readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: bert-base-cased-rte
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE RTE
        type: glue
        args: rte
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.6534296028880866
  ---
  
  <!-- 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-base-cased-rte
  
  This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the GLUE RTE dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9753
  - Accuracy: 0.6534
  
  ## 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: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.06
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4837        | 3.21  | 500  | 0.9753          | 0.6534   |
  | 0.0827        | 6.41  | 1000 | 1.6693          | 0.6715   |
  | 0.0253        | 9.62  | 1500 | 1.7777          | 0.6643   |
  
  
  ### Framework versions
  
  - Transformers 4.21.3
  - Pytorch 1.7.1
  - Datasets 1.18.3
  - Tokenizers 0.11.6
  