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metadata
license: mit
base_model: facebook/bart-large-cnn
tags:
  - generated_from_trainer
datasets:
  - clupubhealth
metrics:
  - rouge
model-index:
  - name: bart-cnn-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: 28.8807

bart-cnn-pubhealth-expanded-hi-grad

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

  • Loss: 2.1939
  • Rouge1: 28.8807
  • Rouge2: 8.9567
  • Rougel: 19.5591
  • Rougelsum: 20.6726
  • Gen Len: 66.99

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
3.4166 0.49 2 2.4019 22.0991 4.6789 15.1628 17.4382 75.065
3.2194 0.98 4 2.3372 25.0981 6.6975 17.4606 19.2018 71.225
3.0969 1.47 6 2.2979 26.4747 7.1948 18.2262 19.6241 67.19
3.0313 1.96 8 2.3038 26.8637 7.5831 18.2923 19.6327 66.875
2.9753 2.44 10 2.2976 27.8942 8.3434 19.095 20.6248 67.975
2.9296 2.93 12 2.2602 28.1255 8.6477 19.0575 20.7787 68.515
2.8681 3.42 14 2.2341 28.0812 8.598 19.3391 20.7526 68.285
2.867 3.91 16 2.2246 28.3624 8.7921 19.5552 21.1147 68.225
2.8157 4.4 18 2.2178 28.8197 8.8423 19.3606 20.698 69.08
2.8007 4.89 20 2.2149 28.34 8.5084 18.8293 20.1169 68.255
2.7797 5.38 22 2.2123 28.2156 8.4891 19.3472 20.5036 67.525
2.7563 5.87 24 2.2083 27.8927 8.3783 19.1194 20.2498 68.365
2.736 6.36 26 2.2035 28.2588 8.2345 18.9418 20.2931 68.335
2.7208 6.85 28 2.2020 28.2471 8.599 19.3465 20.5104 68.44
2.713 7.33 30 2.2022 28.1863 8.5142 19.194 20.2467 68.3
2.7135 7.82 32 2.2013 28.462 8.6346 19.2465 20.4812 68.195
2.6987 8.31 34 2.1988 28.9168 8.8888 19.6491 20.7796 67.275
2.6978 8.8 36 2.1965 28.7303 8.9879 19.5924 20.6943 67.31
2.6769 9.29 38 2.1946 28.7956 8.9652 19.545 20.7352 67.33
2.6821 9.78 40 2.1939 28.8807 8.9567 19.5591 20.6726 66.99

Framework versions

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