readme: ---
  license: mit
  tags:
  - generated_from_trainer
  model-index:
  - name: BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT
    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. -->
  
  # BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT
  
  This model is a fine-tuned version of [Sotireas/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT](https://huggingface.co/Sotireas/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext-ContaminationQAmodel_PubmedBERT) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 3.0853
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss |
  |:-------------:|:-----:|:----:|:---------------:|
  | No log        | 1.0   | 21   | 3.8118          |
  | No log        | 2.0   | 42   | 3.5006          |
  | No log        | 3.0   | 63   | 3.1242          |
  | No log        | 4.0   | 84   | 2.9528          |
  | No log        | 5.0   | 105  | 2.9190          |
  | No log        | 6.0   | 126  | 2.9876          |
  | No log        | 7.0   | 147  | 3.0574          |
  | No log        | 8.0   | 168  | 3.0718          |
  | No log        | 9.0   | 189  | 3.0426          |
  | No log        | 10.0  | 210  | 3.0853          |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  