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t5-base-TEDxJP-0front-1body-1rear
This model is a fine-tuned version of sonoisa/t5-base-japanese on the te_dx_jp dataset. It achieves the following results on the evaluation set:
- Loss: 0.4869
- Wer: 0.1801
- Mer: 0.1739
- Wil: 0.2635
- Wip: 0.7365
- Hits: 55253
- Substitutions: 6626
- Deletions: 2708
- Insertions: 2296
- Cer: 0.1411
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.6609 | 1.0 | 1457 | 0.5121 | 0.2181 | 0.2049 | 0.2958 | 0.7042 | 54651 | 6867 | 3069 | 4151 | 0.1880 |
0.5633 | 2.0 | 2914 | 0.4719 | 0.1891 | 0.1817 | 0.2714 | 0.7286 | 55015 | 6654 | 2918 | 2644 | 0.1558 |
0.5212 | 3.0 | 4371 | 0.4626 | 0.1838 | 0.1771 | 0.2666 | 0.7334 | 55168 | 6635 | 2784 | 2452 | 0.1462 |
0.4498 | 4.0 | 5828 | 0.4616 | 0.1807 | 0.1747 | 0.2643 | 0.7357 | 55148 | 6630 | 2809 | 2231 | 0.1420 |
0.4058 | 5.0 | 7285 | 0.4633 | 0.1799 | 0.1739 | 0.2631 | 0.7369 | 55200 | 6592 | 2795 | 2231 | 0.1419 |
0.3802 | 6.0 | 8742 | 0.4675 | 0.1796 | 0.1733 | 0.2630 | 0.7370 | 55311 | 6636 | 2640 | 2321 | 0.1412 |
0.4126 | 7.0 | 10199 | 0.4737 | 0.1781 | 0.1724 | 0.2617 | 0.7383 | 55245 | 6595 | 2747 | 2163 | 0.1394 |
0.3436 | 8.0 | 11656 | 0.4772 | 0.1788 | 0.1729 | 0.2624 | 0.7376 | 55247 | 6616 | 2724 | 2208 | 0.1401 |
0.3249 | 9.0 | 13113 | 0.4827 | 0.1796 | 0.1735 | 0.2632 | 0.7368 | 55265 | 6635 | 2687 | 2281 | 0.1407 |
0.3347 | 10.0 | 14570 | 0.4869 | 0.1801 | 0.1739 | 0.2635 | 0.7365 | 55253 | 6626 | 2708 | 2296 | 0.1411 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1