readme: ---
  license: cc-by-nc-sa-4.0
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: gpt_16_5_5.6e-5
    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. -->
  
  # gpt_16_5_5.6e-5
  
  This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 4.0127
  - Rouge1: 0.6626
  - Rouge2: 0.1168
  - Rougel: 0.6456
  - Bleu1: 0.0
  - Bleu2: 0.0
  - Bleu3: 0.0
  - Bleu4: 0.0
  - Gen Len: 1.0
  
  ## 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: 5.6e-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_ratio: 0.1
  - num_epochs: 5.0
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Bleu1 | Bleu2 | Bleu3 | Bleu4 | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:-----:|:-----:|:-----:|:-----:|:-------:|
  | 3.1501        | 0.38  | 1000  | 4.1034          | 0.6908 | 0.1016 | 0.6736 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 3.1688        | 0.76  | 2000  | 4.0936          | 0.6447 | 0.1117 | 0.6372 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.8769        | 1.13  | 3000  | 4.0668          | 0.7186 | 0.1365 | 0.7012 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.8633        | 1.51  | 4000  | 4.0363          | 0.625  | 0.1187 | 0.6116 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.8928        | 1.89  | 5000  | 4.0112          | 0.6599 | 0.1237 | 0.6476 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.6186        | 2.27  | 6000  | 4.0204          | 0.6953 | 0.1259 | 0.6761 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.6653        | 2.64  | 7000  | 4.0044          | 0.6795 | 0.1163 | 0.6627 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.5418        | 3.02  | 8000  | 4.0183          | 0.602  | 0.1154 | 0.5883 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.4621        | 3.4   | 9000  | 4.0149          | 0.5889 | 0.101  | 0.578  | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.4651        | 3.78  | 10000 | 3.9979          | 0.604  | 0.1098 | 0.5943 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.3334        | 4.15  | 11000 | 4.0226          | 0.6352 | 0.0993 | 0.6211 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.2859        | 4.53  | 12000 | 4.0152          | 0.6782 | 0.1125 | 0.6593 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  | 2.314         | 4.91  | 13000 | 4.0127          | 0.6626 | 0.1168 | 0.6456 | 0.0   | 0.0   | 0.0   | 0.0   | 1.0     |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.9.0+cu102
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  