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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- cnn_dailymail |
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metrics: |
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- rouge |
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model-index: |
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- name: led-large-16384-cnn_dailymail |
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results: |
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- task: |
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name: Sequence-to-sequence Language Modeling |
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type: text2text-generation |
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dataset: |
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name: cnn_dailymail |
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type: cnn_dailymail |
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config: 3.0.0 |
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split: test |
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args: 3.0.0 |
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metrics: |
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- name: Rouge1 |
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type: rouge |
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value: 0.3869876274946419 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# led-large-16384-cnn_dailymail |
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This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the cnn_dailymail dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5544 |
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- Rouge1: 0.3870 |
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- Rouge2: 0.1736 |
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- Rougel: 0.2599 |
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- Rougelsum: 0.3653 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- gradient_accumulation_steps: 64 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 10 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 1.9531 | 0.4 | 500 | 1.8639 | 0.3485 | 0.1441 | 0.2275 | 0.3288 | |
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| 1.9563 | 0.8 | 1000 | 1.8260 | 0.3538 | 0.1482 | 0.2315 | 0.3343 | |
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| 1.7176 | 1.2 | 1500 | 1.8208 | 0.3628 | 0.1527 | 0.2383 | 0.3433 | |
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| 1.7197 | 1.6 | 2000 | 1.8162 | 0.3696 | 0.1602 | 0.2434 | 0.3486 | |
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| 1.8086 | 2.0 | 2500 | 1.7924 | 0.3558 | 0.1533 | 0.2334 | 0.3361 | |
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| 1.2448 | 2.4 | 3000 | 1.8510 | 0.3703 | 0.1591 | 0.2447 | 0.3483 | |
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| 1.3574 | 2.8 | 3500 | 1.8277 | 0.3741 | 0.1593 | 0.2422 | 0.3540 | |
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| 1.0966 | 3.2 | 4000 | 1.8924 | 0.3682 | 0.1576 | 0.2424 | 0.3479 | |
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| 0.9938 | 3.6 | 4500 | 1.8957 | 0.3723 | 0.1599 | 0.2451 | 0.3511 | |
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| 1.0735 | 4.0 | 5000 | 1.8772 | 0.3653 | 0.1557 | 0.2399 | 0.3454 | |
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| 0.9106 | 4.4 | 5500 | 1.9401 | 0.3720 | 0.1585 | 0.2436 | 0.3504 | |
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| 1.015 | 4.8 | 6000 | 1.9320 | 0.3725 | 0.1570 | 0.2429 | 0.3515 | |
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| 1.7854 | 0.36 | 6500 | 1.7800 | 0.3624 | 0.1544 | 0.2390 | 0.3422 | |
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| 1.9079 | 0.39 | 7000 | 1.7629 | 0.3573 | 0.1553 | 0.2352 | 0.3370 | |
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| 1.7606 | 3.34 | 7500 | 1.6902 | 0.3783 | 0.1673 | 0.2521 | 0.3570 | |
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| 1.7571 | 3.57 | 8000 | 1.6563 | 0.3802 | 0.1691 | 0.2538 | 0.3587 | |
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| 1.6602 | 3.79 | 8500 | 1.6439 | 0.3814 | 0.1693 | 0.2548 | 0.3600 | |
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| 1.6614 | 4.01 | 9000 | 1.6312 | 0.3812 | 0.1691 | 0.2544 | 0.3599 | |
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| 1.668 | 4.24 | 9500 | 1.6189 | 0.3815 | 0.1689 | 0.2550 | 0.3603 | |
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| 1.6491 | 4.46 | 10000 | 1.6172 | 0.3799 | 0.1681 | 0.2540 | 0.3586 | |
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| 1.5994 | 4.68 | 10500 | 1.6132 | 0.3825 | 0.1702 | 0.2560 | 0.3610 | |
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| 1.6493 | 4.9 | 11000 | 1.6093 | 0.3828 | 0.1701 | 0.2561 | 0.3613 | |
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| 1.6769 | 5.13 | 11500 | 1.6074 | 0.3831 | 0.1706 | 0.2569 | 0.3619 | |
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| 1.6554 | 5.35 | 12000 | 1.6044 | 0.3817 | 0.1695 | 0.2559 | 0.3605 | |
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| 1.6155 | 5.57 | 12500 | 1.6010 | 0.3825 | 0.1700 | 0.2561 | 0.3608 | |
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| 1.5863 | 5.8 | 13000 | 1.5981 | 0.3829 | 0.1704 | 0.2569 | 0.3614 | |
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| 1.6306 | 6.02 | 13500 | 1.6004 | 0.3831 | 0.1702 | 0.2563 | 0.3618 | |
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| 1.6425 | 6.24 | 14000 | 1.5987 | 0.3821 | 0.1698 | 0.2561 | 0.3610 | |
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| 1.6863 | 6.46 | 14500 | 1.5876 | 0.3837 | 0.1710 | 0.2569 | 0.3622 | |
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| 1.6085 | 6.69 | 15000 | 1.5815 | 0.3836 | 0.1717 | 0.2573 | 0.3621 | |
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| 1.6267 | 6.91 | 15500 | 1.5792 | 0.3852 | 0.1722 | 0.2579 | 0.3633 | |
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| 1.5637 | 7.13 | 16000 | 1.5768 | 0.3830 | 0.1709 | 0.2568 | 0.3611 | |
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| 1.5586 | 7.36 | 16500 | 1.5740 | 0.3833 | 0.1706 | 0.2567 | 0.3617 | |
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| 1.5389 | 7.58 | 17000 | 1.5689 | 0.3858 | 0.1729 | 0.2590 | 0.3640 | |
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| 1.5694 | 7.8 | 17500 | 1.5645 | 0.3853 | 0.1731 | 0.2589 | 0.3636 | |
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| 1.5265 | 8.02 | 18000 | 1.5621 | 0.3871 | 0.1733 | 0.2596 | 0.3654 | |
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| 1.5273 | 8.25 | 18500 | 1.5624 | 0.3861 | 0.1726 | 0.2588 | 0.3646 | |
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| 1.5148 | 8.47 | 19000 | 1.5602 | 0.3866 | 0.1733 | 0.2592 | 0.3651 | |
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| 1.532 | 8.69 | 19500 | 1.5599 | 0.3859 | 0.1732 | 0.2593 | 0.3642 | |
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| 1.5113 | 8.92 | 20000 | 1.5602 | 0.3877 | 0.1748 | 0.2606 | 0.3658 | |
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| 1.5133 | 9.14 | 20500 | 1.5595 | 0.3855 | 0.1725 | 0.2587 | 0.3637 | |
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| 1.4875 | 9.36 | 21000 | 1.5572 | 0.3873 | 0.1741 | 0.2600 | 0.3654 | |
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| 1.5038 | 9.59 | 21500 | 1.5557 | 0.3860 | 0.1728 | 0.2590 | 0.3641 | |
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| 1.5062 | 9.81 | 22000 | 1.5544 | 0.3870 | 0.1736 | 0.2599 | 0.3653 | |
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### Framework versions |
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- Transformers 4.27.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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