readme: ---
  language:
  - en
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - accuracy
  model-index:
  - name: roberta-base-sst2
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: GLUE SST2
        type: glue
        args: sst2
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9357798165137615
  ---
  
  <!-- 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-sst2
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the GLUE SST2 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2314
  - Accuracy: 0.9358
  
  ## 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: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.06
  - num_epochs: 10.0
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.2287        | 1.0   | 4210  | 0.2314          | 0.9358   |
  | 0.1959        | 2.0   | 8420  | 0.3027          | 0.9266   |
  | 0.1635        | 3.0   | 12630 | 0.3022          | 0.9300   |
  | 0.1148        | 4.0   | 16840 | 0.3162          | 0.9289   |
  
  
  ### Framework versions
  
  - Transformers 4.20.0.dev0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  