---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: finetuned_multi_news_bart_text_summarisation
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: textsummarization
    dataset:
      name: multi_news
      type: multi_news
      config: default
      split: test
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.4038
pipeline_tag: summarization
---

<!-- 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. -->

# finetuned_multi_news_bart_text_summarisation

This model is a fine-tuned version of [slauw87/bart_summarisation](https://huggingface.co/slauw87/bart_summarisation) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8952
- Rouge1: 0.4038
- Rouge2: 0.1389
- Rougel: 0.2155
- Rougelsum: 0.2147
- Gen Len: 138.7667

## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len  |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| No log        | 1.0   | 15   | 2.9651          | 0.3903 | 0.134  | 0.21   | 0.2098    | 137.6    |
| No log        | 2.0   | 30   | 2.8952          | 0.4038 | 0.1389 | 0.2155 | 0.2147    | 138.7667 |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3