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---
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
---
<!-- 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. -->
# mt5-small-multi-news
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/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 |