readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  model-index:
  - name: biolinkbert-mednli
    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. -->
  
  # biolinkbert-mednli
  
  This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-large](https://huggingface.co/michiyasunaga/BioLinkBERT-large) on [MedNLI](https://physionet.org/content/mednli/1.0.0/).
  It achieves the following results on the evaluation set:
  ```
  {
      "eval_accuracy": 0.8788530230522156,
      "eval_loss": 0.7843484878540039,
      "eval_runtime": 39.7009,
      "eval_samples": 1395,
      "eval_samples_per_second": 35.138,
      "eval_steps_per_second": 1.108
  }
  ```
  
  The accuracy for the test set is
  ```
  {
      "eval_accuracy": 0.8607594966888428,
      "eval_loss": 0.879707932472229,
      "eval_runtime": 27.4404,
      "eval_samples": 1395,
      "eval_samples_per_second": 51.821,
      "eval_steps_per_second": 1.64
  }
  ```
  The labels are
  ```
  "id2label": {
      "0": "entailment",
      "1": "neutral",
      "2": "contradiction"
    },
  ```
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 3e-05
  - train_batch_size: 16
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 32
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.5
  - num_epochs: 10.0
  - mixed_precision_training: Native AMP
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.13.0+cu117
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  