---
base_model: Rostlab/prot_bert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: prot_bert-fine-tuned-toxicity_3
  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-fine-tuned-toxicity_3

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: 1.0450
- Accuracy: 0.7419
- Precision: 0.7530
- Recall: 0.7419
- F1: 0.7340

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.5735        | 1.0   | 16   | 0.5968          | 0.8065   | 0.8093    | 0.8065 | 0.8044 |
| 0.4855        | 2.0   | 32   | 0.4939          | 0.8387   | 0.8391    | 0.8387 | 0.8380 |
| 0.3499        | 3.0   | 48   | 0.5234          | 0.8065   | 0.8093    | 0.8065 | 0.8044 |
| 0.3094        | 4.0   | 64   | 0.4639          | 0.8387   | 0.8391    | 0.8387 | 0.8380 |
| 0.235         | 5.0   | 80   | 0.5654          | 0.8387   | 0.8391    | 0.8387 | 0.8380 |
| 0.1608        | 6.0   | 96   | 0.6409          | 0.8387   | 0.8391    | 0.8387 | 0.8380 |
| 0.1147        | 7.0   | 112  | 0.6271          | 0.8387   | 0.8391    | 0.8387 | 0.8380 |
| 0.171         | 8.0   | 128  | 0.7704          | 0.8065   | 0.8093    | 0.8065 | 0.8044 |
| 0.1762        | 9.0   | 144  | 0.7978          | 0.8065   | 0.8093    | 0.8065 | 0.8044 |
| 0.1438        | 10.0  | 160  | 0.9561          | 0.7419   | 0.7530    | 0.7419 | 0.7340 |
| 0.0953        | 11.0  | 176  | 1.0074          | 0.7419   | 0.7530    | 0.7419 | 0.7340 |
| 0.0394        | 12.0  | 192  | 1.0037          | 0.7419   | 0.7530    | 0.7419 | 0.7340 |
| 0.0896        | 13.0  | 208  | 1.0128          | 0.7419   | 0.7530    | 0.7419 | 0.7340 |
| 0.0849        | 14.0  | 224  | 1.0404          | 0.7419   | 0.7530    | 0.7419 | 0.7340 |
| 0.0207        | 15.0  | 240  | 1.0450          | 0.7419   | 0.7530    | 0.7419 | 0.7340 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1