Some weights of EsmForSequenceClassification were not initialized from the model checkpoint at facebook/esm2_t6_8M_UR50D and are newly initialized: ['classifier.out_proj.weight', 'classifier.dense.bias', 'classifier.out_proj.bias', 'classifier.dense.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
/home/rjo21/miniconda3/envs/finesm/lib/python3.9/site-packages/transformers/optimization.py:411: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning
  warnings.warn(

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 14%|█▎        | 1821/13405 [01:44<10:55, 17.67it/s]
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 14%|█▎        | 1825/13405 [01:44<10:54, 17.68it/s]
 14%|█▎        | 1827/13405 [01:44<10:54, 17.69it/s]
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 14%|█▍        | 1851/13405 [01:45<10:52, 17.71it/s]
 14%|█▍        | 1853/13405 [01:45<10:52, 17.70it/s]
 14%|█▍        | 1855/13405 [01:45<10:52, 17.70it/s]
 14%|█▍        | 1857/13405 [01:46<10:51, 17.71it/s]
 14%|█▍        | 1859/13405 [01:46<10:51, 17.71it/s]
 14%|█▍        | 1861/13405 [01:46<10:51, 17.71it/s]
 14%|█▍        | 1863/13405 [01:46<10:51, 17.71it/s]
 14%|█▍        | 1865/13405 [01:46<10:51, 17.71it/s]
 14%|█▍        | 1867/13405 [01:46<10:51, 17.72it/s]
 14%|█▍        | 1869/13405 [01:46<10:51, 17.72it/s]
 14%|█▍        | 1871/13405 [01:46<10:51, 17.71it/s]
 14%|█▍        | 1873/13405 [01:46<10:51, 17.71it/s]
 14%|█▍        | 1875/13405 [01:47<10:51, 17.69it/s]
 14%|█▍        | 1877/13405 [01:47<10:51, 17.70it/s]
 14%|█▍        | 1879/13405 [01:47<10:50, 17.71it/s]
 14%|█▍        | 1881/13405 [01:47<10:50, 17.70it/s]
 14%|█▍        | 1883/13405 [01:47<10:50, 17.70it/s]
 14%|█▍        | 1885/13405 [01:47<10:50, 17.71it/s]
 14%|█▍        | 1887/13405 [01:47<10:51, 17.69it/s]
 14%|█▍        | 1889/13405 [01:47<10:50, 17.69it/s]
 14%|█▍        | 1891/13405 [01:47<10:50, 17.70it/s]
 14%|█▍        | 1893/13405 [01:48<10:50, 17.71it/s]
 14%|█▍        | 1895/13405 [01:48<10:50, 17.70it/s]
 14%|█▍        | 1897/13405 [01:48<10:49, 17.71it/s]
 14%|█▍        | 1899/13405 [01:48<10:49, 17.71it/s]
 14%|█▍        | 1901/13405 [01:48<10:49, 17.71it/s]
 14%|█▍        | 1903/13405 [01:48<10:49, 17.71it/s]
 14%|█▍        | 1905/13405 [01:48<10:49, 17.71it/s]
 14%|█▍        | 1907/13405 [01:48<10:49, 17.71it/s]
 14%|█▍        | 1909/13405 [01:48<10:49, 17.71it/s]
 14%|█▍        | 1911/13405 [01:49<10:48, 17.71it/s]
 14%|█▍        | 1913/13405 [01:49<10:48, 17.71it/s]
 14%|█▍        | 1915/13405 [01:49<10:48, 17.71it/s]
 14%|█▍        | 1917/13405 [01:49<10:48, 17.70it/s]
 14%|█▍        | 1919/13405 [01:49<10:48, 17.71it/s]
 14%|█▍        | 1921/13405 [01:49<10:48, 17.70it/s]
 14%|█▍        | 1923/13405 [01:49<10:48, 17.70it/s]
 14%|█▍        | 1925/13405 [01:49<10:48, 17.70it/s]
 14%|█▍        | 1927/13405 [01:50<10:48, 17.69it/s]
 14%|█▍        | 1929/13405 [01:50<10:48, 17.69it/s]
 14%|█▍        | 1931/13405 [01:50<10:48, 17.70it/s]
 14%|█▍        | 1933/13405 [01:50<10:47, 17.70it/s]
 14%|█▍        | 1935/13405 [01:50<10:47, 17.70it/s]
 14%|█▍        | 1937/13405 [01:50<10:47, 17.71it/s]
 14%|█▍        | 1939/13405 [01:50<10:47, 17.71it/s]
 14%|█▍        | 1941/13405 [01:50<10:47, 17.71it/s]
 14%|█▍        | 1943/13405 [01:50<10:47, 17.71it/s]
 15%|█▍        | 1945/13405 [01:51<10:47, 17.70it/s]
 15%|█▍        | 1947/13405 [01:51<10:47, 17.70it/s]
 15%|█▍        | 1949/13405 [01:51<10:47, 17.71it/s]
 15%|█▍        | 1951/13405 [01:51<10:46, 17.71it/s]
 15%|█▍        | 1953/13405 [01:51<10:46, 17.71it/s]
 15%|█▍        | 1955/13405 [01:51<10:46, 17.71it/s]
 15%|█▍        | 1957/13405 [01:51<10:46, 17.71it/s]
 15%|█▍        | 1959/13405 [01:51<10:46, 17.72it/s]
 15%|█▍        | 1961/13405 [01:51<10:46, 17.71it/s]
 15%|█▍        | 1963/13405 [01:52<10:46, 17.70it/s]
 15%|█▍        | 1965/13405 [01:52<10:45, 17.71it/s]
 15%|█▍        | 1967/13405 [01:52<10:45, 17.71it/s]
 15%|█▍        | 1969/13405 [01:52<10:46, 17.70it/s]
 15%|█▍        | 1971/13405 [01:52<10:45, 17.70it/s]
 15%|█▍        | 1973/13405 [01:52<10:45, 17.70it/s]
 15%|█▍        | 1975/13405 [01:52<10:45, 17.70it/s]
 15%|█▍        | 1977/13405 [01:52<10:45, 17.71it/s]
 15%|█▍        | 1979/13405 [01:52<10:45, 17.71it/s]
 15%|█▍        | 1981/13405 [01:53<10:45, 17.70it/s]
 15%|█▍        | 1983/13405 [01:53<10:45, 17.70it/s]
 15%|█▍        | 1985/13405 [01:53<10:45, 17.70it/s]
 15%|█▍        | 1987/13405 [01:53<10:44, 17.70it/s]
 15%|█▍        | 1989/13405 [01:53<10:44, 17.71it/s]
 15%|█▍        | 1991/13405 [01:53<10:44, 17.71it/s]
 15%|█▍        | 1993/13405 [01:53<10:44, 17.72it/s]
 15%|█▍        | 1995/13405 [01:53<10:44, 17.71it/s]
 15%|█▍        | 1997/13405 [01:53<10:44, 17.71it/s]
 15%|█▍        | 1999/13405 [01:54<10:43, 17.71it/s]
                                                    

 15%|█▍        | 2000/13405 [01:54<10:43, 17.71it/s]
 15%|█▍        | 2001/13405 [01:54<20:16,  9.37it/s]
 15%|█▍        | 2003/13405 [01:54<17:24, 10.91it/s]
 15%|█▍        | 2005/13405 [01:54<15:24, 12.33it/s]
 15%|█▍        | 2007/13405 [01:54<14:00, 13.56it/s]
 15%|█▍        | 2009/13405 [01:54<13:01, 14.59it/s]
 15%|█▌        | 2011/13405 [01:55<12:19, 15.40it/s]
 15%|█▌        | 2013/13405 [01:55<11:50, 16.03it/s]
 15%|█▌        | 2015/13405 [01:55<11:30, 16.50it/s]
 15%|█▌        | 2017/13405 [01:55<11:16, 16.85it/s]
 15%|█▌        | 2019/13405 [01:55<11:06, 17.09it/s]
 15%|█▌        | 2021/13405 [01:55<10:58, 17.28it/s]
 15%|█▌        | 2023/13405 [01:55<10:53, 17.41it/s]
 15%|█▌        | 2025/13405 [01:55<10:50, 17.49it/s]
 15%|█▌        | 2027/13405 [01:55<10:47, 17.56it/s]
 15%|█▌        | 2029/13405 [01:56<10:46, 17.61it/s]
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 16%|█▌        | 2079/13405 [01:58<10:39, 17.71it/s]
 16%|█▌        | 2081/13405 [01:59<10:39, 17.71it/s]
 16%|█▌        | 2083/13405 [01:59<10:39, 17.72it/s]
 16%|█▌        | 2085/13405 [01:59<10:38, 17.72it/s]
 16%|█▌        | 2087/13405 [01:59<10:38, 17.72it/s]
 16%|█▌        | 2089/13405 [01:59<10:38, 17.72it/s]
 16%|█▌        | 2091/13405 [01:59<10:38, 17.71it/s]
 16%|█▌        | 2093/13405 [01:59<10:38, 17.71it/s]
 16%|█▌        | 2095/13405 [01:59<10:38, 17.72it/s]
 16%|█▌        | 2097/13405 [01:59<10:38, 17.71it/s]
 16%|█▌        | 2099/13405 [02:00<10:38, 17.70it/s]
 16%|█▌        | 2101/13405 [02:00<10:38, 17.71it/s]
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 16%|█▌        | 2107/13405 [02:00<10:37, 17.71it/s]
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{'loss': 0.4858, 'learning_rate': 0.0009254009697873927, 'epoch': 0.37}
{'loss': 0.4611, 'learning_rate': 0.0008881014546810892, 'epoch': 0.56}
{'loss': 0.4746, 'learning_rate': 0.0008508019395747856, 'epoch': 0.75}
{'loss': 0.463, 'learning_rate': 0.0008135024244684819, 'epoch': 0.93}


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{'loss': 0.4575, 'learning_rate': 0.0007762029093621783, 'epoch': 1.12}
{'loss': 0.4647, 'learning_rate': 0.0007389033942558748, 'epoch': 1.31}
{'loss': 0.465, 'learning_rate': 0.000701603879149571, 'epoch': 1.49}
{'loss': 0.4431, 'learning_rate': 0.0006643043640432674, 'epoch': 1.68}
{'loss': 0.4458, 'learning_rate': 0.0006270048489369638, 'epoch': 1.86}


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 60%|█████▉    | 8041/13405 [08:53<05:02, 17.75it/s]{'eval_loss': 0.45141956210136414, 'eval_runtime': 37.0512, 'eval_samples_per_second': 144.719, 'eval_steps_per_second': 18.11, 'epoch': 2.0}
{'loss': 0.4258, 'learning_rate': 0.0005897053338306602, 'epoch': 2.05}
{'loss': 0.4508, 'learning_rate': 0.0005524058187243566, 'epoch': 2.24}
{'loss': 0.4334, 'learning_rate': 0.000515106303618053, 'epoch': 2.42}
{'loss': 0.428, 'learning_rate': 0.0004778067885117494, 'epoch': 2.61}
{'loss': 0.4264, 'learning_rate': 0.0004405072734054457, 'epoch': 2.8}
{'loss': 0.4153, 'learning_rate': 0.0004032077582991421, 'epoch': 2.98}


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{'loss': 0.436, 'learning_rate': 0.0003659082431928385, 'epoch': 3.17}
{'loss': 0.4182, 'learning_rate': 0.00032860872808653484, 'epoch': 3.36}
{'loss': 0.4337, 'learning_rate': 0.0002913092129802313, 'epoch': 3.54}
{'loss': 0.4216, 'learning_rate': 0.0002540096978739276, 'epoch': 3.73}
{'loss': 0.4141, 'learning_rate': 0.00021671018276762404, 'epoch': 3.92}


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100%|█████████▉| 13403/13405 [15:12<00:00, 17.75it/s]{'eval_loss': 0.42015573382377625, 'eval_runtime': 37.0509, 'eval_samples_per_second': 144.72, 'eval_steps_per_second': 18.11, 'epoch': 4.0}
{'loss': 0.4175, 'learning_rate': 0.00017941066766132043, 'epoch': 4.1}
{'loss': 0.4318, 'learning_rate': 0.00014211115255501676, 'epoch': 4.29}
{'loss': 0.4045, 'learning_rate': 0.00010481163744871317, 'epoch': 4.48}
{'loss': 0.4222, 'learning_rate': 6.751212234240956e-05, 'epoch': 4.66}
{'loss': 0.4103, 'learning_rate': 3.021260723610593e-05, 'epoch': 4.85}


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{'eval_loss': 0.4142831861972809, 'eval_runtime': 37.0588, 'eval_samples_per_second': 144.689, 'eval_steps_per_second': 18.106, 'epoch': 5.0}
{'train_runtime': 949.8054, 'train_samples_per_second': 112.897, 'train_steps_per_second': 14.113, 'train_loss': 0.43869128627414267, 'epoch': 5.0}