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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-5front-1body-5rear |
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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-5front-1body-5rear |
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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: 0.4383 |
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- Wer: 0.1697 |
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- Mer: 0.1641 |
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- Wil: 0.2500 |
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- Wip: 0.7500 |
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- Hits: 55852 |
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- Substitutions: 6314 |
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- Deletions: 2421 |
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- Insertions: 2228 |
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- Cer: 0.1328 |
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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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| 0.6185 | 1.0 | 1457 | 0.4683 | 0.1948 | 0.1863 | 0.2758 | 0.7242 | 54959 | 6658 | 2970 | 2956 | 0.1682 | |
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| 0.5149 | 2.0 | 2914 | 0.4280 | 0.1773 | 0.1713 | 0.2591 | 0.7409 | 55376 | 6468 | 2743 | 2238 | 0.1426 | |
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| 0.4705 | 3.0 | 4371 | 0.4173 | 0.1743 | 0.1682 | 0.2552 | 0.7448 | 55680 | 6418 | 2489 | 2351 | 0.1387 | |
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| 0.4023 | 4.0 | 5828 | 0.4114 | 0.1713 | 0.1656 | 0.2515 | 0.7485 | 55751 | 6313 | 2523 | 2230 | 0.1335 | |
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| 0.3497 | 5.0 | 7285 | 0.4162 | 0.1722 | 0.1662 | 0.2522 | 0.7478 | 55787 | 6331 | 2469 | 2323 | 0.1365 | |
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| 0.3246 | 6.0 | 8742 | 0.4211 | 0.1714 | 0.1655 | 0.2513 | 0.7487 | 55802 | 6310 | 2475 | 2284 | 0.1367 | |
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| 0.3492 | 7.0 | 10199 | 0.4282 | 0.1711 | 0.1652 | 0.2514 | 0.7486 | 55861 | 6350 | 2376 | 2325 | 0.1341 | |
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| 0.2788 | 8.0 | 11656 | 0.4322 | 0.1698 | 0.1641 | 0.2502 | 0.7498 | 55883 | 6342 | 2362 | 2265 | 0.1327 | |
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| 0.2801 | 9.0 | 13113 | 0.4362 | 0.1710 | 0.1652 | 0.2514 | 0.7486 | 55828 | 6351 | 2408 | 2288 | 0.1352 | |
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| 0.2773 | 10.0 | 14570 | 0.4383 | 0.1697 | 0.1641 | 0.2500 | 0.7500 | 55852 | 6314 | 2421 | 2228 | 0.1328 | |
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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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