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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-1body-2context |
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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-1body-2context |
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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: 0.4968 |
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- Wer: 0.1969 |
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- Mer: 0.1895 |
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- Wil: 0.2801 |
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- Wip: 0.7199 |
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- Hits: 55902 |
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- Substitutions: 6899 |
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- Deletions: 3570 |
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- Insertions: 2599 |
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- Cer: 0.1727 |
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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: 64 |
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- eval_batch_size: 8 |
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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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| 0.7136 | 1.0 | 746 | 0.5716 | 0.2512 | 0.2345 | 0.3279 | 0.6721 | 54430 | 7249 | 4692 | 4731 | 0.2344 | |
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| 0.6267 | 2.0 | 1492 | 0.5152 | 0.2088 | 0.2005 | 0.2917 | 0.7083 | 55245 | 6949 | 4177 | 2732 | 0.2009 | |
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| 0.5416 | 3.0 | 2238 | 0.4969 | 0.2025 | 0.1948 | 0.2851 | 0.7149 | 55575 | 6871 | 3925 | 2646 | 0.1802 | |
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| 0.5223 | 4.0 | 2984 | 0.4915 | 0.1989 | 0.1917 | 0.2816 | 0.7184 | 55652 | 6826 | 3893 | 2481 | 0.1754 | |
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| 0.4985 | 5.0 | 3730 | 0.4929 | 0.1991 | 0.1916 | 0.2814 | 0.7186 | 55759 | 6828 | 3784 | 2603 | 0.1753 | |
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| 0.4675 | 6.0 | 4476 | 0.4910 | 0.1969 | 0.1897 | 0.2799 | 0.7201 | 55834 | 6859 | 3678 | 2534 | 0.1756 | |
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| 0.445 | 7.0 | 5222 | 0.4940 | 0.1955 | 0.1884 | 0.2782 | 0.7218 | 55881 | 6821 | 3669 | 2485 | 0.1712 | |
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| 0.4404 | 8.0 | 5968 | 0.4932 | 0.1979 | 0.1903 | 0.2801 | 0.7199 | 55881 | 6828 | 3662 | 2643 | 0.1742 | |
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| 0.4525 | 9.0 | 6714 | 0.4951 | 0.1968 | 0.1893 | 0.2799 | 0.7201 | 55939 | 6897 | 3535 | 2632 | 0.1740 | |
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| 0.4077 | 10.0 | 7460 | 0.4968 | 0.1969 | 0.1895 | 0.2801 | 0.7199 | 55902 | 6899 | 3570 | 2599 | 0.1727 | |
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### Framework versions |
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- Transformers 4.12.5 |
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- Pytorch 1.10.0+cu102 |
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- Datasets 1.15.1 |
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- Tokenizers 0.10.3 |
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