---
license: cc-by-sa-4.0
base_model: retrieva-jp/t5-base-long
tags:
- generated_from_trainer
- summarization
datasets:
- csebuetnlp/xlsum
metrics:
- rouge
model-index:
- name: t5-base-xlsum-ja
  results:
  - task:
      name: Sequence-to-sequence Language Modeling
      type: text2text-generation
    dataset:
      name: csebuetnlp/xlsum
      type: xlsum
      config: japanese
      split: test
      args: japanese
    metrics:
    - name: Rouge1
      type: rouge
      value: 0.3648008957585529
    - name: Rouge2
      type: rouge
      value: 0.16411161798042992
language:
- ja
library_name: transformers
---

<!-- 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. -->

# t5-base-xlsum-ja

This model is a fine-tuned version of [retrieva-jp/t5-base-long](https://huggingface.co/retrieva-jp/t5-base-long) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6563
- Rouge1: 0.3648
- Rouge2: 0.1641
- Rougel: 0.2965
- Rougelsum: 0.3132

## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.9166        | 1.8   | 100  | 3.4095          | 0.3569 | 0.1509 | 0.2416 | 0.3209    |
| 4.1162        | 3.61  | 200  | 3.0980          | 0.3262 | 0.1354 | 0.2557 | 0.2805    |
| 3.8578        | 5.41  | 300  | 2.8853          | 0.3428 | 0.1445 | 0.2628 | 0.2881    |
| 3.7309        | 7.22  | 400  | 2.7714          | 0.3621 | 0.1615 | 0.2951 | 0.3151    |
| 3.6716        | 9.02  | 500  | 2.7042          | 0.3727 | 0.1668 | 0.2982 | 0.3225    |
| 3.6393        | 10.82 | 600  | 2.6666          | 0.3676 | 0.1592 | 0.2987 | 0.3206    |
| 3.6291        | 12.63 | 700  | 2.6587          | 0.3654 | 0.1576 | 0.2955 | 0.3108    |
| 3.6224        | 14.43 | 800  | 2.6563          | 0.3648 | 0.1641 | 0.2965 | 0.3132    |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0