---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
  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. -->

# pegasus-samsum

This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4251

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1284        | 0.01  | 10   | 2.5960          |
| 3.122         | 0.02  | 20   | 2.5579          |
| 3.0196        | 0.03  | 30   | 2.4983          |
| 2.9803        | 0.04  | 40   | 2.4197          |
| 2.8471        | 0.05  | 50   | 2.3258          |
| 2.7692        | 0.07  | 60   | 2.2438          |
| 2.682         | 0.08  | 70   | 2.1608          |
| 2.3648        | 0.09  | 80   | 2.0838          |
| 2.5696        | 0.1   | 90   | 2.0222          |
| 2.3403        | 0.11  | 100  | 1.9713          |
| 2.2036        | 0.12  | 110  | 1.9199          |
| 2.1998        | 0.13  | 120  | 1.8750          |
| 2.3006        | 0.14  | 130  | 1.8382          |
| 2.1182        | 0.15  | 140  | 1.8050          |
| 2.1493        | 0.16  | 150  | 1.7748          |
| 2.0437        | 0.17  | 160  | 1.7494          |
| 1.9236        | 0.18  | 170  | 1.7289          |
| 2.0114        | 0.2   | 180  | 1.7106          |
| 1.9939        | 0.21  | 190  | 1.6906          |
| 1.928         | 0.22  | 200  | 1.6737          |
| 1.9444        | 0.23  | 210  | 1.6603          |
| 1.9071        | 0.24  | 220  | 1.6485          |
| 1.8314        | 0.25  | 230  | 1.6369          |
| 1.8085        | 0.26  | 240  | 1.6277          |
| 1.7493        | 0.27  | 250  | 1.6203          |
| 1.8539        | 0.28  | 260  | 1.6089          |
| 1.7048        | 0.29  | 270  | 1.5999          |
| 1.7486        | 0.3   | 280  | 1.5921          |
| 1.795         | 0.31  | 290  | 1.5842          |
| 1.6613        | 0.33  | 300  | 1.5815          |
| 1.8163        | 0.34  | 310  | 1.5732          |
| 1.6133        | 0.35  | 320  | 1.5621          |
| 1.8           | 0.36  | 330  | 1.5542          |
| 1.7159        | 0.37  | 340  | 1.5506          |
| 1.8081        | 0.38  | 350  | 1.5483          |
| 1.7365        | 0.39  | 360  | 1.5451          |
| 1.7334        | 0.4   | 370  | 1.5405          |
| 1.7329        | 0.41  | 380  | 1.5334          |
| 1.6923        | 0.42  | 390  | 1.5259          |
| 1.6868        | 0.43  | 400  | 1.5227          |
| 1.7033        | 0.45  | 410  | 1.5163          |
| 1.6805        | 0.46  | 420  | 1.5144          |
| 1.6056        | 0.47  | 430  | 1.5126          |
| 1.7317        | 0.48  | 440  | 1.5086          |
| 1.6303        | 0.49  | 450  | 1.5015          |
| 1.7136        | 0.5   | 460  | 1.4943          |
| 1.534         | 0.51  | 470  | 1.4910          |
| 1.6682        | 0.52  | 480  | 1.4917          |
| 1.6234        | 0.53  | 490  | 1.4885          |
| 1.7103        | 0.54  | 500  | 1.4857          |
| 1.7673        | 0.55  | 510  | 1.4800          |
| 1.6631        | 0.56  | 520  | 1.4776          |
| 1.7073        | 0.58  | 530  | 1.4745          |
| 1.6843        | 0.59  | 540  | 1.4698          |
| 1.6849        | 0.6   | 550  | 1.4679          |
| 1.6054        | 0.61  | 560  | 1.4642          |
| 1.6073        | 0.62  | 570  | 1.4629          |
| 1.5896        | 0.63  | 580  | 1.4591          |
| 1.608         | 0.64  | 590  | 1.4580          |
| 1.58          | 0.65  | 600  | 1.4548          |
| 1.5722        | 0.66  | 610  | 1.4548          |
| 1.5529        | 0.67  | 620  | 1.4542          |
| 1.5948        | 0.68  | 630  | 1.4518          |
| 1.5869        | 0.7   | 640  | 1.4489          |
| 1.577         | 0.71  | 650  | 1.4488          |
| 1.6517        | 0.72  | 660  | 1.4477          |
| 1.5955        | 0.73  | 670  | 1.4436          |
| 1.5678        | 0.74  | 680  | 1.4402          |
| 1.6743        | 0.75  | 690  | 1.4384          |
| 1.5791        | 0.76  | 700  | 1.4374          |
| 1.6397        | 0.77  | 710  | 1.4380          |
| 1.5637        | 0.78  | 720  | 1.4363          |
| 1.5849        | 0.79  | 730  | 1.4356          |
| 1.5815        | 0.8   | 740  | 1.4350          |
| 1.5797        | 0.81  | 750  | 1.4362          |
| 1.5551        | 0.83  | 760  | 1.4354          |
| 1.5486        | 0.84  | 770  | 1.4341          |
| 1.5756        | 0.85  | 780  | 1.4320          |
| 1.5326        | 0.86  | 790  | 1.4300          |
| 1.6198        | 0.87  | 800  | 1.4290          |
| 1.5947        | 0.88  | 810  | 1.4288          |
| 1.6326        | 0.89  | 820  | 1.4291          |
| 1.6231        | 0.9   | 830  | 1.4288          |
| 1.597         | 0.91  | 840  | 1.4281          |
| 1.5781        | 0.92  | 850  | 1.4273          |
| 1.6835        | 0.93  | 860  | 1.4260          |
| 1.5373        | 0.94  | 870  | 1.4257          |
| 1.5458        | 0.96  | 880  | 1.4252          |
| 1.4953        | 0.97  | 890  | 1.4252          |
| 1.5299        | 0.98  | 900  | 1.4252          |
| 1.5853        | 0.99  | 910  | 1.4251          |
| 1.5723        | 1.0   | 920  | 1.4251          |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 1.18.4
- Tokenizers 0.12.1