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t5-base-TEDxJP-2front-1body-2rear
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: 0.4506
- Wer: 0.1731
- Mer: 0.1672
- Wil: 0.2543
- Wip: 0.7457
- Hits: 55684
- Substitutions: 6416
- Deletions: 2487
- Insertions: 2280
- Cer: 0.1363
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 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0.6398 | 1.0 | 1457 | 0.4851 | 0.2123 | 0.1997 | 0.2912 | 0.7088 | 54936 | 6930 | 2721 | 4058 | 0.1854 |
0.525 | 2.0 | 2914 | 0.4364 | 0.1800 | 0.1735 | 0.2623 | 0.7377 | 55355 | 6566 | 2666 | 2392 | 0.1406 |
0.4731 | 3.0 | 4371 | 0.4294 | 0.1796 | 0.1728 | 0.2600 | 0.7400 | 55542 | 6444 | 2601 | 2558 | 0.1417 |
0.4093 | 4.0 | 5828 | 0.4291 | 0.1728 | 0.1670 | 0.2541 | 0.7459 | 55667 | 6419 | 2501 | 2238 | 0.1349 |
0.3718 | 5.0 | 7285 | 0.4285 | 0.1727 | 0.1669 | 0.2542 | 0.7458 | 55657 | 6430 | 2500 | 2223 | 0.1351 |
0.3436 | 6.0 | 8742 | 0.4318 | 0.1725 | 0.1669 | 0.2541 | 0.7459 | 55614 | 6412 | 2561 | 2171 | 0.1361 |
0.371 | 7.0 | 10199 | 0.4382 | 0.1722 | 0.1665 | 0.2530 | 0.7470 | 55664 | 6361 | 2562 | 2197 | 0.1351 |
0.3007 | 8.0 | 11656 | 0.4427 | 0.1726 | 0.1668 | 0.2535 | 0.7465 | 55691 | 6380 | 2516 | 2252 | 0.1357 |
0.2997 | 9.0 | 13113 | 0.4464 | 0.1731 | 0.1672 | 0.2543 | 0.7457 | 55682 | 6420 | 2485 | 2276 | 0.1362 |
0.2969 | 10.0 | 14570 | 0.4506 | 0.1731 | 0.1672 | 0.2543 | 0.7457 | 55684 | 6416 | 2487 | 2280 | 0.1363 |
Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Datasets 2.4.0
- Tokenizers 0.12.1