diff --git "a/esm_log.txt" "b/esm_log.txt"
--- "a/esm_log.txt"
+++ "b/esm_log.txt"
@@ -1,527 +1,190 @@
-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.
+
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 /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%|█▎        | 1825/13405 [01:44<10:54, 17.68it/s]
 14%|█▎        | 1827/13405 [01:44<10:54, 17.69it/s]
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 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]
 15%|█▌        | 2031/13405 [01:56<10:44, 17.64it/s]
 15%|█▌        | 2033/13405 [01:56<10:46, 17.60it/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]
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 16%|█▌        | 2085/13405 [01:59<10:38, 17.72it/s]
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 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]
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 20%|█▉        | 2679/13405 [02:33<10:05, 17.71it/s]{'loss': 0.4817, 'learning_rate': 0.0009627004848936964, 'epoch': 0.19}
-{'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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  4%|▎         | 2000/53620 [01:55<48:34, 17.71it/s]
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  4%|▎         | 2005/53620 [01:56<1:10:00, 12.29it/s]
  4%|▎         | 2007/53620 [01:56<1:03:34, 13.53it/s]
  4%|▎         | 2009/53620 [01:56<59:04, 14.56it/s]  
  4%|▍         | 2011/53620 [01:56<55:54, 15.38it/s]
  4%|▍         | 2013/53620 [01:56<53:42, 16.01it/s]
  4%|▍         | 2015/53620 [01:56<52:09, 16.49it/s]
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  5%|▍         | 2679/53620 [02:34<47:58, 17.69it/s]{'loss': 0.4441, 'learning_rate': 0.000990675121223424, 'epoch': 0.19}
+{'loss': 0.4405, 'learning_rate': 0.0009813502424468482, 'epoch': 0.37}
+{'loss': 0.4162, 'learning_rate': 0.0009720253636702722, 'epoch': 0.56}
+{'loss': 0.4395, 'learning_rate': 0.0009627004848936964, 'epoch': 0.75}
+{'loss': 0.4358, 'learning_rate': 0.0009533756061171205, 'epoch': 0.93}
 
 
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+
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+
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+
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+
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+
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+
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+
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+
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+
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+
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  4%|▍         | 28/671 [00:01<00:35, 18.10it/s]
 
  4%|▍         | 30/671 [00:01<00:35, 18.09it/s]
-
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-
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+
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+
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+
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+
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+
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+
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+
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  9%|▊         | 58/671 [00:03<00:33, 18.06it/s]
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+
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+
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+
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  5%|▌         | 2681/53620 [02:45<47:58, 17.69it/s]
 
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 40%|███▉      | 5360/13405 [05:42<07:33, 17.75it/s]{'eval_loss': 0.4510901868343353, 'eval_runtime': 37.1183, 'eval_samples_per_second': 144.457, 'eval_steps_per_second': 18.077, 'epoch': 1.0}
-{'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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@@ -531,1192 +194,6686 @@ You should probably TRAIN this model on a down-stream task to be able to use it
 
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+{'loss': 0.4353, 'learning_rate': 0.0009067512122342409, 'epoch': 1.86}
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-{'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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 15%|█▍        | 8041/53620 [08:54<42:47, 17.75it/s]{'eval_loss': 0.41897135972976685, 'eval_runtime': 36.9951, 'eval_samples_per_second': 144.938, 'eval_steps_per_second': 18.138, 'epoch': 2.0}
+{'loss': 0.4174, 'learning_rate': 0.0008974263334576651, 'epoch': 2.05}
+{'loss': 0.4357, 'learning_rate': 0.0008881014546810892, 'epoch': 2.24}
+{'loss': 0.4269, 'learning_rate': 0.0008787765759045132, 'epoch': 2.42}
+{'loss': 0.4179, 'learning_rate': 0.0008694516971279373, 'epoch': 2.61}
+{'loss': 0.4195, 'learning_rate': 0.0008601268183513614, 'epoch': 2.8}
+{'loss': 0.412, 'learning_rate': 0.0008508019395747856, 'epoch': 2.98}
 
 
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 20%|█▉        | 10722/53620 [12:04<40:15, 17.76it/s]{'eval_loss': 0.4080427587032318, 'eval_runtime': 36.9682, 'eval_samples_per_second': 145.043, 'eval_steps_per_second': 18.151, 'epoch': 3.0}
+{'loss': 0.4339, 'learning_rate': 0.0008414770607982097, 'epoch': 3.17}
+{'loss': 0.4187, 'learning_rate': 0.0008321521820216338, 'epoch': 3.36}
+{'loss': 0.4271, 'learning_rate': 0.0008228273032450578, 'epoch': 3.54}
+{'loss': 0.4192, 'learning_rate': 0.0008135024244684819, 'epoch': 3.73}
+{'loss': 0.4131, 'learning_rate': 0.000804177545691906, 'epoch': 3.92}
+
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 25%|██▍       | 13403/53620 [15:13<37:47, 17.74it/s]{'eval_loss': 0.42126333713531494, 'eval_runtime': 36.9861, 'eval_samples_per_second': 144.973, 'eval_steps_per_second': 18.142, 'epoch': 4.0}
+{'loss': 0.4179, 'learning_rate': 0.0007948526669153301, 'epoch': 4.1}
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+{'loss': 0.4124, 'learning_rate': 0.0007575531518090265, 'epoch': 4.85}
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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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 30%|██▉       | 16084/53620 [18:23<35:13, 17.76it/s]{'eval_loss': 0.522628128528595, 'eval_runtime': 36.9896, 'eval_samples_per_second': 144.96, 'eval_steps_per_second': 18.14, 'epoch': 5.0}
+{'loss': 0.4211, 'learning_rate': 0.0007482282730324506, 'epoch': 5.04}
+{'loss': 0.395, 'learning_rate': 0.0007389033942558748, 'epoch': 5.22}
+{'loss': 0.3985, 'learning_rate': 0.0007295785154792987, 'epoch': 5.41}
+{'loss': 0.422, 'learning_rate': 0.0007202536367027229, 'epoch': 5.59}
+{'loss': 0.4165, 'learning_rate': 0.000710928757926147, 'epoch': 5.78}
+{'loss': 0.4216, 'learning_rate': 0.000701603879149571, 'epoch': 5.97}
 
 
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+{'loss': 0.3941, 'learning_rate': 0.0005524058187243566, 'epoch': 8.95}
+
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 65%|██████▍   | 34851/53620 [40:32<17:37, 17.75it/s]{'eval_loss': 0.3820453882217407, 'eval_runtime': 36.9797, 'eval_samples_per_second': 144.998, 'eval_steps_per_second': 18.145, 'epoch': 12.0}
+{'loss': 0.3892, 'learning_rate': 0.00039388287952256624, 'epoch': 12.12}
+{'loss': 0.3909, 'learning_rate': 0.0003845580007459903, 'epoch': 12.31}
+{'loss': 0.423, 'learning_rate': 0.0003752331219694144, 'epoch': 12.5}
+{'loss': 0.3883, 'learning_rate': 0.0003659082431928385, 'epoch': 12.68}
+{'loss': 0.369, 'learning_rate': 0.0003565833644162626, 'epoch': 12.87}
+
+
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+
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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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+{'loss': 0.3723, 'learning_rate': 0.00014211115255501676, 'epoch': 17.16}
+{'loss': 0.3804, 'learning_rate': 0.0001327862737784409, 'epoch': 17.34}
+{'loss': 0.3677, 'learning_rate': 0.000123461395001865, 'epoch': 17.53}
+{'loss': 0.3742, 'learning_rate': 0.00011413651622528907, 'epoch': 17.72}
+{'loss': 0.3673, 'learning_rate': 0.00010481163744871317, 'epoch': 17.9}
 
 
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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}
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100%|█████████▉| 53618/53620 [1:02:41<00:00, 17.76it/s]{'eval_loss': 0.37231209874153137, 'eval_runtime': 36.9858, 'eval_samples_per_second': 144.975, 'eval_steps_per_second': 18.142, 'epoch': 19.0}
+{'loss': 0.3549, 'learning_rate': 4.886236478925774e-05, 'epoch': 19.02}
+{'loss': 0.379, 'learning_rate': 3.953748601268184e-05, 'epoch': 19.21}
+{'loss': 0.3653, 'learning_rate': 3.021260723610593e-05, 'epoch': 19.4}
+{'loss': 0.3586, 'learning_rate': 2.0887728459530027e-05, 'epoch': 19.58}
+{'loss': 0.3639, 'learning_rate': 1.1562849682954122e-05, 'epoch': 19.77}
+{'loss': 0.3742, 'learning_rate': 2.237970906378217e-06, 'epoch': 19.96}
+
+
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+{'eval_loss': 0.37200844287872314, 'eval_runtime': 36.9871, 'eval_samples_per_second': 144.969, 'eval_steps_per_second': 18.141, 'epoch': 20.0}
+{'train_runtime': 3798.2262, 'train_samples_per_second': 112.926, 'train_steps_per_second': 14.117, 'train_loss': 0.39866488114171666, 'epoch': 20.0}