readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - scim
  metrics:
  - accuracy
  model_index:
  - name: roberta-base-finetuned-sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: scim
        type: scim
        args: eod
      metric:
        name: Accuracy
        type: accuracy
        value: 0.9111111111111111
  ---
  
  <!-- 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. -->
  
  # roberta-base-finetuned-sst2
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the scim dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4632
  - Accuracy: 0.9111
  
  ## 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: 4
  - eval_batch_size: 4
  - 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        | 1.0   | 90   | 2.0273          | 0.6667   |
  | No log        | 2.0   | 180  | 0.8802          | 0.8556   |
  | No log        | 3.0   | 270  | 0.5908          | 0.8889   |
  | No log        | 4.0   | 360  | 0.4632          | 0.9111   |
  | No log        | 5.0   | 450  | 0.4294          | 0.9111   |
  
  
  ### Framework versions
  
  - Transformers 4.9.1
  - Pytorch 1.9.0+cu102
  - Datasets 1.11.0
  - Tokenizers 0.10.3
  