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Update README.md

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@@ -108,7 +108,7 @@ if __name__ == "__main__":
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  'learning_rate': 5e-05, 'weight_decay': 0, 'num_train_epochs': 100, 'max_steps': -1, 'lr_scheduler_type': 'constant', 'do_eval': True, \
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  'evaluation_strategy': 'epoch', 'per_device_eval_batch_size': 32, 'logging_strategy': 'epoch', 'save_strategy': 'epoch', 'save_steps': 820, \
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  'dataloader_num_workers': 0, 'run_name': 'downstream_esm1b_localization_fix', 'optim': 'adamw_torch', 'resume_from_checkpoint': False, \
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- 'label_names': ['labels'], 'load_best_model_at_end': True, 'metric_for_best_model': 'accuracy', 'bf16'}
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  training_args = HfArgumentParser(TrainingArguments).parse_dict(training_args, allow_extra_keys=False)[0]
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  def tokenize_protein(example, tokenizer=None):
 
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  'learning_rate': 5e-05, 'weight_decay': 0, 'num_train_epochs': 100, 'max_steps': -1, 'lr_scheduler_type': 'constant', 'do_eval': True, \
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  'evaluation_strategy': 'epoch', 'per_device_eval_batch_size': 32, 'logging_strategy': 'epoch', 'save_strategy': 'epoch', 'save_steps': 820, \
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  'dataloader_num_workers': 0, 'run_name': 'downstream_esm1b_localization_fix', 'optim': 'adamw_torch', 'resume_from_checkpoint': False, \
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+ 'label_names': ['labels'], 'load_best_model_at_end': True, 'metric_for_best_model': 'accuracy', 'bf16': True}
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  training_args = HfArgumentParser(TrainingArguments).parse_dict(training_args, allow_extra_keys=False)[0]
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  def tokenize_protein(example, tokenizer=None):