t5-base-xlsum-ja / README.md
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metadata
license: cc-by-sa-4.0
base_model: retrieva-jp/t5-base-long
tags:
  - generated_from_trainer
  - summarization
datasets:
  - 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: XL-Sum
          config: japanese
          split: train
          args: japanese
        metrics:
          - name: Rouge1
            type: rouge
            value: 0.3648008957585529
          - name: Rouge2
            type: rouge
            value: 0.16411161798042992
language:
  - ja
library_name: transformers

t5-base-xlsum-ja

This model is a fine-tuned version of 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