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@@ -16,16 +16,16 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4380
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- - Wer: 0.1697
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- - Mer: 0.1639
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- - Wil: 0.2501
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- - Wip: 0.7499
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- - Hits: 55904
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- - Substitutions: 6350
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- - Deletions: 2333
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- - Insertions: 2275
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- - Cer: 0.1321
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  ## Model description
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@@ -47,7 +47,7 @@ The following hyperparameters were used during training:
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  - learning_rate: 0.0001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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- - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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  | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
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- | 0.5926 | 1.0 | 1457 | 0.4717 | 0.2141 | 0.2008 | 0.2898 | 0.7102 | 55014 | 6714 | 2859 | 4253 | 0.1829 |
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- | 0.4821 | 2.0 | 2914 | 0.4178 | 0.1796 | 0.1733 | 0.2595 | 0.7405 | 55368 | 6348 | 2871 | 2384 | 0.1452 |
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- | 0.4444 | 3.0 | 4371 | 0.4103 | 0.1768 | 0.1700 | 0.2561 | 0.7439 | 55745 | 6359 | 2483 | 2577 | 0.1416 |
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- | 0.3824 | 4.0 | 5828 | 0.4145 | 0.1712 | 0.1653 | 0.2516 | 0.7484 | 55844 | 6362 | 2381 | 2314 | 0.1335 |
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- | 0.3481 | 5.0 | 7285 | 0.4133 | 0.1722 | 0.1659 | 0.2512 | 0.7488 | 55917 | 6283 | 2387 | 2449 | 0.1357 |
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- | 0.312 | 6.0 | 8742 | 0.4204 | 0.1719 | 0.1659 | 0.2516 | 0.7484 | 55845 | 6315 | 2427 | 2363 | 0.1360 |
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- | 0.3001 | 7.0 | 10199 | 0.4253 | 0.1684 | 0.1629 | 0.2486 | 0.7514 | 55908 | 6297 | 2382 | 2200 | 0.1312 |
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- | 0.3152 | 8.0 | 11656 | 0.4282 | 0.1689 | 0.1632 | 0.2491 | 0.7509 | 55909 | 6317 | 2361 | 2228 | 0.1322 |
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- | 0.2716 | 9.0 | 13113 | 0.4338 | 0.1694 | 0.1637 | 0.2497 | 0.7503 | 55865 | 6316 | 2406 | 2217 | 0.1321 |
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- | 0.2544 | 10.0 | 14570 | 0.4380 | 0.1697 | 0.1639 | 0.2501 | 0.7499 | 55904 | 6350 | 2333 | 2275 | 0.1321 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4385
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+ - Wer: 0.1688
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+ - Mer: 0.1632
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+ - Wil: 0.2486
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+ - Wip: 0.7514
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+ - Hits: 55895
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+ - Substitutions: 6273
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+ - Deletions: 2419
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+ - Insertions: 2208
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+ - Cer: 0.1334
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  ## Model description
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  - learning_rate: 0.0001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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+ - seed: 10
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
 
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  | Training Loss | Epoch | Step | Validation Loss | Wer | Mer | Wil | Wip | Hits | Substitutions | Deletions | Insertions | Cer |
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  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
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+ | 0.5657 | 1.0 | 1457 | 0.4744 | 0.2212 | 0.2056 | 0.2946 | 0.7054 | 55195 | 6783 | 2609 | 4892 | 0.1922 |
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+ | 0.5388 | 2.0 | 2914 | 0.4193 | 0.1791 | 0.1724 | 0.2601 | 0.7399 | 55526 | 6485 | 2576 | 2507 | 0.1404 |
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+ | 0.447 | 3.0 | 4371 | 0.4119 | 0.1728 | 0.1670 | 0.2531 | 0.7469 | 55690 | 6334 | 2563 | 2266 | 0.1341 |
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+ | 0.3952 | 4.0 | 5828 | 0.4088 | 0.1704 | 0.1647 | 0.2503 | 0.7497 | 55802 | 6283 | 2502 | 2220 | 0.1330 |
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+ | 0.3498 | 5.0 | 7285 | 0.4138 | 0.1701 | 0.1643 | 0.2500 | 0.7500 | 55881 | 6305 | 2401 | 2280 | 0.1321 |
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+ | 0.3107 | 6.0 | 8742 | 0.4201 | 0.1693 | 0.1636 | 0.2497 | 0.7503 | 55888 | 6334 | 2365 | 2236 | 0.1319 |
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+ | 0.3443 | 7.0 | 10199 | 0.4239 | 0.1694 | 0.1637 | 0.2495 | 0.7505 | 55890 | 6309 | 2388 | 2241 | 0.1328 |
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+ | 0.3099 | 8.0 | 11656 | 0.4316 | 0.1695 | 0.1639 | 0.2496 | 0.7504 | 55833 | 6292 | 2462 | 2192 | 0.1337 |
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+ | 0.2804 | 9.0 | 13113 | 0.4345 | 0.1687 | 0.1631 | 0.2486 | 0.7514 | 55896 | 6273 | 2418 | 2206 | 0.1332 |
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+ | 0.2714 | 10.0 | 14570 | 0.4385 | 0.1688 | 0.1632 | 0.2486 | 0.7514 | 55895 | 6273 | 2419 | 2208 | 0.1334 |
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  ### Framework versions