readme: ---
  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
  should probably proofread and complete it, then remove this comment. -->
  
  # 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
  