readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - aihub_paper_summarization
  metrics:
  - rouge
  model-index:
  - name: kobart-base-v2-finetuned-paper
    results:
    - task:
        name: Sequence-to-sequence Language Modeling
        type: text2text-generation
      dataset:
        name: aihub_paper_summarization
        type: aihub_paper_summarization
        config: default
        split: train
        args: default
      metrics:
      - name: Rouge1
        type: rouge
        value: 6.2883
  ---
  
  <!-- 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. -->
  
  # kobart-base-v2-finetuned-paper
  
  This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the aihub_paper_summarization dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2966
  - Rouge1: 6.2883
  - Rouge2: 1.7038
  - Rougel: 6.2556
  - Rougelsum: 6.2618
  - Gen Len: 20.0
  
  ## 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: 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: 3
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
  | 1.2215        | 1.0   | 8831  | 1.3293          | 6.2425 | 1.7317 | 6.2246 | 6.2247    | 20.0    |
  | 1.122         | 2.0   | 17662 | 1.3056          | 6.2298 | 1.7005 | 6.2042 | 6.2109    | 20.0    |
  | 1.0914        | 3.0   | 26493 | 1.2966          | 6.2883 | 1.7038 | 6.2556 | 6.2618    | 20.0    |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.1
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  