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t5-base-TEDxJP-9front-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: 0.4576
- Wer: 0.1728
- Mer: 0.1669
- Wil: 0.2543
- Wip: 0.7457
- Hits: 55705
- Substitutions: 6444
- Deletions: 2438
- Insertions: 2281
- Cer: 0.1351
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.649 | 1.0 | 1457 | 0.4844 | 0.2290 | 0.2126 | 0.3015 | 0.6985 | 54758 | 6748 | 3081 | 4959 | 0.2080 |
0.5319 | 2.0 | 2914 | 0.4385 | 0.1804 | 0.1741 | 0.2614 | 0.7386 | 55298 | 6437 | 2852 | 2364 | 0.1465 |
0.4819 | 3.0 | 4371 | 0.4338 | 0.1760 | 0.1698 | 0.2569 | 0.7431 | 55558 | 6419 | 2610 | 2336 | 0.1389 |
0.4307 | 4.0 | 5828 | 0.4328 | 0.1759 | 0.1696 | 0.2569 | 0.7431 | 55649 | 6454 | 2484 | 2424 | 0.1390 |
0.3735 | 5.0 | 7285 | 0.4331 | 0.1740 | 0.1680 | 0.2549 | 0.7451 | 55652 | 6398 | 2537 | 2306 | 0.1367 |
0.3495 | 6.0 | 8742 | 0.4380 | 0.1740 | 0.1681 | 0.2552 | 0.7448 | 55619 | 6420 | 2548 | 2267 | 0.1356 |
0.3679 | 7.0 | 10199 | 0.4437 | 0.1741 | 0.1682 | 0.2556 | 0.7444 | 55621 | 6441 | 2525 | 2281 | 0.1354 |
0.3035 | 8.0 | 11656 | 0.4494 | 0.1727 | 0.1669 | 0.2542 | 0.7458 | 55672 | 6433 | 2482 | 2237 | 0.1350 |
0.3041 | 9.0 | 13113 | 0.4541 | 0.1736 | 0.1677 | 0.2550 | 0.7450 | 55674 | 6441 | 2472 | 2302 | 0.1383 |
0.2948 | 10.0 | 14570 | 0.4576 | 0.1728 | 0.1669 | 0.2543 | 0.7457 | 55705 | 6444 | 2438 | 2281 | 0.1351 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1