---
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