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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-8rear |
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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-8rear |
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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: 0.4376 |
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- Wer: 0.1693 |
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- Mer: 0.1635 |
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- Wil: 0.2492 |
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- Wip: 0.7508 |
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- Hits: 55925 |
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- Substitutions: 6304 |
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- Deletions: 2358 |
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- Insertions: 2270 |
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- Cer: 0.1339 |
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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: 40 |
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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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| 0.6093 | 1.0 | 1457 | 0.4649 | 0.2573 | 0.2324 | 0.3203 | 0.6797 | 54884 | 6813 | 2890 | 6916 | 0.2380 | |
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| 0.5049 | 2.0 | 2914 | 0.4186 | 0.1791 | 0.1722 | 0.2595 | 0.7405 | 55594 | 6456 | 2537 | 2575 | 0.1432 | |
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| 0.4746 | 3.0 | 4371 | 0.4147 | 0.1741 | 0.1681 | 0.2539 | 0.7461 | 55665 | 6315 | 2607 | 2324 | 0.1392 | |
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| 0.4295 | 4.0 | 5828 | 0.4118 | 0.1723 | 0.1661 | 0.2523 | 0.7477 | 55884 | 6360 | 2343 | 2425 | 0.1342 | |
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| 0.3881 | 5.0 | 7285 | 0.4123 | 0.1696 | 0.1639 | 0.2496 | 0.7504 | 55896 | 6301 | 2390 | 2265 | 0.1397 | |
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| 0.3151 | 6.0 | 8742 | 0.4174 | 0.1687 | 0.1631 | 0.2482 | 0.7518 | 55924 | 6249 | 2414 | 2236 | 0.1329 | |
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| 0.2977 | 7.0 | 10199 | 0.4248 | 0.1674 | 0.1618 | 0.2466 | 0.7534 | 56006 | 6227 | 2354 | 2229 | 0.1321 | |
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| 0.2737 | 8.0 | 11656 | 0.4293 | 0.1685 | 0.1629 | 0.2485 | 0.7515 | 55898 | 6288 | 2401 | 2192 | 0.1340 | |
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| 0.2574 | 9.0 | 13113 | 0.4374 | 0.1683 | 0.1627 | 0.2480 | 0.7520 | 55930 | 6268 | 2389 | 2212 | 0.1329 | |
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| 0.2472 | 10.0 | 14570 | 0.4376 | 0.1693 | 0.1635 | 0.2492 | 0.7508 | 55925 | 6304 | 2358 | 2270 | 0.1339 | |
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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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