LABEL_0 LABEL_1 LABEL_10 LABEL_11 LABEL_12 LABEL_13 LABEL_14 LABEL_15 LABEL_16 LABEL_17 LABEL_18 LABEL_19 LABEL_2 LABEL_3 LABEL_4 LABEL_5 LABEL_6 LABEL_7 LABEL_8 LABEL_9
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ProtBert-finetuned-proteinBindingDB
  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. -->

# ProtBert-finetuned-proteinBindingDB

This model is a fine-tuned version of [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5764
- Accuracy: 0.885
- F1: 0.8459
- Precision: 0.8255
- Recall: 0.885

## 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-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8056        | 1.0   | 5000  | 1.5153          | 0.745    | 0.6391 | 0.5606    | 0.745  |
| 0.7873        | 2.0   | 10000 | 0.5976          | 0.865    | 0.8267 | 0.8063    | 0.865  |
| 0.7427        | 3.0   | 15000 | 0.6316          | 0.875    | 0.8364 | 0.8176    | 0.875  |
| 1.0022        | 4.0   | 20000 | 0.6766          | 0.85     | 0.8112 | 0.7951    | 0.85   |
| 0.7379        | 5.0   | 25000 | 0.6181          | 0.865    | 0.8267 | 0.8063    | 0.865  |
| 0.6987        | 6.0   | 30000 | 0.7094          | 0.87     | 0.8336 | 0.82      | 0.87   |
| 0.6984        | 7.0   | 35000 | 0.5377          | 0.885    | 0.8471 | 0.8290    | 0.885  |
| 0.6657        | 8.0   | 40000 | 0.6278          | 0.875    | 0.8373 | 0.8213    | 0.875  |
| 0.6695        | 9.0   | 45000 | 0.6323          | 0.88     | 0.8421 | 0.8240    | 0.88   |
| 0.6352        | 10.0  | 50000 | 0.5764          | 0.885    | 0.8459 | 0.8255    | 0.885  |


### Framework versions

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