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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: 0.4713
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- - Wer: 0.1763
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- - Mer: 0.1704
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- - Wil: 0.2586
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- - Wip: 0.7414
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- - Hits: 55456
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- - Substitutions: 6510
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- - Deletions: 2621
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- - Insertions: 2256
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- - Cer: 0.1383
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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: 0
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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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- | 0.6725 | 1.0 | 1457 | 0.4909 | 0.2293 | 0.2133 | 0.3017 | 0.6983 | 54628 | 6686 | 3273 | 4851 | 0.2018 |
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- | 0.5083 | 2.0 | 2914 | 0.4537 | 0.1849 | 0.1781 | 0.2663 | 0.7337 | 55108 | 6513 | 2966 | 2464 | 0.1465 |
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- | 0.4943 | 3.0 | 4371 | 0.4466 | 0.1778 | 0.1716 | 0.2599 | 0.7401 | 55424 | 6519 | 2644 | 2319 | 0.1377 |
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- | 0.4454 | 4.0 | 5828 | 0.4385 | 0.1760 | 0.1703 | 0.2579 | 0.7421 | 55384 | 6452 | 2751 | 2163 | 0.1380 |
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- | 0.411 | 5.0 | 7285 | 0.4460 | 0.1755 | 0.1697 | 0.2570 | 0.7430 | 55466 | 6430 | 2691 | 2216 | 0.1379 |
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- | 0.3756 | 6.0 | 8742 | 0.4519 | 0.1750 | 0.1694 | 0.2568 | 0.7432 | 55419 | 6435 | 2733 | 2133 | 0.1383 |
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- | 0.3647 | 7.0 | 10199 | 0.4585 | 0.1755 | 0.1699 | 0.2579 | 0.7421 | 55368 | 6475 | 2744 | 2115 | 0.1379 |
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- | 0.3079 | 8.0 | 11656 | 0.4622 | 0.1763 | 0.1704 | 0.2590 | 0.7410 | 55416 | 6540 | 2631 | 2213 | 0.1387 |
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- | 0.3029 | 9.0 | 13113 | 0.4699 | 0.1762 | 0.1703 | 0.2584 | 0.7416 | 55451 | 6499 | 2637 | 2245 | 0.1386 |
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- | 0.2968 | 10.0 | 14570 | 0.4713 | 0.1763 | 0.1704 | 0.2586 | 0.7414 | 55456 | 6510 | 2621 | 2256 | 0.1383 |
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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: 0.4705
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+ - Wer: 0.1772
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+ - Mer: 0.1711
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+ - Wil: 0.2598
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+ - Wip: 0.7402
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+ - Hits: 55441
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+ - Substitutions: 6558
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+ - Deletions: 2588
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+ - Insertions: 2296
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+ - Cer: 0.1388
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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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+ | 0.6067 | 1.0 | 1457 | 0.4967 | 0.2034 | 0.1934 | 0.2844 | 0.7156 | 54800 | 6821 | 2966 | 3351 | 0.1679 |
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+ | 0.579 | 2.0 | 2914 | 0.4534 | 0.1882 | 0.1805 | 0.2697 | 0.7303 | 55162 | 6619 | 2806 | 2728 | 0.1546 |
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+ | 0.4934 | 3.0 | 4371 | 0.4463 | 0.1768 | 0.1710 | 0.2592 | 0.7408 | 55362 | 6496 | 2729 | 2197 | 0.1396 |
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+ | 0.4371 | 4.0 | 5828 | 0.4444 | 0.1766 | 0.1707 | 0.2580 | 0.7420 | 55381 | 6417 | 2789 | 2197 | 0.1387 |
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+ | 0.3917 | 5.0 | 7285 | 0.4450 | 0.1771 | 0.1711 | 0.2595 | 0.7405 | 55415 | 6520 | 2652 | 2269 | 0.1389 |
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+ | 0.3614 | 6.0 | 8742 | 0.4516 | 0.1775 | 0.1714 | 0.2592 | 0.7408 | 55443 | 6481 | 2663 | 2323 | 0.1379 |
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+ | 0.375 | 7.0 | 10199 | 0.4568 | 0.1777 | 0.1715 | 0.2593 | 0.7407 | 55418 | 6475 | 2694 | 2306 | 0.1396 |
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+ | 0.3615 | 8.0 | 11656 | 0.4622 | 0.1764 | 0.1706 | 0.2585 | 0.7415 | 55380 | 6472 | 2735 | 2188 | 0.1382 |
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+ | 0.3129 | 9.0 | 13113 | 0.4678 | 0.1770 | 0.1709 | 0.2592 | 0.7408 | 55474 | 6524 | 2589 | 2318 | 0.1385 |
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+ | 0.3082 | 10.0 | 14570 | 0.4705 | 0.1772 | 0.1711 | 0.2598 | 0.7402 | 55441 | 6558 | 2588 | 2296 | 0.1388 |
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  ### Framework versions