readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        config: sst2
        split: train
        args: sst2
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9071100917431193
  ---
  
  <!-- 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. -->
  
  # distilbert-base-uncased-finetuned-sst2
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2842
  - Accuracy: 0.9071
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | No log        | 0.02  | 100  | 0.3316          | 0.8624   |
  | No log        | 0.05  | 200  | 0.3357          | 0.8612   |
  | No log        | 0.07  | 300  | 0.3996          | 0.8383   |
  | No log        | 0.1   | 400  | 0.3012          | 0.8716   |
  | 0.3421        | 0.12  | 500  | 0.3227          | 0.8693   |
  | 0.3421        | 0.14  | 600  | 0.3643          | 0.8727   |
  | 0.3421        | 0.17  | 700  | 0.2734          | 0.8853   |
  | 0.3421        | 0.19  | 800  | 0.3077          | 0.8945   |
  | 0.3421        | 0.21  | 900  | 0.2709          | 0.9002   |
  | 0.2705        | 0.24  | 1000 | 0.2737          | 0.8899   |
  | 0.2705        | 0.26  | 1100 | 0.3079          | 0.8979   |
  | 0.2705        | 0.29  | 1200 | 0.2713          | 0.8968   |
  | 0.2705        | 0.31  | 1300 | 0.2505          | 0.8933   |
  | 0.2705        | 0.33  | 1400 | 0.2932          | 0.8922   |
  | 0.239         | 0.36  | 1500 | 0.2842          | 0.9071   |
  | 0.239         | 0.38  | 1600 | 0.2509          | 0.9014   |
  | 0.239         | 0.4   | 1700 | 0.2819          | 0.8853   |
  | 0.239         | 0.43  | 1800 | 0.2515          | 0.8956   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  