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t5-base-TEDxJP-10front-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: 0.4586
- Wer: 0.1729
- Mer: 0.1671
- Wil: 0.2545
- Wip: 0.7455
- Hits: 55669
- Substitutions: 6448
- Deletions: 2470
- Insertions: 2249
- Cer: 0.1350
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.6477 | 1.0 | 1457 | 0.4829 | 0.2234 | 0.2082 | 0.2973 | 0.7027 | 54891 | 6766 | 2930 | 4734 | 0.2060 |
0.5306 | 2.0 | 2914 | 0.4366 | 0.1808 | 0.1743 | 0.2615 | 0.7385 | 55312 | 6431 | 2844 | 2402 | 0.1439 |
0.4743 | 3.0 | 4371 | 0.4311 | 0.1827 | 0.1752 | 0.2623 | 0.7377 | 55558 | 6456 | 2573 | 2771 | 0.1483 |
0.4299 | 4.0 | 5828 | 0.4286 | 0.1778 | 0.1711 | 0.2580 | 0.7420 | 55641 | 6422 | 2524 | 2540 | 0.1419 |
0.3815 | 5.0 | 7285 | 0.4321 | 0.1741 | 0.1680 | 0.2554 | 0.7446 | 55673 | 6448 | 2466 | 2330 | 0.1379 |
0.3508 | 6.0 | 8742 | 0.4392 | 0.1737 | 0.1677 | 0.2547 | 0.7453 | 55683 | 6417 | 2487 | 2312 | 0.1373 |
0.3594 | 7.0 | 10199 | 0.4477 | 0.1726 | 0.1666 | 0.2528 | 0.7472 | 55757 | 6344 | 2486 | 2319 | 0.1349 |
0.2975 | 8.0 | 11656 | 0.4509 | 0.1726 | 0.1668 | 0.2537 | 0.7463 | 55691 | 6401 | 2495 | 2251 | 0.1349 |
0.2947 | 9.0 | 13113 | 0.4550 | 0.1725 | 0.1667 | 0.2539 | 0.7461 | 55700 | 6426 | 2461 | 2257 | 0.1347 |
0.2892 | 10.0 | 14570 | 0.4586 | 0.1729 | 0.1671 | 0.2545 | 0.7455 | 55669 | 6448 | 2470 | 2249 | 0.1350 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1