readme: ---
  tags:
  - generated_from_trainer
  model-index:
  - name: protBERTbfd_AAV2_regressor
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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  # protBERTbfd_AAV2_regressor
  
  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: 0.0327
  - Mse: 0.0327
  - Rmse: 0.1808
  - Mae: 0.0618
  - R2: 0.8691
  - Smape: 101.2324
  
  ## 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
  - lr_scheduler_warmup_steps: 200
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Mse    | Rmse   | Mae    | R2     | Smape    |
  |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 58   | 0.0985          | 0.0985 | 0.3138 | 0.1707 | 0.6057 | 102.5806 |
  | No log        | 2.0   | 116  | 0.0689          | 0.0689 | 0.2625 | 0.1432 | 0.7242 | 112.9846 |
  | No log        | 3.0   | 174  | 0.0400          | 0.0400 | 0.1999 | 0.0859 | 0.8399 | 102.6132 |
  | No log        | 4.0   | 232  | 0.0402          | 0.0402 | 0.2005 | 0.0745 | 0.8389 | 103.3228 |
  | No log        | 5.0   | 290  | 0.0337          | 0.0337 | 0.1836 | 0.0665 | 0.8650 | 101.0925 |
  | No log        | 6.0   | 348  | 0.0327          | 0.0327 | 0.1808 | 0.0618 | 0.8691 | 101.2324 |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  