readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - commonsense_qa
  metrics:
  - accuracy
  model_index:
  - name: aristo-roberta-finetuned-csqa
    results:
    - dataset:
        name: commonsense_qa
        type: commonsense_qa
        args: default
      metric:
        name: Accuracy
        type: accuracy
        value: 0.7305487394332886
  ---
  
  <!-- 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. -->
  
  # aristo-roberta-finetuned-csqa
  
  This model is a fine-tuned version of [LIAMF-USP/aristo-roberta](https://huggingface.co/LIAMF-USP/aristo-roberta) on the commonsense_qa dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2187
  - Accuracy: 0.7305
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.131         | 1.0   | 609  | 0.7109          | 0.7232   |
  | 0.6957        | 2.0   | 1218 | 0.6912          | 0.7346   |
  | 0.459         | 3.0   | 1827 | 0.8364          | 0.7305   |
  | 0.3063        | 4.0   | 2436 | 1.0595          | 0.7322   |
  | 0.2283        | 5.0   | 3045 | 1.2187          | 0.7305   |
  
  
  ### Framework versions
  
  - Transformers 4.9.0
  - Pytorch 1.9.0
  - Datasets 1.10.2
  - Tokenizers 0.10.3
  