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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-4front-1body-4rear |
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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-4front-1body-4rear |
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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: 0.4398 |
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- Wer: 0.1697 |
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- Mer: 0.1641 |
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- Wil: 0.2506 |
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- Wip: 0.7494 |
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- Hits: 55824 |
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- Substitutions: 6360 |
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- Deletions: 2403 |
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- Insertions: 2197 |
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- Cer: 0.1335 |
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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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| 0.5976 | 1.0 | 1457 | 0.4725 | 0.2267 | 0.2107 | 0.2985 | 0.7015 | 54877 | 6654 | 3056 | 4935 | 0.2024 | |
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| 0.4849 | 2.0 | 2914 | 0.4229 | 0.1789 | 0.1726 | 0.2590 | 0.7410 | 55401 | 6358 | 2828 | 2371 | 0.1432 | |
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| 0.4632 | 3.0 | 4371 | 0.4167 | 0.1725 | 0.1667 | 0.2529 | 0.7471 | 55723 | 6347 | 2517 | 2280 | 0.1343 | |
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| 0.3981 | 4.0 | 5828 | 0.4146 | 0.1716 | 0.1658 | 0.2521 | 0.7479 | 55784 | 6355 | 2448 | 2282 | 0.1336 | |
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| 0.3551 | 5.0 | 7285 | 0.4189 | 0.1713 | 0.1652 | 0.2512 | 0.7488 | 55909 | 6340 | 2338 | 2388 | 0.1345 | |
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| 0.3253 | 6.0 | 8742 | 0.4238 | 0.1714 | 0.1656 | 0.2514 | 0.7486 | 55805 | 6315 | 2467 | 2291 | 0.1359 | |
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| 0.308 | 7.0 | 10199 | 0.4292 | 0.1703 | 0.1645 | 0.2506 | 0.7494 | 55862 | 6341 | 2384 | 2271 | 0.1353 | |
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| 0.324 | 8.0 | 11656 | 0.4304 | 0.1693 | 0.1637 | 0.2497 | 0.7503 | 55856 | 6324 | 2407 | 2205 | 0.1336 | |
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| 0.2861 | 9.0 | 13113 | 0.4356 | 0.1694 | 0.1639 | 0.2501 | 0.7499 | 55814 | 6336 | 2437 | 2166 | 0.1332 | |
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| 0.2788 | 10.0 | 14570 | 0.4398 | 0.1697 | 0.1641 | 0.2506 | 0.7494 | 55824 | 6360 | 2403 | 2197 | 0.1335 | |
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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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