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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: 0.4373 |
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- Wer: 0.1699 |
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- Mer: 0.1642 |
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- Wil: 0.2499 |
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- Wip: 0.7501 |
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- Hits: 55848 |
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- Substitutions: 6297 |
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- Deletions: 2442 |
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- Insertions: 2236 |
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- Cer: 0.1360 |
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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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| 0.5812 | 1.0 | 1457 | 0.4658 | 0.2393 | 0.2197 | 0.3076 | 0.6924 | 54882 | 6717 | 2988 | 5750 | 0.2187 | |
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| 0.5253 | 2.0 | 2914 | 0.4264 | 0.1832 | 0.1756 | 0.2632 | 0.7368 | 55549 | 6498 | 2540 | 2793 | 0.1520 | |
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| 0.4412 | 3.0 | 4371 | 0.4161 | 0.1728 | 0.1670 | 0.2535 | 0.7465 | 55665 | 6363 | 2559 | 2240 | 0.1360 | |
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| 0.3465 | 4.0 | 5828 | 0.4155 | 0.1706 | 0.1650 | 0.2504 | 0.7496 | 55756 | 6266 | 2565 | 2186 | 0.1356 | |
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| 0.3575 | 5.0 | 7285 | 0.4196 | 0.1696 | 0.1642 | 0.2498 | 0.7502 | 55781 | 6283 | 2523 | 2151 | 0.1358 | |
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| 0.3556 | 6.0 | 8742 | 0.4164 | 0.1687 | 0.1632 | 0.2487 | 0.7513 | 55857 | 6274 | 2456 | 2167 | 0.1341 | |
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| 0.3145 | 7.0 | 10199 | 0.4245 | 0.1705 | 0.1648 | 0.2504 | 0.7496 | 55819 | 6297 | 2471 | 2244 | 0.1355 | |
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| 0.3074 | 8.0 | 11656 | 0.4266 | 0.1693 | 0.1639 | 0.2494 | 0.7506 | 55799 | 6274 | 2514 | 2148 | 0.1358 | |
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| 0.269 | 9.0 | 13113 | 0.4352 | 0.1693 | 0.1637 | 0.2492 | 0.7508 | 55878 | 6288 | 2421 | 2225 | 0.1346 | |
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| 0.3162 | 10.0 | 14570 | 0.4373 | 0.1699 | 0.1642 | 0.2499 | 0.7501 | 55848 | 6297 | 2442 | 2236 | 0.1360 | |
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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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