pytorch tensorboard bart text2text-generation dataset:multi_news transformers generated_from_trainer license:mit model-index autotrain_compatible
---
license: mit
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-multi-news
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: multi_news
      type: multi_news
      args: default
    metrics:
    - name: Rouge1
      type: rouge
      value: 42.0423
---

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# bart-large-cnn-finetuned-multi-news

This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0950
- Rouge1: 42.0423
- Rouge2: 14.8812
- Rougel: 23.3412
- Rougelsum: 36.2613

## Model description

bart-large-cnn fine tuned on sample of multi-news dataset

## Intended uses & limitations

The intended use of the model is for downstream summarization tasks but it's limited to input text 1024 words. Any text longer than that would be truncated.

## 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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.2037        | 1.0   | 750  | 2.0950          | 42.0423 | 14.8812 | 23.3412 | 36.2613   |


### Framework versions

- Transformers 4.18.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
