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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-7front-1body-7rear |
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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-7front-1body-7rear |
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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: 0.4380 |
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- Wer: 0.1697 |
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- Mer: 0.1639 |
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- Wil: 0.2501 |
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- Wip: 0.7499 |
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- Hits: 55904 |
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- Substitutions: 6350 |
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- Deletions: 2333 |
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- Insertions: 2275 |
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- Cer: 0.1321 |
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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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| 0.5926 | 1.0 | 1457 | 0.4717 | 0.2141 | 0.2008 | 0.2898 | 0.7102 | 55014 | 6714 | 2859 | 4253 | 0.1829 | |
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| 0.4821 | 2.0 | 2914 | 0.4178 | 0.1796 | 0.1733 | 0.2595 | 0.7405 | 55368 | 6348 | 2871 | 2384 | 0.1452 | |
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| 0.4444 | 3.0 | 4371 | 0.4103 | 0.1768 | 0.1700 | 0.2561 | 0.7439 | 55745 | 6359 | 2483 | 2577 | 0.1416 | |
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| 0.3824 | 4.0 | 5828 | 0.4145 | 0.1712 | 0.1653 | 0.2516 | 0.7484 | 55844 | 6362 | 2381 | 2314 | 0.1335 | |
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| 0.3481 | 5.0 | 7285 | 0.4133 | 0.1722 | 0.1659 | 0.2512 | 0.7488 | 55917 | 6283 | 2387 | 2449 | 0.1357 | |
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| 0.312 | 6.0 | 8742 | 0.4204 | 0.1719 | 0.1659 | 0.2516 | 0.7484 | 55845 | 6315 | 2427 | 2363 | 0.1360 | |
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| 0.3001 | 7.0 | 10199 | 0.4253 | 0.1684 | 0.1629 | 0.2486 | 0.7514 | 55908 | 6297 | 2382 | 2200 | 0.1312 | |
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| 0.3152 | 8.0 | 11656 | 0.4282 | 0.1689 | 0.1632 | 0.2491 | 0.7509 | 55909 | 6317 | 2361 | 2228 | 0.1322 | |
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| 0.2716 | 9.0 | 13113 | 0.4338 | 0.1694 | 0.1637 | 0.2497 | 0.7503 | 55865 | 6316 | 2406 | 2217 | 0.1321 | |
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| 0.2544 | 10.0 | 14570 | 0.4380 | 0.1697 | 0.1639 | 0.2501 | 0.7499 | 55904 | 6350 | 2333 | 2275 | 0.1321 | |
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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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