readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: albert-base-v2-finetuned-qnli
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: qnli
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9112209408749771
  ---
  
  <!-- 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. -->
  
  # albert-base-v2-finetuned-qnli
  
  This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3194
  - Accuracy: 0.9112
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.3116        | 1.0   | 6547  | 0.2818          | 0.8849   |
  | 0.2467        | 2.0   | 13094 | 0.2532          | 0.9001   |
  | 0.1858        | 3.0   | 19641 | 0.3194          | 0.9112   |
  | 0.1449        | 4.0   | 26188 | 0.4338          | 0.9103   |
  | 0.0584        | 5.0   | 32735 | 0.5752          | 0.9052   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.18.0
  - Tokenizers 0.10.3
  