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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-8front-1body-0rear |
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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-8front-1body-0rear |
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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: 0.4589 |
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- Wer: 0.1739 |
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- Mer: 0.1679 |
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- Wil: 0.2545 |
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- Wip: 0.7455 |
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- Hits: 55667 |
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- Substitutions: 6385 |
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- Deletions: 2535 |
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- Insertions: 2309 |
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- Cer: 0.1363 |
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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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| 0.6586 | 1.0 | 1457 | 0.4812 | 0.2110 | 0.1994 | 0.2888 | 0.7112 | 54745 | 6712 | 3130 | 3789 | 0.1784 | |
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| 0.5246 | 2.0 | 2914 | 0.4383 | 0.1839 | 0.1770 | 0.2641 | 0.7359 | 55251 | 6428 | 2908 | 2544 | 0.1481 | |
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| 0.4795 | 3.0 | 4371 | 0.4327 | 0.1811 | 0.1740 | 0.2610 | 0.7390 | 55523 | 6438 | 2626 | 2631 | 0.1458 | |
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| 0.4224 | 4.0 | 5828 | 0.4328 | 0.1754 | 0.1693 | 0.2555 | 0.7445 | 55577 | 6338 | 2672 | 2318 | 0.1397 | |
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| 0.3755 | 5.0 | 7285 | 0.4351 | 0.1723 | 0.1668 | 0.2529 | 0.7471 | 55607 | 6326 | 2654 | 2150 | 0.1362 | |
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| 0.3538 | 6.0 | 8742 | 0.4413 | 0.1728 | 0.1670 | 0.2531 | 0.7469 | 55696 | 6341 | 2550 | 2271 | 0.1372 | |
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| 0.3686 | 7.0 | 10199 | 0.4455 | 0.1715 | 0.1659 | 0.2519 | 0.7481 | 55692 | 6319 | 2576 | 2180 | 0.1354 | |
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| 0.3004 | 8.0 | 11656 | 0.4518 | 0.1727 | 0.1668 | 0.2537 | 0.7463 | 55712 | 6400 | 2475 | 2281 | 0.1371 | |
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| 0.2914 | 9.0 | 13113 | 0.4564 | 0.1739 | 0.1678 | 0.2544 | 0.7456 | 55681 | 6378 | 2528 | 2323 | 0.1370 | |
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| 0.297 | 10.0 | 14570 | 0.4589 | 0.1739 | 0.1679 | 0.2545 | 0.7455 | 55667 | 6385 | 2535 | 2309 | 0.1363 | |
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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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