readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: bart-large-cnn-finetuned-roundup-16
    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-large-cnn-finetuned-roundup-16
  
  This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.8957
  - Rouge1: 49.4097
  - Rouge2: 29.3516
  - Rougel: 31.527
  - Rougelsum: 46.4241
  - Gen Len: 141.9
  
  ## 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: 2
  - eval_batch_size: 2
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 16
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
  | No log        | 1.0   | 132  | 1.3170          | 48.412  | 29.2017 | 31.6679 | 45.494    | 141.85  |
  | No log        | 2.0   | 264  | 1.2292          | 49.0133 | 29.6645 | 30.7612 | 46.1673   | 142.0   |
  | No log        | 3.0   | 396  | 1.2670          | 49.183  | 29.4104 | 31.573  | 46.7082   | 142.0   |
  | 0.9596        | 4.0   | 528  | 1.3059          | 47.3854 | 26.6865 | 28.4666 | 44.4934   | 141.8   |
  | 0.9596        | 5.0   | 660  | 1.3288          | 48.1189 | 26.9242 | 31.2938 | 45.3462   | 142.0   |
  | 0.9596        | 6.0   | 792  | 1.4084          | 47.5713 | 26.7488 | 29.2959 | 45.1764   | 141.3   |
  | 0.9596        | 7.0   | 924  | 1.5043          | 46.5407 | 26.0995 | 29.9007 | 43.9335   | 142.0   |
  | 0.3369        | 8.0   | 1056 | 1.5115          | 49.6891 | 29.0514 | 32.33   | 46.9357   | 142.0   |
  | 0.3369        | 9.0   | 1188 | 1.6131          | 47.5773 | 27.6348 | 30.5294 | 45.1151   | 142.0   |
  | 0.3369        | 10.0  | 1320 | 1.6837          | 46.5699 | 26.3805 | 29.8581 | 43.5252   | 142.0   |
  | 0.3369        | 11.0  | 1452 | 1.7874          | 47.1383 | 26.535  | 30.1724 | 44.2508   | 142.0   |
  | 0.148         | 12.0  | 1584 | 1.7776          | 49.8061 | 30.1994 | 33.2405 | 47.6102   | 142.0   |
  | 0.148         | 13.0  | 1716 | 1.8144          | 48.4451 | 28.2949 | 30.9026 | 45.6614   | 142.0   |
  | 0.148         | 14.0  | 1848 | 1.8646          | 50.1964 | 30.4426 | 32.8156 | 47.4134   | 142.0   |
  | 0.148         | 15.0  | 1980 | 1.8829          | 48.8129 | 29.2358 | 32.3247 | 46.2233   | 142.0   |
  | 0.0726        | 16.0  | 2112 | 1.8957          | 49.4097 | 29.3516 | 31.527  | 46.4241   | 141.9   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  