---
base_model: google/pegasus-xsum
tags:
- generated_from_trainer
datasets:
- samsum
metrics:
- rouge
- precision
- recall
- f1
model-index:
- name: Pegasus_xsum_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: 0.5072
    - name: Precision
      type: precision
      value: 0.9247
    - name: Recall
      type: recall
      value: 0.9099
    - name: F1
      type: f1
      value: 0.917
---

<!-- 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_xsum_samsum

This model is a fine-tuned version of [google/pegasus-xsum](https://huggingface.co/google/pegasus-xsum) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4709
- Rouge1: 0.5072
- Rouge2: 0.2631
- Rougel: 0.4243
- Rougelsum: 0.4244
- Gen Len: 19.1479
- Precision: 0.9247
- Recall: 0.9099
- F1: 0.917

## 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: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:---------:|:------:|:------:|
| 1.9542        | 1.0   | 920  | 1.5350          | 0.4928 | 0.2436 | 0.4085 | 0.4086    | 18.5672 | 0.9229    | 0.9074 | 0.9149 |
| 1.6331        | 2.0   | 1841 | 1.4914          | 0.5037 | 0.257  | 0.4202 | 0.4206    | 18.8154 | 0.9246    | 0.9092 | 0.9166 |
| 1.5694        | 3.0   | 2762 | 1.4761          | 0.5071 | 0.259  | 0.4212 | 0.4214    | 19.4487 | 0.9241    | 0.9103 | 0.917  |
| 1.5374        | 4.0   | 3680 | 1.4709          | 0.5072 | 0.2631 | 0.4243 | 0.4244    | 19.1479 | 0.9247    | 0.9099 | 0.917  |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0