---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-base-cnn-xsum-swe
  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. -->

# bart-base-cnn-xsum-swe

This model is a fine-tuned version of [Gabriel/bart-base-cnn-swe](https://huggingface.co/Gabriel/bart-base-cnn-swe) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1895
- Rouge1: 31.1693
- Rouge2: 12.7388
- Rougel: 25.7655
- Rougelsum: 25.7862
- Gen Len: 19.7733

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 8
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 2.3079        | 1.0   | 6375  | 2.1998          | 29.7845 | 11.125  | 24.3181 | 24.3562   | 19.7119 |
| 2.064         | 2.0   | 12750 | 2.1245          | 30.4641 | 11.7383 | 25.0254 | 25.0633   | 19.653  |
| 1.8647        | 3.0   | 19125 | 2.1005          | 30.8903 | 12.2265 | 25.3996 | 25.4252   | 19.7457 |
| 1.7098        | 4.0   | 25500 | 2.1073          | 31.1173 | 12.4124 | 25.6553 | 25.6913   | 19.7546 |
| 1.5761        | 5.0   | 31875 | 2.1227          | 30.9586 | 12.4907 | 25.5474 | 25.5745   | 19.7675 |
| 1.4618        | 6.0   | 38250 | 2.1484          | 31.115  | 12.6546 | 25.684  | 25.7151   | 19.7456 |
| 1.3643        | 7.0   | 44625 | 2.1705          | 31.2225 | 12.8069 | 25.7901 | 25.8154   | 19.7842 |
| 1.2944        | 8.0   | 51000 | 2.1895          | 31.1693 | 12.7388 | 25.7655 | 25.7862   | 19.7733 |


### Framework versions

- Transformers 4.22.1
- Pytorch 1.12.1+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1