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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: 0.4371 |
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- Wer: 0.1693 |
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- Mer: 0.1636 |
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- Wil: 0.2493 |
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- Wip: 0.7507 |
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- Hits: 55894 |
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- Substitutions: 6298 |
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- Deletions: 2395 |
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- Insertions: 2240 |
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- Cer: 0.1325 |
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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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| 0.6129 | 1.0 | 1457 | 0.4667 | 0.2078 | 0.1962 | 0.2857 | 0.7143 | 54967 | 6724 | 2896 | 3799 | 0.1785 | |
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| 0.5027 | 2.0 | 2914 | 0.4202 | 0.1767 | 0.1705 | 0.2573 | 0.7427 | 55529 | 6397 | 2661 | 2356 | 0.1393 | |
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| 0.486 | 3.0 | 4371 | 0.4128 | 0.1720 | 0.1667 | 0.2522 | 0.7478 | 55546 | 6265 | 2776 | 2068 | 0.1352 | |
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| 0.4381 | 4.0 | 5828 | 0.4077 | 0.1726 | 0.1664 | 0.2515 | 0.7485 | 55866 | 6263 | 2458 | 2427 | 0.1363 | |
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| 0.3859 | 5.0 | 7285 | 0.4151 | 0.1703 | 0.1644 | 0.2502 | 0.7498 | 55873 | 6310 | 2404 | 2282 | 0.1322 | |
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| 0.3091 | 6.0 | 8742 | 0.4172 | 0.1709 | 0.1649 | 0.2501 | 0.7499 | 55913 | 6267 | 2407 | 2365 | 0.1386 | |
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| 0.3012 | 7.0 | 10199 | 0.4258 | 0.1697 | 0.1637 | 0.2493 | 0.7507 | 55996 | 6304 | 2287 | 2369 | 0.1325 | |
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| 0.2837 | 8.0 | 11656 | 0.4275 | 0.1696 | 0.1639 | 0.2499 | 0.7501 | 55858 | 6325 | 2404 | 2222 | 0.1328 | |
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| 0.2625 | 9.0 | 13113 | 0.4339 | 0.1696 | 0.1639 | 0.2496 | 0.7504 | 55880 | 6296 | 2411 | 2248 | 0.1327 | |
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| 0.2466 | 10.0 | 14570 | 0.4371 | 0.1693 | 0.1636 | 0.2493 | 0.7507 | 55894 | 6298 | 2395 | 2240 | 0.1325 | |
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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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