---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: prot_bert_classification_finetuned_no_finetune
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# prot_bert_classification_finetuned_no_finetune

This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6212
- Accuracy: 0.6473
- F1: 0.6623
- Precision: 0.6201
- Recall: 0.7107

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 3
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.6494        | 1.0   | 3332  | 0.6479          | 0.6439   | 0.6679 | 0.6116    | 0.7357 |
| 0.5357        | 2.0   | 6664  | 0.6440          | 0.6148   | 0.6459 | 0.5845    | 0.7218 |
| 0.4661        | 3.0   | 9996  | 0.6265          | 0.6283   | 0.6414 | 0.6047    | 0.6829 |
| 0.506         | 4.0   | 13328 | 0.6192          | 0.6439   | 0.6567 | 0.6187    | 0.6996 |
| 0.4204        | 5.0   | 16660 | 0.6122          | 0.6567   | 0.6752 | 0.6259    | 0.7330 |
| 0.6071        | 6.0   | 19992 | 0.6212          | 0.6473   | 0.6623 | 0.6201    | 0.7107 |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
