---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: prot_bert_bfd-disoRNA
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# prot_bert_bfd-disoRNA

This model is a fine-tuned version of [Rostlab/prot_bert_bfd](https://huggingface.co/Rostlab/prot_bert_bfd) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0634
- Precision: 0.9746
- Recall: 0.9872
- F1: 0.9809

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.0627        | 1.0   | 61   | 0.0665          | 0.9746    | 0.9872 | 0.9809 |
| 0.0186        | 2.0   | 122  | 0.0644          | 0.9746    | 0.9872 | 0.9809 |
| 0.015         | 3.0   | 183  | 0.0634          | 0.9746    | 0.9872 | 0.9809 |


### Framework versions

- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1