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t5-base-TEDxJP-0front-1body-5rear
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: 0.4695
- Wer: 0.1761
- Mer: 0.1701
- Wil: 0.2587
- Wip: 0.7413
- Hits: 55488
- Substitutions: 6549
- Deletions: 2550
- Insertions: 2272
- Cer: 0.1410
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.6479 | 1.0 | 1457 | 0.4975 | 0.1917 | 0.1845 | 0.2761 | 0.7239 | 54735 | 6812 | 3040 | 2530 | 0.1545 |
0.549 | 2.0 | 2914 | 0.4537 | 0.1833 | 0.1768 | 0.2659 | 0.7341 | 55124 | 6589 | 2874 | 2378 | 0.1435 |
0.4961 | 3.0 | 4371 | 0.4472 | 0.1758 | 0.1701 | 0.2582 | 0.7418 | 55387 | 6493 | 2707 | 2154 | 0.1369 |
0.432 | 4.0 | 5828 | 0.4439 | 0.1765 | 0.1707 | 0.2593 | 0.7407 | 55403 | 6544 | 2640 | 2217 | 0.1387 |
0.3789 | 5.0 | 7285 | 0.4471 | 0.1749 | 0.1693 | 0.2574 | 0.7426 | 55419 | 6490 | 2678 | 2128 | 0.1387 |
0.3524 | 6.0 | 8742 | 0.4483 | 0.1754 | 0.1697 | 0.2573 | 0.7427 | 55414 | 6449 | 2724 | 2153 | 0.1405 |
0.3961 | 7.0 | 10199 | 0.4562 | 0.1756 | 0.1698 | 0.2574 | 0.7426 | 55454 | 6455 | 2678 | 2206 | 0.1390 |
0.3238 | 8.0 | 11656 | 0.4593 | 0.1768 | 0.1708 | 0.2590 | 0.7410 | 55463 | 6514 | 2610 | 2298 | 0.1416 |
0.3054 | 9.0 | 13113 | 0.4652 | 0.1756 | 0.1697 | 0.2577 | 0.7423 | 55522 | 6498 | 2567 | 2279 | 0.1408 |
0.3087 | 10.0 | 14570 | 0.4695 | 0.1761 | 0.1701 | 0.2587 | 0.7413 | 55488 | 6549 | 2550 | 2272 | 0.1410 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1