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t5-base-TEDxJP-0front-1body-10rear-order-RB

Text2Text Generation
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t5
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text-generation-inference
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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: 0.4714
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- - Wer: 0.1751
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- - Mer: 0.1694
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- - Wil: 0.2572
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- - Wip: 0.7428
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- - Hits: 55476
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- - Substitutions: 6473
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- - Deletions: 2638
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- - Insertions: 2201
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- - Cer: 0.1381
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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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- | 0.6116 | 1.0 | 1457 | 0.4923 | 0.2289 | 0.2127 | 0.3015 | 0.6985 | 54722 | 6733 | 3132 | 4917 | 0.1992 |
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- | 0.5362 | 2.0 | 2914 | 0.4506 | 0.1835 | 0.1770 | 0.2661 | 0.7339 | 55105 | 6590 | 2892 | 2369 | 0.1447 |
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- | 0.4869 | 3.0 | 4371 | 0.4459 | 0.1806 | 0.1742 | 0.2629 | 0.7371 | 55298 | 6556 | 2733 | 2374 | 0.1424 |
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- | 0.4642 | 4.0 | 5828 | 0.4413 | 0.1767 | 0.1710 | 0.2588 | 0.7412 | 55331 | 6462 | 2794 | 2157 | 0.1379 |
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- | 0.4395 | 5.0 | 7285 | 0.4462 | 0.1779 | 0.1719 | 0.2594 | 0.7406 | 55367 | 6451 | 2769 | 2270 | 0.1391 |
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- | 0.3831 | 6.0 | 8742 | 0.4493 | 0.1751 | 0.1696 | 0.2568 | 0.7432 | 55370 | 6409 | 2808 | 2092 | 0.1369 |
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- | 0.3446 | 7.0 | 10199 | 0.4563 | 0.1769 | 0.1710 | 0.2595 | 0.7405 | 55401 | 6535 | 2651 | 2238 | 0.1397 |
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- | 0.3031 | 8.0 | 11656 | 0.4657 | 0.1754 | 0.1697 | 0.2578 | 0.7422 | 55436 | 6492 | 2659 | 2179 | 0.1372 |
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- | 0.3406 | 9.0 | 13113 | 0.4677 | 0.1750 | 0.1692 | 0.2570 | 0.7430 | 55502 | 6474 | 2611 | 2219 | 0.1365 |
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- | 0.3067 | 10.0 | 14570 | 0.4714 | 0.1751 | 0.1694 | 0.2572 | 0.7428 | 55476 | 6473 | 2638 | 2201 | 0.1381 |
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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: 0.4749
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+ - Wer: 0.1754
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+ - Mer: 0.1696
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+ - Wil: 0.2575
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+ - Wip: 0.7425
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+ - Hits: 55482
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+ - Substitutions: 6478
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+ - Deletions: 2627
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+ - Insertions: 2225
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+ - Cer: 0.1370
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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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+ | 0.637 | 1.0 | 1457 | 0.4932 | 0.2359 | 0.2179 | 0.3082 | 0.6918 | 54682 | 6909 | 2996 | 5331 | 0.2100 |
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+ | 0.5501 | 2.0 | 2914 | 0.4572 | 0.1831 | 0.1766 | 0.2655 | 0.7345 | 55134 | 6575 | 2878 | 2370 | 0.1461 |
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+ | 0.5505 | 3.0 | 4371 | 0.4470 | 0.1787 | 0.1728 | 0.2609 | 0.7391 | 55267 | 6494 | 2826 | 2222 | 0.1400 |
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+ | 0.4921 | 4.0 | 5828 | 0.4426 | 0.1794 | 0.1730 | 0.2606 | 0.7394 | 55420 | 6468 | 2699 | 2423 | 0.1407 |
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+ | 0.4465 | 5.0 | 7285 | 0.4507 | 0.1783 | 0.1721 | 0.2596 | 0.7404 | 55420 | 6458 | 2709 | 2351 | 0.1390 |
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+ | 0.3557 | 6.0 | 8742 | 0.4567 | 0.1768 | 0.1708 | 0.2585 | 0.7415 | 55416 | 6459 | 2712 | 2245 | 0.1401 |
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+ | 0.3367 | 7.0 | 10199 | 0.4613 | 0.1772 | 0.1709 | 0.2589 | 0.7411 | 55505 | 6497 | 2585 | 2363 | 0.1387 |
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+ | 0.328 | 8.0 | 11656 | 0.4624 | 0.1769 | 0.1708 | 0.2587 | 0.7413 | 55442 | 6478 | 2667 | 2278 | 0.1383 |
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+ | 0.2992 | 9.0 | 13113 | 0.4726 | 0.1764 | 0.1704 | 0.2580 | 0.7420 | 55461 | 6463 | 2663 | 2264 | 0.1378 |
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+ | 0.2925 | 10.0 | 14570 | 0.4749 | 0.1754 | 0.1696 | 0.2575 | 0.7425 | 55482 | 6478 | 2627 | 2225 | 0.1370 |
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  ### Framework versions