---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: led-base-16384-finetune-cnn
  results: []
---

<!-- 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-base-16384-finetune-cnn

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: 3.2020
- Rouge1: 24.2258
- Rouge2: 9.0151
- Rougel: 19.0336
- Rougelsum: 22.2604
- Gen Len: 20.0

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 1.8988        | 1.0   | 2000  | 2.0031          | 25.1709 | 10.0426 | 20.1311 | 23.1639   | 20.0    |
| 1.6038        | 2.0   | 4000  | 2.0314          | 25.0213 | 9.8701  | 19.8987 | 23.0129   | 20.0    |
| 1.3352        | 3.0   | 6000  | 2.1124          | 24.99   | 9.905   | 19.9566 | 23.0973   | 20.0    |
| 1.1173        | 4.0   | 8000  | 2.2055          | 25.0568 | 10.0949 | 19.9602 | 23.18     | 20.0    |
| 0.9566        | 5.0   | 10000 | 2.3262          | 24.941  | 9.5856  | 19.6285 | 23.042    | 20.0    |
| 0.7986        | 6.0   | 12000 | 2.4489          | 24.4114 | 9.2808  | 19.3296 | 22.5481   | 20.0    |
| 0.6685        | 7.0   | 14000 | 2.5211          | 24.467  | 9.5124  | 19.2685 | 22.5624   | 20.0    |
| 0.5601        | 8.0   | 16000 | 2.6299          | 24.6939 | 9.6533  | 19.4627 | 22.8048   | 20.0    |
| 0.4757        | 9.0   | 18000 | 2.7185          | 24.2098 | 9.1232  | 19.0181 | 22.4085   | 20.0    |
| 0.3926        | 10.0  | 20000 | 2.7947          | 24.5092 | 9.3964  | 19.2593 | 22.5592   | 20.0    |
| 0.3391        | 11.0  | 22000 | 2.8626          | 24.4731 | 9.3634  | 19.2966 | 22.5688   | 20.0    |
| 0.2872        | 12.0  | 24000 | 2.9175          | 24.5587 | 9.3888  | 19.3335 | 22.6443   | 20.0    |
| 0.2479        | 13.0  | 26000 | 2.9658          | 24.2983 | 9.1038  | 19.019  | 22.3675   | 20.0    |
| 0.213         | 14.0  | 28000 | 3.0273          | 24.4196 | 9.1481  | 19.0458 | 22.5135   | 20.0    |
| 0.1828        | 15.0  | 30000 | 3.0751          | 24.3283 | 9.2334  | 18.9771 | 22.3322   | 20.0    |
| 0.1608        | 16.0  | 32000 | 3.1185          | 24.3965 | 9.2047  | 19.0899 | 22.4666   | 20.0    |
| 0.1442        | 17.0  | 34000 | 3.1494          | 24.3832 | 9.1915  | 19.077  | 22.4366   | 20.0    |
| 0.1293        | 18.0  | 36000 | 3.1738          | 24.3796 | 9.1132  | 19.1015 | 22.3862   | 20.0    |
| 0.1165        | 19.0  | 38000 | 3.2073          | 24.2804 | 9.1018  | 19.0692 | 22.3023   | 20.0    |
| 0.1118        | 20.0  | 40000 | 3.2020          | 24.2258 | 9.0151  | 19.0336 | 22.2604   | 20.0    |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3