readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: distilbart-cnn-arxiv-pubmed-v3-e32
    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. -->
  
  # distilbart-cnn-arxiv-pubmed-v3-e32
  
  This model is a fine-tuned version of [theojolliffe/distilbart-cnn-arxiv-pubmed](https://huggingface.co/theojolliffe/distilbart-cnn-arxiv-pubmed) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9622
  - Rouge1: 58.4519
  - Rouge2: 45.6847
  - Rougel: 49.3188
  - Rougelsum: 57.1351
  - Gen Len: 141.9815
  
  ## 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: 32
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
  | 1.4924        | 1.0   | 795   | 1.0924          | 52.3565 | 32.9081 | 34.6648 | 49.6351   | 142.0    |
  | 0.8865        | 2.0   | 1590  | 0.9394          | 54.2962 | 35.9725 | 38.3888 | 51.5708   | 140.9815 |
  | 0.6979        | 3.0   | 2385  | 0.8831          | 53.6795 | 35.226  | 37.4988 | 51.4424   | 141.8704 |
  | 0.4868        | 4.0   | 3180  | 0.8457          | 53.9141 | 35.2212 | 37.6423 | 51.63     | 142.0    |
  | 0.3903        | 5.0   | 3975  | 0.8252          | 54.8908 | 36.8468 | 39.072  | 52.6068   | 141.8704 |
  | 0.2725        | 6.0   | 4770  | 0.8338          | 54.2424 | 36.4675 | 39.6312 | 51.9973   | 142.0    |
  | 0.2177        | 7.0   | 5565  | 0.8224          | 54.0085 | 36.9395 | 39.7131 | 51.8476   | 142.0    |
  | 0.1736        | 8.0   | 6360  | 0.8001          | 55.5106 | 38.8828 | 41.7174 | 53.3171   | 141.7222 |
  | 0.1368        | 9.0   | 7155  | 0.8036          | 56.7284 | 40.8327 | 42.8486 | 54.6505   | 141.8519 |
  | 0.1272        | 10.0  | 7950  | 0.8197          | 54.5703 | 38.5037 | 41.591  | 52.4417   | 141.2963 |
  | 0.0977        | 11.0  | 8745  | 0.8463          | 55.3691 | 40.5406 | 43.9156 | 53.6637   | 141.7593 |
  | 0.0768        | 12.0  | 9540  | 0.8467          | 56.7099 | 41.6472 | 44.8171 | 54.8111   | 142.0    |
  | 0.0702        | 13.0  | 10335 | 0.8488          | 56.6646 | 41.2164 | 43.8938 | 54.7209   | 142.0    |
  | 0.0597        | 14.0  | 11130 | 0.8543          | 55.7245 | 40.9593 | 42.5698 | 53.8763   | 142.0    |
  | 0.0514        | 15.0  | 11925 | 0.8567          | 56.4837 | 41.8224 | 44.5484 | 54.9102   | 142.0    |
  | 0.045         | 16.0  | 12720 | 0.8794          | 57.5862 | 43.4725 | 46.3658 | 55.9579   | 142.0    |
  | 0.0367        | 17.0  | 13515 | 0.8974          | 57.1023 | 42.9042 | 45.8444 | 55.2216   | 142.0    |
  | 0.0346        | 18.0  | 14310 | 0.9143          | 57.7781 | 43.8333 | 47.0943 | 56.0032   | 142.0    |
  | 0.03          | 19.0  | 15105 | 0.9044          | 56.9211 | 41.9678 | 44.5081 | 54.8092   | 141.6667 |
  | 0.0241        | 20.0  | 15900 | 0.9109          | 57.7747 | 44.1122 | 46.5743 | 55.9199   | 141.8148 |
  | 0.0225        | 21.0  | 16695 | 0.9180          | 56.2307 | 42.2787 | 45.602  | 54.6285   | 142.0    |
  | 0.0184        | 22.0  | 17490 | 0.9120          | 57.4024 | 43.657  | 46.5646 | 55.4614   | 142.0    |
  | 0.0182        | 23.0  | 18285 | 0.9262          | 57.292  | 42.8935 | 46.1294 | 55.3741   | 141.963  |
  | 0.016         | 24.0  | 19080 | 0.9268          | 58.2018 | 44.3914 | 47.7056 | 56.4628   | 142.0    |
  | 0.0139        | 25.0  | 19875 | 0.9373          | 58.1187 | 44.7233 | 47.8946 | 56.26     | 142.0    |
  | 0.0125        | 26.0  | 20670 | 0.9300          | 57.8399 | 44.3073 | 48.4549 | 56.1325   | 141.8889 |
  | 0.012         | 27.0  | 21465 | 0.9487          | 57.8585 | 43.8361 | 47.6488 | 56.2748   | 142.0    |
  | 0.0095        | 28.0  | 22260 | 0.9620          | 57.5966 | 44.0481 | 46.8771 | 56.079    | 141.6852 |
  | 0.009         | 29.0  | 23055 | 0.9526          | 57.8869 | 44.2234 | 48.0884 | 56.3158   | 141.9815 |
  | 0.008         | 30.0  | 23850 | 0.9626          | 58.2649 | 45.0371 | 48.5288 | 56.7707   | 141.9815 |
  | 0.0076        | 31.0  | 24645 | 0.9640          | 58.1467 | 45.0457 | 48.7258 | 56.7111   | 141.3704 |
  | 0.0072        | 32.0  | 25440 | 0.9622          | 58.4519 | 45.6847 | 49.3188 | 57.1351   | 141.9815 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  