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t5-base-TEDxJP-3front-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: 0.4641
- Wer: 0.1743
- Mer: 0.1684
- Wil: 0.2557
- Wip: 0.7443
- Hits: 55594
- Substitutions: 6428
- Deletions: 2565
- Insertions: 2267
- Cer: 0.1368
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.6567 | 1.0 | 1457 | 0.4959 | 0.2072 | 0.1966 | 0.2877 | 0.7123 | 54688 | 6836 | 3063 | 3486 | 0.1936 |
0.5486 | 2.0 | 2914 | 0.4504 | 0.1870 | 0.1796 | 0.2677 | 0.7323 | 55158 | 6518 | 2911 | 2647 | 0.1528 |
0.4957 | 3.0 | 4371 | 0.4410 | 0.1764 | 0.1705 | 0.2578 | 0.7422 | 55412 | 6429 | 2746 | 2216 | 0.1375 |
0.4371 | 4.0 | 5828 | 0.4379 | 0.1761 | 0.1702 | 0.2572 | 0.7428 | 55447 | 6407 | 2733 | 2232 | 0.1377 |
0.387 | 5.0 | 7285 | 0.4408 | 0.1756 | 0.1696 | 0.2562 | 0.7438 | 55510 | 6372 | 2705 | 2263 | 0.1399 |
0.3589 | 6.0 | 8742 | 0.4466 | 0.1737 | 0.1681 | 0.2552 | 0.7448 | 55532 | 6406 | 2649 | 2165 | 0.1359 |
0.3876 | 7.0 | 10199 | 0.4532 | 0.1746 | 0.1689 | 0.2563 | 0.7437 | 55491 | 6436 | 2660 | 2179 | 0.1363 |
0.3199 | 8.0 | 11656 | 0.4591 | 0.1738 | 0.1681 | 0.2554 | 0.7446 | 55568 | 6431 | 2588 | 2208 | 0.1362 |
0.3079 | 9.0 | 13113 | 0.4625 | 0.1743 | 0.1685 | 0.2557 | 0.7443 | 55579 | 6425 | 2583 | 2252 | 0.1366 |
0.3124 | 10.0 | 14570 | 0.4641 | 0.1743 | 0.1684 | 0.2557 | 0.7443 | 55594 | 6428 | 2565 | 2267 | 0.1368 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1