---
license: apache-2.0
base_model: tsmatz/mt5_summarize_japanese
tags:
- generated_from_trainer
datasets:
- xlsum
metrics:
- rouge
model-index:
- name: mt5_summarize_japanese-6051-japanese
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: xlsum
      type: xlsum
      config: japanese
      split: validation
      args: japanese
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.3394
---

<!-- 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_summarize_japanese-6051-japanese

This model is a fine-tuned version of [tsmatz/mt5_summarize_japanese](https://huggingface.co/tsmatz/mt5_summarize_japanese) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1045
- Rouge1: 0.3394
- Rouge2: 0.0501
- Rougel: 0.3328
- Rougelsum: 0.3321
- Gen Len: 31.8808

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.3889        | 4.5   | 500  | 1.1112          | 0.3381 | 0.05   | 0.3306 | 0.3302    | 31.3127 |
| 1.3351        | 8.99  | 1000 | 1.1045          | 0.3394 | 0.0501 | 0.3328 | 0.3321    | 31.8808 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0