readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: bart-large-cnn-finetuned-roundup
    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
  
  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: 0.8956
  - Rouge1: 58.1914
  - Rouge2: 45.822
  - Rougel: 49.4407
  - Rougelsum: 56.6379
  - Gen Len: 142.0
  
  ## 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: 1
  - eval_batch_size: 1
  - 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  |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
  | 1.2575        | 1.0   | 795   | 0.9154          | 53.8792 | 34.3203 | 35.8768 | 51.1789   | 142.0    |
  | 0.7053        | 2.0   | 1590  | 0.7921          | 54.3918 | 35.3346 | 37.7539 | 51.6989   | 142.0    |
  | 0.5379        | 3.0   | 2385  | 0.7566          | 52.1651 | 32.5699 | 36.3105 | 49.3327   | 141.5185 |
  | 0.3496        | 4.0   | 3180  | 0.7584          | 54.3258 | 36.403  | 39.6938 | 52.0186   | 142.0    |
  | 0.2688        | 5.0   | 3975  | 0.7343          | 55.9101 | 39.0709 | 42.4138 | 53.572    | 141.8333 |
  | 0.1815        | 6.0   | 4770  | 0.7924          | 53.9272 | 36.8138 | 40.0614 | 51.7496   | 142.0    |
  | 0.1388        | 7.0   | 5565  | 0.7674          | 55.0347 | 38.7978 | 42.0081 | 53.0297   | 142.0    |
  | 0.1048        | 8.0   | 6360  | 0.7700          | 55.2993 | 39.4075 | 42.6837 | 53.5179   | 141.9815 |
  | 0.0808        | 9.0   | 7155  | 0.7796          | 56.1508 | 40.0863 | 43.2178 | 53.7908   | 142.0    |
  | 0.0719        | 10.0  | 7950  | 0.8057          | 56.2302 | 41.3004 | 44.7921 | 54.4304   | 142.0    |
  | 0.0503        | 11.0  | 8745  | 0.8259          | 55.7603 | 41.0643 | 44.5518 | 54.2305   | 142.0    |
  | 0.0362        | 12.0  | 9540  | 0.8604          | 55.8612 | 41.5984 | 44.444  | 54.2493   | 142.0    |
  | 0.0307        | 13.0  | 10335 | 0.8516          | 57.7259 | 44.542  | 47.6724 | 56.0166   | 142.0    |
  | 0.0241        | 14.0  | 11130 | 0.8826          | 56.7943 | 43.7139 | 47.2866 | 55.1824   | 142.0    |
  | 0.0193        | 15.0  | 11925 | 0.8856          | 57.4135 | 44.3147 | 47.9136 | 55.8843   | 142.0    |
  | 0.0154        | 16.0  | 12720 | 0.8956          | 58.1914 | 45.822  | 49.4407 | 56.6379   | 142.0    |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  