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t5-base-TEDxJP-5front-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: 0.4633
- Wer: 0.1756
- Mer: 0.1693
- Wil: 0.2562
- Wip: 0.7438
- Hits: 55657
- Substitutions: 6415
- Deletions: 2515
- Insertions: 2414
- Cer: 0.1382
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.6441 | 1.0 | 1457 | 0.4872 | 0.2061 | 0.1954 | 0.2850 | 0.7150 | 54813 | 6709 | 3065 | 3540 | 0.1823 |
0.543 | 2.0 | 2914 | 0.4422 | 0.1832 | 0.1765 | 0.2641 | 0.7359 | 55188 | 6458 | 2941 | 2432 | 0.1491 |
0.4896 | 3.0 | 4371 | 0.4373 | 0.1811 | 0.1739 | 0.2612 | 0.7388 | 55568 | 6464 | 2555 | 2679 | 0.1450 |
0.4299 | 4.0 | 5828 | 0.4326 | 0.1745 | 0.1685 | 0.2553 | 0.7447 | 55604 | 6391 | 2592 | 2288 | 0.1367 |
0.3853 | 5.0 | 7285 | 0.4390 | 0.1758 | 0.1693 | 0.2561 | 0.7439 | 55696 | 6406 | 2485 | 2462 | 0.1375 |
0.357 | 6.0 | 8742 | 0.4433 | 0.1835 | 0.1757 | 0.2619 | 0.7381 | 55609 | 6386 | 2592 | 2871 | 0.1438 |
0.3735 | 7.0 | 10199 | 0.4479 | 0.1799 | 0.1729 | 0.2598 | 0.7402 | 55582 | 6425 | 2580 | 2617 | 0.1411 |
0.302 | 8.0 | 11656 | 0.4554 | 0.1770 | 0.1702 | 0.2569 | 0.7431 | 55725 | 6408 | 2454 | 2568 | 0.1386 |
0.2992 | 9.0 | 13113 | 0.4614 | 0.1784 | 0.1715 | 0.2581 | 0.7419 | 55672 | 6405 | 2510 | 2606 | 0.1404 |
0.2972 | 10.0 | 14570 | 0.4633 | 0.1756 | 0.1693 | 0.2562 | 0.7438 | 55657 | 6415 | 2515 | 2414 | 0.1382 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1