readme: ---
  license: apache-2.0
  tags:
  - protein language model
  - generated_from_trainer
  datasets:
  - train
  metrics:
  - spearmanr
  model-index:
  - name: tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: cradle-bio/tape-fluorescence
        type: train
      metrics:
      - name: Spearmanr
        type: spearmanr
        value: 0.5505486770316164
  ---
  
  <!-- 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. -->
  
  # tape-fluorescence-prediction-tape-fluorescence-evotuning-DistilProtBert
  
  This model is a fine-tuned version of [thundaa/tape-fluorescence-evotuning-DistilProtBert](https://huggingface.co/thundaa/tape-fluorescence-evotuning-DistilProtBert) on the cradle-bio/tape-fluorescence dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3377
  - Spearmanr: 0.5505
  
  ## 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: 40
  - eval_batch_size: 40
  - seed: 42
  - gradient_accumulation_steps: 64
  - total_train_batch_size: 2560
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 30
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Spearmanr |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|
  | 6.2764        | 0.93  | 7    | 1.9927          | -0.0786   |
  | 1.1206        | 1.93  | 14   | 0.8223          | -0.1543   |
  | 0.8054        | 2.93  | 21   | 0.6894          | 0.2050    |
  | 0.7692        | 3.93  | 28   | 0.8084          | 0.2807    |
  | 0.7597        | 4.93  | 35   | 0.6613          | 0.4003    |
  | 0.7416        | 5.93  | 42   | 0.6803          | 0.3829    |
  | 0.7256        | 6.93  | 49   | 0.6428          | 0.4416    |
  | 0.6966        | 7.93  | 56   | 0.6086          | 0.4506    |
  | 0.7603        | 8.93  | 63   | 0.9119          | 0.4697    |
  | 0.9187        | 9.93  | 70   | 0.6048          | 0.4757    |
  | 1.0371        | 10.93 | 77   | 2.0742          | 0.4076    |
  | 1.0947        | 11.93 | 84   | 0.6633          | 0.4522    |
  | 0.6946        | 12.93 | 91   | 0.6008          | 0.4123    |
  | 0.6618        | 13.93 | 98   | 0.5931          | 0.4457    |
  | 0.8635        | 14.93 | 105  | 1.9561          | 0.4331    |
  | 0.9444        | 15.93 | 112  | 0.5627          | 0.5041    |
  | 0.5535        | 16.93 | 119  | 0.4348          | 0.4840    |
  | 0.9059        | 17.93 | 126  | 0.6704          | 0.5123    |
  | 0.5693        | 18.93 | 133  | 0.4616          | 0.5285    |
  | 0.6298        | 19.93 | 140  | 0.6915          | 0.5166    |
  | 0.955         | 20.93 | 147  | 0.6679          | 0.5677    |
  | 0.7866        | 21.93 | 154  | 0.8136          | 0.5559    |
  | 0.6687        | 22.93 | 161  | 0.4782          | 0.5561    |
  | 0.5336        | 23.93 | 168  | 0.4447          | 0.5499    |
  | 0.4673        | 24.93 | 175  | 0.4258          | 0.5428    |
  | 0.478         | 25.93 | 182  | 0.3651          | 0.5329    |
  | 0.4023        | 26.93 | 189  | 0.3688          | 0.5428    |
  | 0.3961        | 27.93 | 196  | 0.3692          | 0.5509    |
  | 0.3808        | 28.93 | 203  | 0.3434          | 0.5514    |
  | 0.3433        | 29.93 | 210  | 0.3377          | 0.5505    |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  