| | --- |
| | license: apache-2.0 |
| | base_model: google/mt5-small |
| | tags: |
| | - generated_from_trainer |
| | metrics: |
| | - rouge |
| | model-index: |
| | - name: mt5-summarize-ja |
| | results: [] |
| | --- |
| | |
| | <!-- 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-ja |
| |
|
| | This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 2.0695 |
| | - Rouge1: 0.3667 |
| | - Rouge2: 0.1678 |
| | - Rougel: 0.2998 |
| | - Rougelsum: 0.3123 |
| |
|
| | ## 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: 0.0005 |
| | - train_batch_size: 2 |
| | - eval_batch_size: 1 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 16 |
| | - total_train_batch_size: 32 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: linear |
| | - lr_scheduler_warmup_steps: 90 |
| | - num_epochs: 10 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
| | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
| | | 3.3241 | 0.7 | 100 | 2.4795 | 0.2943 | 0.1245 | 0.2472 | 0.2471 | |
| | | 2.7583 | 1.4 | 200 | 2.2710 | 0.3054 | 0.1152 | 0.2539 | 0.2576 | |
| | | 2.5469 | 2.1 | 300 | 2.2936 | 0.3446 | 0.1493 | 0.2808 | 0.2887 | |
| | | 2.5335 | 2.8 | 400 | 2.1913 | 0.3228 | 0.1270 | 0.2665 | 0.2725 | |
| | | 2.4383 | 3.5 | 500 | 2.1507 | 0.3630 | 0.1671 | 0.3082 | 0.3144 | |
| | | 2.3671 | 4.2 | 600 | 2.1338 | 0.3388 | 0.1493 | 0.2814 | 0.2880 | |
| | | 2.349 | 4.9 | 700 | 2.1089 | 0.3621 | 0.1576 | 0.2980 | 0.3079 | |
| | | 2.264 | 5.6 | 800 | 2.1353 | 0.3740 | 0.1784 | 0.3083 | 0.3157 | |
| | | 2.1577 | 6.3 | 900 | 2.1101 | 0.3711 | 0.1716 | 0.3107 | 0.3166 | |
| | | 2.1315 | 7.0 | 1000 | 2.0905 | 0.3862 | 0.1826 | 0.3198 | 0.3269 | |
| | | 2.1418 | 7.7 | 1100 | 2.0893 | 0.3433 | 0.1621 | 0.2895 | 0.2963 | |
| | | 2.0744 | 8.4 | 1200 | 2.0881 | 0.3778 | 0.1834 | 0.3130 | 0.3242 | |
| | | 2.0944 | 9.1 | 1300 | 2.0709 | 0.3676 | 0.1688 | 0.3024 | 0.3140 | |
| | | 2.1015 | 9.8 | 1400 | 2.0695 | 0.3667 | 0.1678 | 0.2998 | 0.3123 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.35.2 |
| | - Pytorch 2.1.0+cu121 |
| | - Datasets 2.17.0 |
| | - Tokenizers 0.15.2 |
| |
|
