transformers pytorch tensorboard safetensors bart text2text-generation summarization generated_from_trainer en dataset:multi_news base_model:facebook/bart-base base_model:finetune:facebook/bart-base license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: facebook/bart-base
datasets:
- multi_news
language:
- en
license: apache-2.0
metrics:
- rouge
tags:
- summarization
- generated_from_trainer
model-index:
- name: bart-base-multi-news
  results:
  - task:
      type: text2text-generation
      name: Sequence-to-sequence Language Modeling
    dataset:
      name: multi_news
      type: multi_news
      config: default
      split: validation
      args: default
    metrics:
    - type: rouge
      value: 26.31
      name: Rouge1
    - type: rouge
      value: 9.6
      name: Rouge2
    - type: rouge
      value: 20.87
      name: Rougel
    - type: rouge
      value: 21.54
      name: Rougelsum
---

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# bart-base-multi-news

This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4147
- Rouge1: 26.31
- Rouge2: 9.6
- Rougel: 20.87
- Rougelsum: 21.54

## Intended uses & limitations

The inteded use of this model is text summarization.
The model requires additional training in order to perform better in the task of summarization.

## Training and evaluation data

The training data were 10000 samples from the multi-news training dataset
and the evaluation data were 500 samples from the multi-news evaluation dataset

## Training procedure

For the training procedure the Seq2SeqTrainer class was used from the transformers library.

### Training hyperparameters

The Hyperparameters were passed to the Seq2SeqTrainingArguments class from the transformers library.

The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.4041        | 1.0   | 1250 | 2.4147          | 26.31  | 9.6    | 20.87  | 21.54     |


### Framework versions

- Transformers 4.30.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3