metadata

language:
  - en
license: apache-2.0
tags:
  - summarization
  - generated_from_trainer
datasets:
  - multi_news
metrics:
  - rouge
base_model: google/mt5-small
model-index:
  - name: mt5-small-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: 22.03
            name: Rouge1
          - type: rouge
            value: 6.95
            name: Rouge2
          - type: rouge
            value: 18.41
            name: Rougel
          - type: rouge
            value: 18.72
            name: Rougelsum

mt5-small-multi-news

This model is a fine-tuned version of google/mt5-small on the multi_news dataset. It achieves the following results on the evaluation set:

Loss: 3.2170
Rouge1: 22.03
Rouge2: 6.95
Rougel: 18.41
Rougelsum: 18.72

Intended uses & limitations

Text summarization is the inteded use of this model. With further training the model could achieve better results.

Training and evaluation data

For the training data we used 10000 samples from the multi-news train dataset. For the evaluation data we used 500 samples from the multi-news evaluation dataset.

Training procedure

Training hyperparameters

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
5.2732	1.0	1250	3.2170	22.03	6.95	18.41	18.72

Framework versions

Transformers 4.28.0
Pytorch 2.0.0+cu117
Datasets 2.12.0
Tokenizers 0.13.3