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update model card README.md

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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: 0.4359
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- - Wer: 0.1693
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- - Mer: 0.1635
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- - Wil: 0.2485
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- - Wip: 0.7515
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- - Hits: 55936
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- - Substitutions: 6242
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- - Deletions: 2409
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- - Insertions: 2283
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- - Cer: 0.1334
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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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- | 0.5891 | 1.0 | 1457 | 0.4696 | 0.2065 | 0.1954 | 0.2843 | 0.7157 | 54931 | 6665 | 2991 | 3684 | 0.1713 |
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- | 0.4787 | 2.0 | 2914 | 0.4172 | 0.1767 | 0.1707 | 0.2566 | 0.7434 | 55432 | 6311 | 2844 | 2256 | 0.1417 |
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- | 0.4425 | 3.0 | 4371 | 0.4078 | 0.1727 | 0.1667 | 0.2528 | 0.7472 | 55754 | 6342 | 2491 | 2320 | 0.1362 |
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- | 0.3887 | 4.0 | 5828 | 0.4101 | 0.1718 | 0.1657 | 0.2521 | 0.7479 | 55850 | 6367 | 2370 | 2356 | 0.1346 |
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- | 0.3567 | 5.0 | 7285 | 0.4120 | 0.1708 | 0.1648 | 0.2505 | 0.7495 | 55926 | 6315 | 2346 | 2371 | 0.1333 |
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- | 0.3157 | 6.0 | 8742 | 0.4174 | 0.1710 | 0.1652 | 0.2510 | 0.7490 | 55805 | 6313 | 2469 | 2260 | 0.1346 |
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- | 0.2958 | 7.0 | 10199 | 0.4277 | 0.1700 | 0.1641 | 0.2493 | 0.7507 | 55934 | 6263 | 2390 | 2330 | 0.1339 |
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- | 0.3088 | 8.0 | 11656 | 0.4268 | 0.1687 | 0.1631 | 0.2483 | 0.7517 | 55890 | 6252 | 2445 | 2198 | 0.1329 |
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- | 0.2725 | 9.0 | 13113 | 0.4352 | 0.1697 | 0.1639 | 0.2490 | 0.7510 | 55911 | 6247 | 2429 | 2287 | 0.1329 |
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- | 0.2588 | 10.0 | 14570 | 0.4359 | 0.1693 | 0.1635 | 0.2485 | 0.7515 | 55936 | 6242 | 2409 | 2283 | 0.1334 |
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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: 0.4383
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+ - Wer: 0.1703
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+ - Mer: 0.1643
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+ - Wil: 0.2498
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+ - Wip: 0.7502
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+ - Hits: 55917
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+ - Substitutions: 6285
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+ - Deletions: 2385
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+ - Insertions: 2327
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+ - Cer: 0.1338
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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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+ | 0.5634 | 1.0 | 1457 | 0.4668 | 0.2271 | 0.2101 | 0.2986 | 0.7014 | 55139 | 6756 | 2692 | 5219 | 0.1993 |
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+ | 0.5335 | 2.0 | 2914 | 0.4168 | 0.1852 | 0.1776 | 0.2649 | 0.7351 | 55407 | 6467 | 2713 | 2782 | 0.1495 |
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+ | 0.4453 | 3.0 | 4371 | 0.4124 | 0.1738 | 0.1678 | 0.2545 | 0.7455 | 55683 | 6391 | 2513 | 2321 | 0.1344 |
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+ | 0.388 | 4.0 | 5828 | 0.4082 | 0.1703 | 0.1646 | 0.2502 | 0.7498 | 55838 | 6297 | 2452 | 2249 | 0.1324 |
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+ | 0.3448 | 5.0 | 7285 | 0.4156 | 0.1704 | 0.1646 | 0.2505 | 0.7495 | 55840 | 6320 | 2427 | 2257 | 0.1339 |
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+ | 0.3103 | 6.0 | 8742 | 0.4177 | 0.1690 | 0.1632 | 0.2484 | 0.7516 | 55955 | 6263 | 2369 | 2280 | 0.1324 |
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+ | 0.3369 | 7.0 | 10199 | 0.4225 | 0.1688 | 0.1631 | 0.2480 | 0.7520 | 55930 | 6230 | 2427 | 2244 | 0.1327 |
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+ | 0.3127 | 8.0 | 11656 | 0.4294 | 0.1692 | 0.1636 | 0.2489 | 0.7511 | 55876 | 6265 | 2446 | 2220 | 0.1331 |
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+ | 0.2739 | 9.0 | 13113 | 0.4329 | 0.1702 | 0.1643 | 0.2501 | 0.7499 | 55903 | 6316 | 2368 | 2307 | 0.1338 |
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+ | 0.269 | 10.0 | 14570 | 0.4383 | 0.1703 | 0.1643 | 0.2498 | 0.7502 | 55917 | 6285 | 2385 | 2327 | 0.1338 |
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  ### Framework versions