readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: ainize-kobart-news-eb-finetuned-meetings-papers
    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. -->
  
  # ainize-kobart-news-eb-finetuned-meetings-papers
  
  This model is a fine-tuned version of [ainize/kobart-news](https://huggingface.co/ainize/kobart-news) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3289
  - Rouge1: 17.3988
  - Rouge2: 7.0454
  - Rougel: 17.3877
  - Rougelsum: 17.42
  - Gen Len: 19.9473
  
  ## 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: 4
  - eval_batch_size: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2 | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
  | 0.1402        | 1.0   | 7588  | 0.2930          | 17.1421 | 7.0141 | 17.1211 | 17.1473   | 19.9374 |
  | 0.0997        | 2.0   | 15176 | 0.2842          | 17.1692 | 6.8824 | 17.1557 | 17.1985   | 19.9435 |
  | 0.0692        | 3.0   | 22764 | 0.3052          | 17.4241 | 7.1083 | 17.4028 | 17.4472   | 19.9453 |
  | 0.0556        | 4.0   | 30352 | 0.3289          | 17.3988 | 7.0454 | 17.3877 | 17.42     | 19.9473 |
  | 0.0533        | 5.0   | 37940 | 0.3289          | 17.3988 | 7.0454 | 17.3877 | 17.42     | 19.9473 |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  