readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: jpqd-bert-base-ft-sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE SST2
        type: glue
        args: sst2
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9254587155963303
  ---
  
  <!-- 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. -->
  
  # jpqd-bert-base-ft-sst2
  
  > **Note**
  > This model was trained for only 1 epoch and is shared for testing purposes
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2181
  - Accuracy: 0.9255
  
  ## 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: 32
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 1.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.4129        | 0.12  | 250  | 0.4416          | 0.8761   |
  | 0.412         | 0.24  | 500  | 0.4969          | 0.8899   |
  | 0.3191        | 0.36  | 750  | 0.2717          | 0.9163   |
  | 0.2688        | 0.48  | 1000 | 0.2432          | 0.9117   |
  | 0.3306        | 0.59  | 1250 | 0.2033          | 0.9243   |
  | 0.224         | 0.71  | 1500 | 0.2383          | 0.9243   |
  | 0.2082        | 0.83  | 1750 | 0.2233          | 0.9255   |
  | 0.2161        | 0.95  | 2000 | 0.2207          | 0.9255   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1+cu117
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  