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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-3front-1body-3rear |
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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-3front-1body-3rear |
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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: 0.4427 |
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- Wer: 0.1709 |
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- Mer: 0.1651 |
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- Wil: 0.2519 |
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- Wip: 0.7481 |
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- Hits: 55802 |
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- Substitutions: 6391 |
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- Deletions: 2394 |
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- Insertions: 2252 |
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- Cer: 0.1337 |
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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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| 0.628 | 1.0 | 1457 | 0.4785 | 0.2008 | 0.1912 | 0.2803 | 0.7197 | 54855 | 6650 | 3082 | 3234 | 0.1735 | |
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| 0.5271 | 2.0 | 2914 | 0.4292 | 0.1779 | 0.1718 | 0.2602 | 0.7398 | 55387 | 6527 | 2673 | 2293 | 0.1469 | |
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| 0.4724 | 3.0 | 4371 | 0.4222 | 0.1719 | 0.1664 | 0.2530 | 0.7470 | 55610 | 6365 | 2612 | 2123 | 0.1353 | |
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| 0.4125 | 4.0 | 5828 | 0.4174 | 0.1707 | 0.1653 | 0.2512 | 0.7488 | 55694 | 6304 | 2589 | 2135 | 0.1342 | |
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| 0.3646 | 5.0 | 7285 | 0.4218 | 0.1712 | 0.1655 | 0.2521 | 0.7479 | 55756 | 6373 | 2458 | 2224 | 0.1339 | |
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| 0.3232 | 6.0 | 8742 | 0.4253 | 0.1695 | 0.1642 | 0.2505 | 0.7495 | 55726 | 6340 | 2521 | 2087 | 0.1333 | |
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| 0.3583 | 7.0 | 10199 | 0.4303 | 0.1699 | 0.1645 | 0.2514 | 0.7486 | 55733 | 6393 | 2461 | 2120 | 0.1338 | |
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| 0.2894 | 8.0 | 11656 | 0.4355 | 0.1699 | 0.1643 | 0.2508 | 0.7492 | 55827 | 6371 | 2389 | 2215 | 0.1325 | |
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| 0.2825 | 9.0 | 13113 | 0.4399 | 0.1705 | 0.1648 | 0.2518 | 0.7482 | 55785 | 6409 | 2393 | 2207 | 0.1334 | |
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| 0.2901 | 10.0 | 14570 | 0.4427 | 0.1709 | 0.1651 | 0.2519 | 0.7481 | 55802 | 6391 | 2394 | 2252 | 0.1337 | |
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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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