readme: ---
  tags:
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: robeta-large-batch128
    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. -->
  
  # robeta-large-batch128
  
  This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on the None dataset.
  It achieves the following results on the evaluation set:
  - Exact Match: 60.4167
  - F1: 67.7400
  - Loss: 1.8242
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - gradient_accumulation_steps: 16
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 15
  - mixed_precision_training: Native AMP
  - label_smoothing_factor: 0.1
  
  ### Training results
  
  | Training Loss | Epoch | Step | Exact Match | F1      | Validation Loss |
  |:-------------:|:-----:|:----:|:-----------:|:-------:|:---------------:|
  | 2.5177        | 2.08  | 100  | 62.9167     | 75.0527 | 1.2793          |
  | 0.7171        | 4.17  | 200  | 60.8333     | 69.9682 | 1.2480          |
  | 0.3504        | 6.25  | 300  | 66.6667     | 76.6871 | 1.2969          |
  | 0.1727        | 8.33  | 400  | 64.1667     | 73.7049 | 1.2617          |
  | 0.3793        | 10.42 | 500  | 58.3333     | 68.6892 | 1.5420          |
  | 0.1726        | 12.5  | 600  | 66.25       | 74.7943 | 1.8848          |
  | 0.0974        | 14.58 | 700  | 60.4167     | 67.7400 | 1.8242          |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu117
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  