metadata
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: 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