readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: BART_corrector_15
    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. -->
  
  # BART_corrector_15
  
  This model is a fine-tuned version of [ainize/bart-base-cnn](https://huggingface.co/ainize/bart-base-cnn) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0214
  - Rouge1: 80.3263
  - Rouge2: 78.1274
  - Rougel: 80.3215
  - Rougelsum: 80.3039
  - Gen Len: 19.3993
  
  ## 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: 8
  - eval_batch_size: 8
  - 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 |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | 0.0597        | 1.0   | 2365  | 0.0367          | 79.3503 | 76.3308 | 79.32   | 79.3005   | 19.3992 |
  | 0.0322        | 2.0   | 4730  | 0.0276          | 79.9515 | 77.4211 | 79.9331 | 79.9164   | 19.3983 |
  | 0.0212        | 3.0   | 7095  | 0.0241          | 80.1413 | 77.8084 | 80.129  | 80.1098   | 19.3992 |
  | 0.0148        | 4.0   | 9460  | 0.0219          | 80.2625 | 78.035  | 80.2579 | 80.2372   | 19.4    |
  | 0.0111        | 5.0   | 11825 | 0.0214          | 80.3263 | 78.1274 | 80.3215 | 80.3039   | 19.3993 |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  