---
base_model: google/pegasus-cnn_dailymail
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: 0.4616
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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: 1.3665
- Rouge1: 0.4616
- Rouge2: 0.2275
- Rougel: 0.3725
- Rougelsum: 0.3738
- Gen Len: 35.6667

## 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: 5.750420024069848e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.2186        | 0.87  | 100  | 1.7567          | 0.3571 | 0.1437 | 0.2779 | 0.2797    | 46.7733 |
| 1.7368        | 1.74  | 200  | 1.4933          | 0.4347 | 0.2053 | 0.3459 | 0.3461    | 35.4533 |
| 1.6744        | 2.61  | 300  | 1.4059          | 0.4547 | 0.2179 | 0.3629 | 0.3634    | 35.68   |
| 1.5978        | 3.47  | 400  | 1.3665          | 0.4616 | 0.2275 | 0.3725 | 0.3738    | 35.6667 |


### Framework versions

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