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--- |
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license: cc-by-sa-4.0 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- te_dx_jp |
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model-index: |
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- name: t5-base-TEDxJP-2front-1body-2rear |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# t5-base-TEDxJP-2front-1body-2rear |
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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: 0.4473 |
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- Wer: 0.1735 |
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- Mer: 0.1675 |
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- Wil: 0.2549 |
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- Wip: 0.7451 |
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- Hits: 55674 |
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- Substitutions: 6443 |
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- Deletions: 2470 |
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- Insertions: 2291 |
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- Cer: 0.1360 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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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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- num_epochs: 10 |
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### Training results |
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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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| 0.5915 | 1.0 | 1457 | 0.4825 | 0.2230 | 0.2081 | 0.2976 | 0.7024 | 54814 | 6785 | 2988 | 4630 | 0.1980 | |
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| 0.5402 | 2.0 | 2914 | 0.4359 | 0.1855 | 0.1779 | 0.2664 | 0.7336 | 55358 | 6566 | 2663 | 2753 | 0.1503 | |
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| 0.4635 | 3.0 | 4371 | 0.4277 | 0.1762 | 0.1701 | 0.2584 | 0.7416 | 55538 | 6528 | 2521 | 2334 | 0.1368 | |
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| 0.3608 | 4.0 | 5828 | 0.4271 | 0.1723 | 0.1667 | 0.2529 | 0.7471 | 55621 | 6337 | 2629 | 2160 | 0.1342 | |
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| 0.3825 | 5.0 | 7285 | 0.4276 | 0.1724 | 0.1666 | 0.2527 | 0.7473 | 55669 | 6328 | 2590 | 2214 | 0.1343 | |
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| 0.3814 | 6.0 | 8742 | 0.4278 | 0.1727 | 0.1670 | 0.2537 | 0.7463 | 55613 | 6373 | 2601 | 2179 | 0.1360 | |
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| 0.3412 | 7.0 | 10199 | 0.4344 | 0.1724 | 0.1668 | 0.2539 | 0.7461 | 55631 | 6410 | 2546 | 2180 | 0.1352 | |
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| 0.3175 | 8.0 | 11656 | 0.4392 | 0.1724 | 0.1665 | 0.2534 | 0.7466 | 55713 | 6394 | 2480 | 2259 | 0.1362 | |
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| 0.2871 | 9.0 | 13113 | 0.4456 | 0.1732 | 0.1671 | 0.2538 | 0.7462 | 55748 | 6393 | 2446 | 2347 | 0.1360 | |
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| 0.3317 | 10.0 | 14570 | 0.4473 | 0.1735 | 0.1675 | 0.2549 | 0.7451 | 55674 | 6443 | 2470 | 2291 | 0.1360 | |
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### Framework versions |
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- Transformers 4.21.2 |
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- Pytorch 1.12.1+cu116 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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