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Text2Text Generation
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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: 0.4383
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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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@@ -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: 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
 
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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: 0.4378
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+ - Wer: 0.1696
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+ - Mer: 0.1640
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+ - Wil: 0.2499
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+ - Wip: 0.7501
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+ - Hits: 55848
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+ - Substitutions: 6316
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+ - Deletions: 2423
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+ - Insertions: 2216
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+ - Cer: 0.1327
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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: 20
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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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+ | 0.5935 | 1.0 | 1457 | 0.4683 | 0.2101 | 0.1975 | 0.2870 | 0.7130 | 55138 | 6765 | 2684 | 4118 | 0.1740 |
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+ | 0.5128 | 2.0 | 2914 | 0.4191 | 0.1781 | 0.1719 | 0.2585 | 0.7415 | 55397 | 6370 | 2820 | 2310 | 0.1415 |
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+ | 0.4948 | 3.0 | 4371 | 0.4085 | 0.1738 | 0.1679 | 0.2540 | 0.7460 | 55644 | 6330 | 2613 | 2284 | 0.1367 |
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+ | 0.4164 | 4.0 | 5828 | 0.4065 | 0.1698 | 0.1643 | 0.2497 | 0.7503 | 55778 | 6263 | 2546 | 2159 | 0.1326 |
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+ | 0.3844 | 5.0 | 7285 | 0.4134 | 0.1705 | 0.1646 | 0.2502 | 0.7498 | 55882 | 6295 | 2410 | 2306 | 0.1344 |
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+ | 0.3327 | 6.0 | 8742 | 0.4175 | 0.1706 | 0.1649 | 0.2506 | 0.7494 | 55774 | 6290 | 2523 | 2203 | 0.1338 |
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+ | 0.2931 | 7.0 | 10199 | 0.4262 | 0.1717 | 0.1655 | 0.2511 | 0.7489 | 55899 | 6307 | 2381 | 2399 | 0.1353 |
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+ | 0.2752 | 8.0 | 11656 | 0.4269 | 0.1694 | 0.1639 | 0.2495 | 0.7505 | 55833 | 6291 | 2463 | 2190 | 0.1324 |
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+ | 0.274 | 9.0 | 13113 | 0.4359 | 0.1701 | 0.1643 | 0.2502 | 0.7498 | 55866 | 6311 | 2410 | 2266 | 0.1333 |
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+ | 0.2426 | 10.0 | 14570 | 0.4378 | 0.1696 | 0.1640 | 0.2499 | 0.7501 | 55848 | 6316 | 2423 | 2216 | 0.1327 |
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  ### Framework versions