readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model_index:
  - name: hackMIT-finetuned-sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: glue
        type: glue
        args: sst2
      metric:
        name: Accuracy
        type: accuracy
        value: 0.7970183486238532
  ---
  
  <!-- 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. -->
  
  # hackMIT-finetuned-sst2
  
  This model is a fine-tuned version of [Blaine-Mason/hackMIT-finetuned-sst2](https://huggingface.co/Blaine-Mason/hackMIT-finetuned-sst2) on the glue dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.0046
  - Accuracy: 0.7970
  
  ## 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: 1.7339491016138283e-05
  - train_batch_size: 64
  - eval_batch_size: 16
  - seed: 23
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.0652        | 1.0   | 1053 | 0.9837          | 0.7970   |
  | 0.0586        | 2.0   | 2106 | 0.9927          | 0.7959   |
  | 0.0549        | 3.0   | 3159 | 1.0046          | 0.7970   |
  
  
  ### Framework versions
  
  - Transformers 4.9.2
  - Pytorch 1.9.0+cu102
  - Datasets 1.11.0
  - Tokenizers 0.10.3
  