readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: roberta-scarcasm-discriminator
    results: []
  ---
  
  <!-- 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-scarcasm-discriminator
  
  roberta-base
  
  label0: unsarcasitic
  
  label1: sarcastic
  The fine tune method in my github https://github.com/yangyangxusheng/Fine-tune-use-transformers
  
  This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.1844
  - Accuracy: 0.9698
  
  ## 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: 5e-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
  - lr_scheduler_warmup_steps: 500
  - num_epochs: 4
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.144         | 1.0   | 2179 | 0.2522          | 0.9215   |
  | 0.116         | 2.0   | 4358 | 0.2105          | 0.9530   |
  | 0.0689        | 3.0   | 6537 | 0.2015          | 0.9610   |
  | 0.028         | 4.0   | 8716 | 0.1844          | 0.9698   |
  
  
  ### Framework versions
  
  - Transformers 4.12.3
  - Pytorch 1.9.0+cu111
  - Datasets 1.15.1
  - Tokenizers 0.10.3
  