readme: ---
  language:
  - en
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - OpenTable
  metrics:
  - accuracy
  model-index:
  - name: roberta-base.CEBaB_confounding.uniform.absa.5-class.seed_42
    results:
    - task:
        name: Text Classification
        type: text-classification
      dataset:
        name: OpenTable OPENTABLE-ABSA
        type: OpenTable
        args: opentable-absa
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9024887800897593
  ---
  
  <!-- 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.CEBaB_confounding.uniform.absa.5-class.seed_42
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the OpenTable OPENTABLE-ABSA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3315
  - Accuracy: 0.9025
  - Macro-f1: 0.9009
  - Weighted-macro-f1: 0.9025
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5.0
  
  ### Training results
  
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.2+cu102
  - Datasets 2.5.2
  - Tokenizers 0.12.1
  