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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: 0.4384
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- - Wer: 0.1692
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- - Mer: 0.1635
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- - Wil: 0.2483
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- - Wip: 0.7517
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- - Hits: 55908
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- - Substitutions: 6222
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- - Deletions: 2457
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- - Insertions: 2249
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- - Cer: 0.1327
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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: 30
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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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- | 0.5881 | 1.0 | 1457 | 0.4572 | 0.2115 | 0.1995 | 0.2882 | 0.7118 | 54840 | 6661 | 3086 | 3916 | 0.1812 |
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- | 0.5018 | 2.0 | 2914 | 0.4167 | 0.1843 | 0.1765 | 0.2640 | 0.7360 | 55546 | 6494 | 2547 | 2863 | 0.1490 |
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- | 0.4633 | 3.0 | 4371 | 0.4110 | 0.1738 | 0.1679 | 0.2540 | 0.7460 | 55623 | 6327 | 2637 | 2260 | 0.1369 |
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- | 0.3971 | 4.0 | 5828 | 0.4068 | 0.1724 | 0.1666 | 0.2522 | 0.7478 | 55672 | 6278 | 2637 | 2218 | 0.1351 |
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- | 0.3907 | 5.0 | 7285 | 0.4131 | 0.1688 | 0.1635 | 0.2479 | 0.7521 | 55789 | 6180 | 2618 | 2106 | 0.1325 |
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- | 0.3305 | 6.0 | 8742 | 0.4147 | 0.1706 | 0.1649 | 0.2504 | 0.7496 | 55797 | 6281 | 2509 | 2227 | 0.1336 |
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- | 0.2937 | 7.0 | 10199 | 0.4236 | 0.1692 | 0.1636 | 0.2482 | 0.7518 | 55883 | 6207 | 2497 | 2223 | 0.1334 |
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- | 0.2649 | 8.0 | 11656 | 0.4307 | 0.1693 | 0.1638 | 0.2493 | 0.7507 | 55806 | 6272 | 2509 | 2154 | 0.1329 |
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- | 0.2914 | 9.0 | 13113 | 0.4319 | 0.1691 | 0.1634 | 0.2482 | 0.7518 | 55928 | 6230 | 2429 | 2262 | 0.1328 |
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- | 0.2598 | 10.0 | 14570 | 0.4384 | 0.1692 | 0.1635 | 0.2483 | 0.7517 | 55908 | 6222 | 2457 | 2249 | 0.1327 |
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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: 0.4371
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+ - Wer: 0.1693
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+ - Mer: 0.1636
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+ - Wil: 0.2493
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+ - Wip: 0.7507
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+ - Hits: 55894
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+ - Substitutions: 6298
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+ - Deletions: 2395
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+ - Insertions: 2240
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+ - Cer: 0.1325
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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: 40
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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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+ | 0.6129 | 1.0 | 1457 | 0.4667 | 0.2078 | 0.1962 | 0.2857 | 0.7143 | 54967 | 6724 | 2896 | 3799 | 0.1785 |
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+ | 0.5027 | 2.0 | 2914 | 0.4202 | 0.1767 | 0.1705 | 0.2573 | 0.7427 | 55529 | 6397 | 2661 | 2356 | 0.1393 |
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+ | 0.486 | 3.0 | 4371 | 0.4128 | 0.1720 | 0.1667 | 0.2522 | 0.7478 | 55546 | 6265 | 2776 | 2068 | 0.1352 |
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+ | 0.4381 | 4.0 | 5828 | 0.4077 | 0.1726 | 0.1664 | 0.2515 | 0.7485 | 55866 | 6263 | 2458 | 2427 | 0.1363 |
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+ | 0.3859 | 5.0 | 7285 | 0.4151 | 0.1703 | 0.1644 | 0.2502 | 0.7498 | 55873 | 6310 | 2404 | 2282 | 0.1322 |
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+ | 0.3091 | 6.0 | 8742 | 0.4172 | 0.1709 | 0.1649 | 0.2501 | 0.7499 | 55913 | 6267 | 2407 | 2365 | 0.1386 |
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+ | 0.3012 | 7.0 | 10199 | 0.4258 | 0.1697 | 0.1637 | 0.2493 | 0.7507 | 55996 | 6304 | 2287 | 2369 | 0.1325 |
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+ | 0.2837 | 8.0 | 11656 | 0.4275 | 0.1696 | 0.1639 | 0.2499 | 0.7501 | 55858 | 6325 | 2404 | 2222 | 0.1328 |
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+ | 0.2625 | 9.0 | 13113 | 0.4339 | 0.1696 | 0.1639 | 0.2496 | 0.7504 | 55880 | 6296 | 2411 | 2248 | 0.1327 |
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+ | 0.2466 | 10.0 | 14570 | 0.4371 | 0.1693 | 0.1636 | 0.2493 | 0.7507 | 55894 | 6298 | 2395 | 2240 | 0.1325 |
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  ### Framework versions