readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: biolinkbert-base-medqa-usmle-nocontext
    results: []
  datasets:
  - GBaker/MedQA-USMLE-4-options-hf
  ---
  
  <!-- 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. -->
  
  # biolinkbert-base-medqa-usmle-nocontext
  
  This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.5149
  - Accuracy: 0.3943
  
  ## 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: 3e-05
  - train_batch_size: 4
  - eval_batch_size: 4
  - seed: 42
  - gradient_accumulation_steps: 64
  - total_train_batch_size: 256
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 0.98  | 39   | 1.3339          | 0.3590   |
  | No log        | 1.98  | 78   | 1.3685          | 0.3794   |
  | No log        | 2.98  | 117  | 1.4162          | 0.3912   |
  | No log        | 3.98  | 156  | 1.4484          | 0.3888   |
  | No log        | 4.98  | 195  | 1.4869          | 0.3983   |
  | No log        | 5.98  | 234  | 1.5149          | 0.3943   |
  
  
  ### Framework versions
  
  - Transformers 4.26.0
  - Pytorch 1.13.1+cu116
  - Datasets 2.9.0
  - Tokenizers 0.13.2