readme: ---
  license: mit
  tags:
  - generated_from_trainer
  metrics:
  - rouge
  model-index:
  - name: bart-cnn-pubmed-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. -->
  
  # bart-cnn-pubmed-arxiv-pubmed-v3-e32
  
  This model is a fine-tuned version of [theojolliffe/bart-cnn-pubmed-arxiv-pubmed](https://huggingface.co/theojolliffe/bart-cnn-pubmed-arxiv-pubmed) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.9707
  - Rouge1: 58.6575
  - Rouge2: 47.1055
  - Rougel: 50.0715
  - Rougelsum: 57.58
  - 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: 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: 32
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len  |
  |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
  | No log        | 1.0   | 398   | 0.9589          | 52.4374 | 32.0538 | 34.189  | 49.8178   | 142.0    |
  | 1.1222        | 2.0   | 796   | 0.8144          | 54.363  | 35.2782 | 37.5982 | 51.9121   | 142.0    |
  | 0.6686        | 3.0   | 1194  | 0.7747          | 53.3334 | 34.9112 | 38.1684 | 50.9676   | 142.0    |
  | 0.4394        | 4.0   | 1592  | 0.7660          | 53.2391 | 34.1677 | 38.4917 | 50.582    | 142.0    |
  | 0.4394        | 5.0   | 1990  | 0.7508          | 54.3922 | 36.631  | 39.6881 | 52.4238   | 142.0    |
  | 0.2962        | 6.0   | 2388  | 0.8112          | 53.9595 | 36.1326 | 38.937  | 51.8107   | 142.0    |
  | 0.201         | 7.0   | 2786  | 0.7842          | 55.3659 | 38.4021 | 41.1556 | 53.3145   | 142.0    |
  | 0.1414        | 8.0   | 3184  | 0.7557          | 54.8476 | 38.7707 | 41.8756 | 53.3081   | 142.0    |
  | 0.107         | 9.0   | 3582  | 0.8296          | 55.7594 | 39.3691 | 41.6456 | 53.9381   | 142.0    |
  | 0.107         | 10.0  | 3980  | 0.8298          | 54.8163 | 38.9233 | 42.4104 | 52.9344   | 142.0    |
  | 0.0838        | 11.0  | 4378  | 0.8492          | 56.3438 | 41.5532 | 44.6348 | 54.6106   | 141.8704 |
  | 0.0637        | 12.0  | 4776  | 0.8619          | 56.8559 | 41.2682 | 43.4566 | 54.7799   | 142.0    |
  | 0.051         | 13.0  | 5174  | 0.8733          | 57.4154 | 42.6009 | 44.401  | 56.0209   | 142.0    |
  | 0.04          | 14.0  | 5572  | 0.8777          | 58.3095 | 44.7657 | 47.8527 | 56.7276   | 142.0    |
  | 0.04          | 15.0  | 5970  | 0.8711          | 57.6542 | 43.1785 | 46.3796 | 56.0532   | 142.0    |
  | 0.0341        | 16.0  | 6368  | 0.9038          | 57.7274 | 43.5198 | 45.8797 | 56.1525   | 142.0    |
  | 0.0272        | 17.0  | 6766  | 0.8845          | 58.4461 | 44.9513 | 47.6616 | 57.0634   | 142.0    |
  | 0.0231        | 18.0  | 7164  | 0.9108          | 58.5774 | 46.2637 | 49.9201 | 57.1939   | 141.963  |
  | 0.018         | 19.0  | 7562  | 0.9059          | 58.7442 | 44.7141 | 47.6061 | 57.3604   | 142.0    |
  | 0.018         | 20.0  | 7960  | 0.9133          | 57.2809 | 43.7722 | 46.2016 | 55.4421   | 142.0    |
  | 0.0159        | 21.0  | 8358  | 0.9245          | 57.1685 | 44.5445 | 48.5015 | 55.9304   | 142.0    |
  | 0.012         | 22.0  | 8756  | 0.9149          | 57.4727 | 44.2417 | 48.0224 | 56.1341   | 141.9444 |
  | 0.0109        | 23.0  | 9154  | 0.9472          | 58.3537 | 45.2341 | 47.8222 | 56.8061   | 141.8148 |
  | 0.0082        | 24.0  | 9552  | 0.9426          | 58.1553 | 45.6645 | 49.019  | 56.7908   | 142.0    |
  | 0.0082        | 25.0  | 9950  | 0.9407          | 58.3571 | 46.0699 | 49.382  | 57.1456   | 142.0    |
  | 0.0071        | 26.0  | 10348 | 0.9654          | 59.5689 | 47.2126 | 50.5317 | 58.2492   | 142.0    |
  | 0.0057        | 27.0  | 10746 | 0.9651          | 58.2261 | 46.2797 | 49.8995 | 57.0725   | 142.0    |
  | 0.0049        | 28.0  | 11144 | 0.9555          | 57.3502 | 44.2364 | 47.6214 | 55.69     | 142.0    |
  | 0.0043        | 29.0  | 11542 | 0.9591          | 57.3909 | 44.5927 | 47.541  | 56.2071   | 142.0    |
  | 0.0043        | 30.0  | 11940 | 0.9637          | 58.3275 | 46.1513 | 49.4288 | 57.073    | 142.0    |
  | 0.0033        | 31.0  | 12338 | 0.9705          | 58.4669 | 46.613  | 49.5711 | 57.3531   | 142.0    |
  | 0.0031        | 32.0  | 12736 | 0.9707          | 58.6575 | 47.1055 | 50.0715 | 57.58     | 142.0    |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.11.0+cu113
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  