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metadata
license: apache-2.0
base_model: facebook/bart-large
tags:
  - generated_from_trainer
datasets:
  - clupubhealth
metrics:
  - rouge
model-index:
  - name: bart-pubhealth-expanded-hi-grad
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: clupubhealth
          type: clupubhealth
          config: expanded
          split: test
          args: expanded
        metrics:
          - name: Rouge1
            type: rouge
            value: 30.2592

bart-pubhealth-expanded-hi-grad

This model is a fine-tuned version of facebook/bart-large on the clupubhealth dataset. It achieves the following results on the evaluation set:

  • Loss: 2.0581
  • Rouge1: 30.2592
  • Rouge2: 11.7027
  • Rougel: 24.1706
  • Rougelsum: 24.3596
  • Gen Len: 19.95

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: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 950
  • total_train_batch_size: 15200
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 10

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum Gen Len
3.7893 0.49 2 2.3943 20.5187 5.4764 15.9378 16.2797 20.0
3.4045 0.98 4 2.1599 24.0858 7.8207 19.0412 19.1609 19.88
3.2488 1.47 6 2.1026 27.3466 9.369 21.1419 21.3136 19.865
3.1823 1.96 8 2.1324 28.825 9.6007 22.0963 22.3776 19.82
3.1263 2.44 10 2.1105 29.2694 10.5001 23.2842 23.5473 19.85
3.0834 2.93 12 2.0837 28.5975 10.2016 22.048 22.1341 19.915
3.0283 3.42 14 2.0773 28.5813 10.447 22.7456 22.8496 19.91
3.0301 3.91 16 2.0730 30.1049 11.4375 24.083 24.3045 19.945
2.9851 4.4 18 2.0775 29.2224 10.2722 22.7019 23.0038 19.95
2.9769 4.89 20 2.0777 29.6981 10.7044 23.2487 23.5232 19.96
2.9623 5.38 22 2.0711 29.0438 10.5105 23.1751 23.415 19.92
2.9421 5.87 24 2.0676 29.096 10.6599 23.1381 23.3765 19.985
2.9234 6.36 26 2.0646 29.6561 10.9096 23.2384 23.4265 19.985
2.9107 6.85 28 2.0616 29.7134 11.1686 23.272 23.4475 19.985
2.9077 7.33 30 2.0593 29.5055 11.0256 23.4406 23.6653 19.955
2.9072 7.82 32 2.0585 30.0504 11.433 23.9176 24.1728 19.95
2.8951 8.31 34 2.0583 29.9401 11.602 23.948 24.1323 19.95
2.8955 8.8 36 2.0584 30.1158 11.4745 24.0509 24.2465 19.94
2.8774 9.29 38 2.0582 30.0476 11.4465 23.8956 24.0527 19.945
2.8851 9.78 40 2.0581 30.2592 11.7027 24.1706 24.3596 19.95

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 2.7.1
  • Tokenizers 0.13.2