss': 0.052, 'learning_rate': 7.1709233791748535e-06, 'epoch': 4.24}
{'loss': 0.0542, 'learning_rate': 7.066142763588737e-06, 'epoch': 4.4}
{'loss': 0.0566, 'learning_rate': 6.96136214800262e-06, 'epoch': 4.56}
{'loss': 0.0502, 'learning_rate': 6.856581532416503e-06, 'epoch': 4.72}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0539, 'learning_rate': 6.751800916830387e-06, 'epoch': 4.87}
{'loss': 0.0533, 'learning_rate': 6.64702030124427e-06, 'epoch': 5.03}
{'loss': 0.0379, 'learning_rate': 6.542239685658154e-06, 'epoch': 5.19}
{'loss': 0.0381, 'learning_rate': 6.437459070072037e-06, 'epoch': 5.34}
{'loss': 0.0403, 'learning_rate': 6.332678454485921e-06, 'epoch': 5.5}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0411, 'learning_rate': 6.227897838899805e-06, 'epoch': 5.66}
{'loss': 0.0432, 'learning_rate': 6.123117223313687e-06, 'epoch': 5.82}
{'loss': 0.0425, 'learning_rate': 6.018336607727571e-06, 'epoch': 5.97}
{'loss': 0.0327, 'learning_rate': 5.9135559921414545e-06, 'epoch': 6.13}
{'loss': 0.028, 'learning_rate': 5.8087753765553375e-06, 'epoch': 6.29}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0334, 'learning_rate': 5.703994760969221e-06, 'epoch': 6.44}
{'loss': 0.0316, 'learning_rate': 5.599214145383105e-06, 'epoch': 6.6}
{'loss': 0.0336, 'learning_rate': 5.494433529796987e-06, 'epoch': 6.76}
{'loss': 0.0338, 'learning_rate': 5.389652914210871e-06, 'epoch': 6.92}
{'loss': 0.0288, 'learning_rate': 5.284872298624755e-06, 'epoch': 7.07}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0245, 'learning_rate': 5.1800916830386384e-06, 'epoch': 7.23}
{'loss': 0.0255, 'learning_rate': 5.075311067452522e-06, 'epoch': 7.39}
{'loss': 0.0289, 'learning_rate': 4.970530451866405e-06, 'epoch': 7.54}
{'loss': 0.0238, 'learning_rate': 4.865749836280289e-06, 'epoch': 7.7}
{'loss': 0.025, 'learning_rate': 4.760969220694172e-06, 'epoch': 7.86}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0252, 'learning_rate': 4.656188605108055e-06, 'epoch': 8.02}
{'loss': 0.0176, 'learning_rate': 4.5514079895219386e-06, 'epoch': 8.17}
{'loss': 0.021, 'learning_rate': 4.446627373935822e-06, 'epoch': 8.33}
{'loss': 0.0203, 'learning_rate': 4.341846758349706e-06, 'epoch': 8.49}
{'loss': 0.0203, 'learning_rate': 4.237066142763589e-06, 'epoch': 8.64}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0235, 'learning_rate': 4.132285527177473e-06, 'epoch': 8.8}
{'loss': 0.0192, 'learning_rate': 4.027504911591356e-06, 'epoch': 8.96}
{'loss': 0.019, 'learning_rate': 3.9227242960052395e-06, 'epoch': 9.12}
{'loss': 0.0161, 'learning_rate': 3.8179436804191224e-06, 'epoch': 9.27}
{'loss': 0.0145, 'learning_rate': 3.7131630648330062e-06, 'epoch': 9.43}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0146, 'learning_rate': 3.6083824492468896e-06, 'epoch': 9.59}
{'loss': 0.0178, 'learning_rate': 3.503601833660773e-06, 'epoch': 9.74}
{'loss': 0.0175, 'learning_rate': 3.3988212180746567e-06, 'epoch': 9.9}
{'loss': 0.0169, 'learning_rate': 3.2940406024885396e-06, 'epoch': 10.06}
{'loss': 0.0129, 'learning_rate': 3.1892599869024234e-06, 'epoch': 10.22}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0131, 'learning_rate': 3.0844793713163068e-06, 'epoch': 10.37}
{'loss': 0.0146, 'learning_rate': 2.9796987557301905e-06, 'epoch': 10.53}
{'loss': 0.0124, 'learning_rate': 2.8749181401440735e-06, 'epoch': 10.69}
{'loss': 0.0127, 'learning_rate': 2.7701375245579572e-06, 'epoch': 10.84}
{'loss': 0.0129, 'learning_rate': 2.6653569089718406e-06, 'epoch': 11.0}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0103, 'learning_rate': 2.5605762933857235e-06, 'epoch': 11.16}
{'loss': 0.0105, 'learning_rate': 2.4557956777996073e-06, 'epoch': 11.32}
{'loss': 0.0111, 'learning_rate': 2.3510150622134907e-06, 'epoch': 11.47}
{'loss': 0.0105, 'learning_rate': 2.246234446627374e-06, 'epoch': 11.63}
{'loss': 0.0097, 'learning_rate': 2.1414538310412574e-06, 'epoch': 11.79}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0108, 'learning_rate': 2.036673215455141e-06, 'epoch': 11.94}
{'loss': 0.0081, 'learning_rate': 1.9318925998690245e-06, 'epoch': 12.1}
{'loss': 0.0078, 'learning_rate': 1.8271119842829076e-06, 'epoch': 12.26}
{'loss': 0.0076, 'learning_rate': 1.7223313686967912e-06, 'epoch': 12.42}
{'loss': 0.0086, 'learning_rate': 1.6175507531106745e-06, 'epoch': 12.57}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0077, 'learning_rate': 1.5127701375245581e-06, 'epoch': 12.73}
{'loss': 0.0076, 'learning_rate': 1.4079895219384415e-06, 'epoch': 12.89}
{'loss': 0.0094, 'learning_rate': 1.3032089063523248e-06, 'epoch': 13.05}
{'loss': 0.0074, 'learning_rate': 1.1984282907662084e-06, 'epoch': 13.2}
{'loss': 0.0076, 'learning_rate': 1.0936476751800917e-06, 'epoch': 13.36}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'loss': 0.0059, 'learning_rate': 9.88867059593975e-07, 'epoch': 13.52}
{'loss': 0.0073, 'learning_rate': 8.840864440078587e-07, 'epoch': 13.67}
{'loss': 0.0052, 'learning_rate': 7.793058284217421e-07, 'epoch': 13.83}
{'loss': 0.0069, 'learning_rate': 6.745252128356255e-07, 'epoch': 13.99}
{'loss': 0.0051, 'learning_rate': 5.697445972495088e-07, 'epoch': 14.15}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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100%|█████████▉| 38000/38175 [3:49:45<00:56,  3.10it/s]{'eval_loss': 3.974342107772827, 'eval_accuracy': 0.585625, 'eval_f1': 0.5845837997904817, 'eval_runtime': 6.0799, 'eval_samples_per_second': 526.322, 'eval_steps_per_second': 4.112, 'epoch': 14.15}
{'loss': 0.0065, 'learning_rate': 4.6496398166339233e-07, 'epoch': 14.3}
{'loss': 0.0052, 'learning_rate': 3.601833660772757e-07, 'epoch': 14.46}
{'loss': 0.0063, 'learning_rate': 2.5540275049115914e-07, 'epoch': 14.62}
{'loss': 0.0066, 'learning_rate': 1.5062213490504257e-07, 'epoch': 14.77}
{'loss': 0.0061, 'learning_rate': 4.5841519318926007e-08, 'epoch': 14.93}


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                                               /home/student_no_backup/sakumar/miniconda3/envs/cuda/lib/python3.12/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.
  warnings.warn('Was asked to gather along dimension 0, but all '

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{'eval_loss': 3.9612317085266113, 'eval_accuracy': 0.59, 'eval_f1': 0.5892940707088435, 'eval_runtime': 5.1075, 'eval_samples_per_second': 626.529, 'eval_steps_per_second': 4.895, 'epoch': 14.93}
{'train_runtime': 13862.4412, 'train_samples_per_second': 176.23, 'train_steps_per_second': 2.754, 'train_loss': 0.07044396612795646, 'epoch': 15.0}
➡️  Saving best ANLI model to ./deberta_v3_large_snli_mnli_anli …