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---
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- f1
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- precision
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- recall
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model-index:
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- name: prot_bert_classification_finetuned_no_finetune
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# prot_bert_classification_finetuned_no_finetune
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This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6212
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- Accuracy: 0.6473
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- F1: 0.6623
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- Precision: 0.6201
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- Recall: 0.7107
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-06
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 3
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 4
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 5
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- num_epochs: 6
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
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| 0.6494 | 1.0 | 3332 | 0.6479 | 0.6439 | 0.6679 | 0.6116 | 0.7357 |
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| 0.5357 | 2.0 | 6664 | 0.6440 | 0.6148 | 0.6459 | 0.5845 | 0.7218 |
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| 0.4661 | 3.0 | 9996 | 0.6265 | 0.6283 | 0.6414 | 0.6047 | 0.6829 |
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| 0.506 | 4.0 | 13328 | 0.6192 | 0.6439 | 0.6567 | 0.6187 | 0.6996 |
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| 0.4204 | 5.0 | 16660 | 0.6122 | 0.6567 | 0.6752 | 0.6259 | 0.7330 |
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| 0.6071 | 6.0 | 19992 | 0.6212 | 0.6473 | 0.6623 | 0.6201 | 0.7107 |
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### Framework versions
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- Transformers 4.18.0
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- Pytorch 1.11.0+cu113
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- Datasets 2.1.0
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- Tokenizers 0.12.1
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