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---
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: 0.3869876274946419
---
<!-- 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. -->
# led-large-16384-cnn_dailymail
This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5544
- Rouge1: 0.3870
- Rouge2: 0.1736
- Rougel: 0.2599
- Rougelsum: 0.3653
## 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: 10
- mixed_precision_training: Native AMP
### 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 |
| 1.6306 | 6.02 | 13500 | 1.6004 | 0.3831 | 0.1702 | 0.2563 | 0.3618 |
| 1.6425 | 6.24 | 14000 | 1.5987 | 0.3821 | 0.1698 | 0.2561 | 0.3610 |
| 1.6863 | 6.46 | 14500 | 1.5876 | 0.3837 | 0.1710 | 0.2569 | 0.3622 |
| 1.6085 | 6.69 | 15000 | 1.5815 | 0.3836 | 0.1717 | 0.2573 | 0.3621 |
| 1.6267 | 6.91 | 15500 | 1.5792 | 0.3852 | 0.1722 | 0.2579 | 0.3633 |
| 1.5637 | 7.13 | 16000 | 1.5768 | 0.3830 | 0.1709 | 0.2568 | 0.3611 |
| 1.5586 | 7.36 | 16500 | 1.5740 | 0.3833 | 0.1706 | 0.2567 | 0.3617 |
| 1.5389 | 7.58 | 17000 | 1.5689 | 0.3858 | 0.1729 | 0.2590 | 0.3640 |
| 1.5694 | 7.8 | 17500 | 1.5645 | 0.3853 | 0.1731 | 0.2589 | 0.3636 |
| 1.5265 | 8.02 | 18000 | 1.5621 | 0.3871 | 0.1733 | 0.2596 | 0.3654 |
| 1.5273 | 8.25 | 18500 | 1.5624 | 0.3861 | 0.1726 | 0.2588 | 0.3646 |
| 1.5148 | 8.47 | 19000 | 1.5602 | 0.3866 | 0.1733 | 0.2592 | 0.3651 |
| 1.532 | 8.69 | 19500 | 1.5599 | 0.3859 | 0.1732 | 0.2593 | 0.3642 |
| 1.5113 | 8.92 | 20000 | 1.5602 | 0.3877 | 0.1748 | 0.2606 | 0.3658 |
| 1.5133 | 9.14 | 20500 | 1.5595 | 0.3855 | 0.1725 | 0.2587 | 0.3637 |
| 1.4875 | 9.36 | 21000 | 1.5572 | 0.3873 | 0.1741 | 0.2600 | 0.3654 |
| 1.5038 | 9.59 | 21500 | 1.5557 | 0.3860 | 0.1728 | 0.2590 | 0.3641 |
| 1.5062 | 9.81 | 22000 | 1.5544 | 0.3870 | 0.1736 | 0.2599 | 0.3653 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.0.0+cu118
- Datasets 2.10.1
- Tokenizers 0.13.2
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