readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - commonsense_qa
  metrics:
  - accuracy
  model_index:
  - name: roberta-large-finetuned-csqa
    results:
    - dataset:
        name: commonsense_qa
        type: commonsense_qa
        args: default
      metric:
        name: Accuracy
        type: accuracy
        value: 0.7330057621002197
  ---
  
  <!-- 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-large-finetuned-csqa
  
  This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the commonsense_qa dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9146
  - Accuracy: 0.7330
  
  ## 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: 1e-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
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.3903        | 1.0   | 609  | 0.8845          | 0.6642   |
  | 0.8939        | 2.0   | 1218 | 0.7054          | 0.7281   |
  | 0.6163        | 3.0   | 1827 | 0.7452          | 0.7314   |
  | 0.4245        | 4.0   | 2436 | 0.8369          | 0.7355   |
  | 0.3258        | 5.0   | 3045 | 0.9146          | 0.7330   |
  
  
  ### Framework versions
  
  - Transformers 4.9.0
  - Pytorch 1.9.0
  - Datasets 1.10.2
  - Tokenizers 0.10.3
  