readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  - f1
  model-index:
  - name: test-trainer-to-hub
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: mrpc
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8455882352941176
      - name: F1
        type: f1
        value: 0.893760539629005
  ---
  
  <!-- 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. -->
  
  # test-trainer-to-hub
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.7352
  - Accuracy: 0.8456
  - F1: 0.8938
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
  | No log        | 1.0   | 459  | 0.4489          | 0.8235   | 0.8792 |
  | 0.5651        | 2.0   | 918  | 0.4885          | 0.8260   | 0.8811 |
  | 0.3525        | 3.0   | 1377 | 0.7352          | 0.8456   | 0.8938 |
  
  
  ### Framework versions
  
  - Transformers 4.16.2
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  