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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-6rear |
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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-6rear |
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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: 0.4688 |
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- Wer: 0.1755 |
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- Mer: 0.1695 |
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- Wil: 0.2577 |
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- Wip: 0.7423 |
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- Hits: 55504 |
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- Substitutions: 6505 |
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- Deletions: 2578 |
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- Insertions: 2249 |
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- Cer: 0.1373 |
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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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| 0.6426 | 1.0 | 1457 | 0.4936 | 0.2128 | 0.2007 | 0.2903 | 0.7097 | 54742 | 6734 | 3111 | 3899 | 0.1791 | |
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| 0.5519 | 2.0 | 2914 | 0.4535 | 0.1970 | 0.1876 | 0.2747 | 0.7253 | 55096 | 6467 | 3024 | 3233 | 0.1567 | |
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| 0.5007 | 3.0 | 4371 | 0.4465 | 0.1819 | 0.1751 | 0.2628 | 0.7372 | 55359 | 6481 | 2747 | 2522 | 0.1435 | |
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| 0.4374 | 4.0 | 5828 | 0.4417 | 0.1761 | 0.1703 | 0.2582 | 0.7418 | 55399 | 6471 | 2717 | 2184 | 0.1373 | |
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| 0.3831 | 5.0 | 7285 | 0.4459 | 0.1755 | 0.1697 | 0.2570 | 0.7430 | 55465 | 6429 | 2693 | 2214 | 0.1383 | |
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| 0.352 | 6.0 | 8742 | 0.4496 | 0.1755 | 0.1697 | 0.2573 | 0.7427 | 55452 | 6450 | 2685 | 2202 | 0.1374 | |
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| 0.3955 | 7.0 | 10199 | 0.4527 | 0.1766 | 0.1707 | 0.2580 | 0.7420 | 55429 | 6429 | 2729 | 2251 | 0.1392 | |
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| 0.3132 | 8.0 | 11656 | 0.4629 | 0.1764 | 0.1703 | 0.2580 | 0.7420 | 55522 | 6472 | 2593 | 2329 | 0.1380 | |
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| 0.3116 | 9.0 | 13113 | 0.4652 | 0.1755 | 0.1695 | 0.2577 | 0.7423 | 55517 | 6505 | 2565 | 2264 | 0.1371 | |
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| 0.313 | 10.0 | 14570 | 0.4688 | 0.1755 | 0.1695 | 0.2577 | 0.7423 | 55504 | 6505 | 2578 | 2249 | 0.1373 | |
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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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