readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: sentiment_roberta_large_with_diary
    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. -->
  
  # sentiment_roberta_large_with_diary
  
  This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5671
  - Micro f1 score: 80.0000
  - Auprc: 77.0282
  - Accuracy: 0.8
  
  ## 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
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 1.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Micro f1 score | Auprc   | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------------:|:-------:|:--------:|
  | 1.6198        | 0.13  | 100  | 1.3872          | 48.9362        | 55.5743 | 0.4894   |
  | 0.6603        | 0.26  | 200  | 0.9249          | 65.9574        | 62.8759 | 0.6596   |
  | 0.5387        | 0.4   | 300  | 0.7262          | 73.1915        | 71.1936 | 0.7319   |
  | 0.4801        | 0.53  | 400  | 0.6623          | 74.0426        | 68.8606 | 0.7404   |
  | 0.4597        | 0.66  | 500  | 0.6092          | 76.1702        | 75.7346 | 0.7617   |
  | 0.4217        | 0.79  | 600  | 0.5929          | 78.7234        | 76.8709 | 0.7872   |
  | 0.4148        | 0.93  | 700  | 0.5671          | 80.0000        | 77.0282 | 0.8      |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  