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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-0front-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-0front-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: 0.4700 |
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- Wer: 0.1779 |
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- Mer: 0.1718 |
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- Wil: 0.2600 |
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- Wip: 0.7400 |
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- Hits: 55384 |
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- Substitutions: 6510 |
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- Deletions: 2693 |
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- Insertions: 2287 |
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- Cer: 0.1398 |
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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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| 0.6519 | 1.0 | 1457 | 0.4991 | 0.2099 | 0.1985 | 0.2891 | 0.7109 | 54721 | 6807 | 3059 | 3689 | 0.1855 | |
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| 0.5507 | 2.0 | 2914 | 0.4589 | 0.1827 | 0.1764 | 0.2653 | 0.7347 | 55094 | 6566 | 2927 | 2305 | 0.1504 | |
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| 0.5097 | 3.0 | 4371 | 0.4493 | 0.1797 | 0.1734 | 0.2615 | 0.7385 | 55330 | 6503 | 2754 | 2352 | 0.1428 | |
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| 0.4457 | 4.0 | 5828 | 0.4458 | 0.1757 | 0.1702 | 0.2581 | 0.7419 | 55319 | 6463 | 2805 | 2078 | 0.1376 | |
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| 0.3913 | 5.0 | 7285 | 0.4486 | 0.1774 | 0.1716 | 0.2600 | 0.7400 | 55324 | 6525 | 2738 | 2195 | 0.1414 | |
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| 0.3641 | 6.0 | 8742 | 0.4553 | 0.1764 | 0.1706 | 0.2595 | 0.7405 | 55397 | 6566 | 2624 | 2202 | 0.1378 | |
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| 0.4101 | 7.0 | 10199 | 0.4596 | 0.1770 | 0.1711 | 0.2596 | 0.7404 | 55360 | 6528 | 2699 | 2202 | 0.1387 | |
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| 0.3305 | 8.0 | 11656 | 0.4654 | 0.1783 | 0.1722 | 0.2606 | 0.7394 | 55358 | 6528 | 2701 | 2288 | 0.1393 | |
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| 0.317 | 9.0 | 13113 | 0.4671 | 0.1782 | 0.1720 | 0.2604 | 0.7396 | 55386 | 6524 | 2677 | 2307 | 0.1400 | |
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| 0.3232 | 10.0 | 14570 | 0.4700 | 0.1779 | 0.1718 | 0.2600 | 0.7400 | 55384 | 6510 | 2693 | 2287 | 0.1398 | |
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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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