---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: libCap_prBERTbfd_clf
  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. -->

# libCap_prBERTbfd_clf

This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5197
- Accuracy: 0.7457
- F1: 0.7940
- Precision: 0.7567
- Recall: 0.8352
- Auroc: 0.7268

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Auroc  |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| No log        | 0.98  | 34   | 0.6393          | 0.6396   | 0.7053 | 0.6782    | 0.7345 | 0.6197 |
| No log        | 1.98  | 68   | 0.5713          | 0.6962   | 0.7499 | 0.7256    | 0.7759 | 0.6795 |
| No log        | 2.98  | 102  | 0.5652          | 0.7126   | 0.7388 | 0.7918    | 0.6924 | 0.7168 |
| No log        | 3.98  | 136  | 0.5360          | 0.7330   | 0.7896 | 0.7345    | 0.8536 | 0.7076 |
| No log        | 4.98  | 170  | 0.5223          | 0.7423   | 0.7830 | 0.7740    | 0.7921 | 0.7318 |
| No log        | 5.98  | 204  | 0.5180          | 0.7454   | 0.7882 | 0.7699    | 0.8075 | 0.7323 |
| No log        | 6.98  | 238  | 0.5179          | 0.7440   | 0.7934 | 0.7537    | 0.8376 | 0.7243 |
| No log        | 7.98  | 272  | 0.5197          | 0.7457   | 0.7940 | 0.7567    | 0.8352 | 0.7268 |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1