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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - cnn_dailymail
metrics:
  - rouge
model-index:
  - name: led-large-16384-cnn_dailymail
    results:
      - task:
          name: Sequence-to-sequence Language Modeling
          type: text2text-generation
        dataset:
          name: cnn_dailymail
          type: cnn_dailymail
          config: 3.0.0
          split: test
          args: 3.0.0
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.38289524455734836

led-large-16384-cnn_dailymail

This model is a fine-tuned version of allenai/led-base-16384 on the cnn_dailymail dataset. It achieves the following results on the evaluation set:

  • Loss: 1.5981
  • Rouge1: 0.3829
  • Rouge2: 0.1704
  • Rougel: 0.2569
  • Rougelsum: 0.3614

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: 5e-05
  • train_batch_size: 2
  • eval_batch_size: 2
  • seed: 42
  • gradient_accumulation_steps: 64
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 6

Training results

Training Loss Epoch Step Validation Loss Rouge1 Rouge2 Rougel Rougelsum
1.9531 0.4 500 1.8639 0.3485 0.1441 0.2275 0.3288
1.9563 0.8 1000 1.8260 0.3538 0.1482 0.2315 0.3343
1.7176 1.2 1500 1.8208 0.3628 0.1527 0.2383 0.3433
1.7197 1.6 2000 1.8162 0.3696 0.1602 0.2434 0.3486
1.8086 2.0 2500 1.7924 0.3558 0.1533 0.2334 0.3361
1.2448 2.4 3000 1.8510 0.3703 0.1591 0.2447 0.3483
1.3574 2.8 3500 1.8277 0.3741 0.1593 0.2422 0.3540
1.0966 3.2 4000 1.8924 0.3682 0.1576 0.2424 0.3479
0.9938 3.6 4500 1.8957 0.3723 0.1599 0.2451 0.3511
1.0735 4.0 5000 1.8772 0.3653 0.1557 0.2399 0.3454
0.9106 4.4 5500 1.9401 0.3720 0.1585 0.2436 0.3504
1.015 4.8 6000 1.9320 0.3725 0.1570 0.2429 0.3515
1.7854 0.36 6500 1.7800 0.3624 0.1544 0.2390 0.3422
1.9079 0.39 7000 1.7629 0.3573 0.1553 0.2352 0.3370
1.7606 3.34 7500 1.6902 0.3783 0.1673 0.2521 0.3570
1.7571 3.57 8000 1.6563 0.3802 0.1691 0.2538 0.3587
1.6602 3.79 8500 1.6439 0.3814 0.1693 0.2548 0.3600
1.6614 4.01 9000 1.6312 0.3812 0.1691 0.2544 0.3599
1.668 4.24 9500 1.6189 0.3815 0.1689 0.2550 0.3603
1.6491 4.46 10000 1.6172 0.3799 0.1681 0.2540 0.3586
1.5994 4.68 10500 1.6132 0.3825 0.1702 0.2560 0.3610
1.6493 4.9 11000 1.6093 0.3828 0.1701 0.2561 0.3613
1.6769 5.13 11500 1.6074 0.3831 0.1706 0.2569 0.3619
1.6554 5.35 12000 1.6044 0.3817 0.1695 0.2559 0.3605
1.6155 5.57 12500 1.6010 0.3825 0.1700 0.2561 0.3608
1.5863 5.8 13000 1.5981 0.3829 0.1704 0.2569 0.3614

Framework versions

  • Transformers 4.30.2
  • Pytorch 1.13.1
  • Datasets 2.13.0
  • Tokenizers 0.13.3