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---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: bart-base-multi-news
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: multi_news
type: multi_news
config: default
split: validation
args: default
metrics:
- name: Rouge1
type: rouge
value: 26.31
- name: Rouge2
type: rouge
value: 9.6
- name: Rougel
type: rouge
value: 20.87
- name: Rougelsum
type: rouge
value: 21.54
language:
- en
---
<!-- 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-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 |