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t5-base-TEDxJP-4front-1body-4rear
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: 0.4406
- Wer: 0.1708
- Mer: 0.1650
- Wil: 0.2510
- Wip: 0.7490
- Hits: 55830
- Substitutions: 6334
- Deletions: 2423
- Insertions: 2273
- Cer: 0.1334
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.6266 | 1.0 | 1457 | 0.4764 | 0.2167 | 0.2030 | 0.2919 | 0.7081 | 54977 | 6723 | 2887 | 4389 | 0.1978 |
0.512 | 2.0 | 2914 | 0.4281 | 0.1787 | 0.1727 | 0.2600 | 0.7400 | 55299 | 6432 | 2856 | 2253 | 0.1408 |
0.4636 | 3.0 | 4371 | 0.4205 | 0.1775 | 0.1708 | 0.2582 | 0.7418 | 55665 | 6466 | 2456 | 2540 | 0.1383 |
0.4055 | 4.0 | 5828 | 0.4158 | 0.1721 | 0.1663 | 0.2529 | 0.7471 | 55724 | 6376 | 2487 | 2250 | 0.1344 |
0.356 | 5.0 | 7285 | 0.4195 | 0.1711 | 0.1654 | 0.2520 | 0.7480 | 55769 | 6376 | 2442 | 2235 | 0.1338 |
0.3273 | 6.0 | 8742 | 0.4228 | 0.1700 | 0.1644 | 0.2506 | 0.7494 | 55792 | 6333 | 2462 | 2183 | 0.1330 |
0.3586 | 7.0 | 10199 | 0.4288 | 0.1702 | 0.1645 | 0.2506 | 0.7494 | 55814 | 6331 | 2442 | 2219 | 0.1326 |
0.2836 | 8.0 | 11656 | 0.4339 | 0.1710 | 0.1651 | 0.2515 | 0.7485 | 55833 | 6359 | 2395 | 2290 | 0.1334 |
0.285 | 9.0 | 13113 | 0.4370 | 0.1708 | 0.1649 | 0.2509 | 0.7491 | 55854 | 6330 | 2403 | 2297 | 0.1333 |
0.285 | 10.0 | 14570 | 0.4406 | 0.1708 | 0.1650 | 0.2510 | 0.7490 | 55830 | 6334 | 2423 | 2273 | 0.1334 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1