---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: bert-small2bert-small-finetuned-cnn_daily_mail-summarization-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: 38.5318
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-small2bert-small-finetuned-cnn_daily_mail-summarization-finetuned-multi_news

This model is a fine-tuned version of [mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization](https://huggingface.co/mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3760
- Rouge1: 38.5318
- Rouge2: 12.7285
- Rougel: 21.4358
- Rougelsum: 33.4565
- Gen Len: 128.985

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1  | Rouge2  | Rougel  | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 4.6946        | 0.89  | 400  | 4.5393          | 37.164  | 11.5191 | 20.2519 | 32.1568   | 126.415 |
| 4.5128        | 1.78  | 800  | 4.4185          | 38.2345 | 12.2053 | 20.954  | 33.0667   | 128.975 |
| 4.2926        | 2.67  | 1200 | 4.3866          | 38.4475 | 12.6488 | 21.3046 | 33.2768   | 129.0   |
| 4.231         | 3.56  | 1600 | 4.3808          | 38.7008 | 12.6323 | 21.307  | 33.3693   | 128.955 |
| 4.125         | 4.44  | 2000 | 4.3760          | 38.5318 | 12.7285 | 21.4358 | 33.4565   | 128.985 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1