---
base_model: google/pegasus-large
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
model-index:
- name: pegasus-samsum
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: samsum
      type: samsum
      config: samsum
      split: validation
      args: samsum
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.4659
---

<!-- 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-large](https://huggingface.co/google/pegasus-large) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4091
- Rouge1: 0.4659
- Rouge2: 0.2345
- Rougel: 0.3946
- Rougelsum: 0.3951
- Gen Len: 17.7467

## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.8025        | 0.27  | 500  | 1.4403          | 0.4466 | 0.2101 | 0.3832 | 0.3841    | 21.64   |
| 1.5936        | 0.54  | 1000 | 1.3766          | 0.4786 | 0.2374 | 0.4017 | 0.4013    | 21.24   |
| 1.5926        | 0.81  | 1500 | 1.3910          | 0.5118 | 0.2643 | 0.4282 | 0.4286    | 20.2267 |
| 1.5067        | 1.09  | 2000 | 1.4028          | 0.4982 | 0.261  | 0.4155 | 0.4157    | 20.4267 |
| 1.5712        | 1.36  | 2500 | 1.4236          | 0.4712 | 0.234  | 0.3964 | 0.3969    | 17.0    |
| 1.6177        | 1.63  | 3000 | 1.4151          | 0.4768 | 0.2382 | 0.4019 | 0.4022    | 16.28   |
| 1.6289        | 1.9   | 3500 | 1.4112          | 0.4744 | 0.2346 | 0.402  | 0.4033    | 17.0267 |
| 1.6326        | 2.17  | 4000 | 1.4096          | 0.4682 | 0.234  | 0.3985 | 0.3994    | 17.1333 |
| 1.5929        | 2.44  | 4500 | 1.4093          | 0.4637 | 0.2342 | 0.3939 | 0.3942    | 17.16   |
| 1.4351        | 2.72  | 5000 | 1.4090          | 0.4684 | 0.2346 | 0.3953 | 0.3955    | 17.8133 |
| 1.6445        | 2.99  | 5500 | 1.4091          | 0.4659 | 0.2345 | 0.3946 | 0.3951    | 17.7467 |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3