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t5-base-TEDxJP-6front-1body-0rear
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: 0.4613
- Wer: 0.1753
- Mer: 0.1692
- Wil: 0.2565
- Wip: 0.7435
- Hits: 55602
- Substitutions: 6443
- Deletions: 2542
- Insertions: 2338
- Cer: 0.1381
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.6523 | 1.0 | 1457 | 0.4912 | 0.2014 | 0.1921 | 0.2829 | 0.7171 | 54699 | 6782 | 3106 | 3118 | 0.1699 |
0.5429 | 2.0 | 2914 | 0.4424 | 0.1879 | 0.1801 | 0.2682 | 0.7318 | 55234 | 6529 | 2824 | 2782 | 0.1505 |
0.4888 | 3.0 | 4371 | 0.4371 | 0.1761 | 0.1699 | 0.2567 | 0.7433 | 55564 | 6391 | 2632 | 2353 | 0.1376 |
0.4287 | 4.0 | 5828 | 0.4328 | 0.1738 | 0.1680 | 0.2542 | 0.7458 | 55584 | 6332 | 2671 | 2223 | 0.1350 |
0.387 | 5.0 | 7285 | 0.4357 | 0.1747 | 0.1687 | 0.2555 | 0.7445 | 55627 | 6399 | 2561 | 2326 | 0.1360 |
0.3497 | 6.0 | 8742 | 0.4405 | 0.1750 | 0.1690 | 0.2558 | 0.7442 | 55587 | 6399 | 2601 | 2301 | 0.1370 |
0.3703 | 7.0 | 10199 | 0.4480 | 0.1748 | 0.1689 | 0.2560 | 0.7440 | 55554 | 6416 | 2617 | 2258 | 0.1363 |
0.3038 | 8.0 | 11656 | 0.4542 | 0.1746 | 0.1686 | 0.2554 | 0.7446 | 55638 | 6399 | 2550 | 2331 | 0.1369 |
0.3045 | 9.0 | 13113 | 0.4574 | 0.1752 | 0.1691 | 0.2564 | 0.7436 | 55596 | 6441 | 2550 | 2324 | 0.1381 |
0.3008 | 10.0 | 14570 | 0.4613 | 0.1753 | 0.1692 | 0.2565 | 0.7435 | 55602 | 6443 | 2542 | 2338 | 0.1381 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1