readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: BERT_MC_OpenBookQA_w_wrong_context
    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. -->
  
  # BERT_MC_OpenBookQA_w_wrong_context
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7450
  - Accuracy: 0.922
  
  ## 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: 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: 11
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.3525        | 1.0   | 1859  | 0.2696          | 0.906    |
  | 0.2084        | 2.0   | 3718  | 0.3284          | 0.9143   |
  | 0.1263        | 3.0   | 5577  | 0.4205          | 0.9143   |
  | 0.0734        | 4.0   | 7436  | 0.4688          | 0.9203   |
  | 0.0437        | 5.0   | 9295  | 0.6266          | 0.9173   |
  | 0.0357        | 6.0   | 11154 | 0.6934          | 0.9207   |
  | 0.0264        | 7.0   | 13013 | 0.6947          | 0.92     |
  | 0.0098        | 8.0   | 14872 | 0.6800          | 0.9197   |
  | 0.0104        | 9.0   | 16731 | 0.7393          | 0.923    |
  | 0.0067        | 10.0  | 18590 | 0.7846          | 0.9217   |
  | 0.0034        | 11.0  | 20449 | 0.7450          | 0.922    |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.12.1
  - Datasets 2.5.1
  - Tokenizers 0.11.0
  