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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-10context |
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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-10context |
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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: 0.3833 |
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- Wer: 0.1983 |
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- Mer: 0.1900 |
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- Wil: 0.2778 |
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- Wip: 0.7222 |
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- Hits: 56229 |
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- Substitutions: 6686 |
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- Deletions: 3593 |
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- Insertions: 2909 |
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- Cer: 0.1823 |
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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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| 0.5641 | 1.0 | 746 | 0.4426 | 0.2336 | 0.2212 | 0.3143 | 0.6857 | 54711 | 7183 | 4614 | 3742 | 0.2238 | |
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| 0.4867 | 2.0 | 1492 | 0.4017 | 0.2045 | 0.1972 | 0.2863 | 0.7137 | 55378 | 6764 | 4366 | 2470 | 0.1853 | |
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| 0.4257 | 3.0 | 2238 | 0.3831 | 0.2008 | 0.1933 | 0.2826 | 0.7174 | 55715 | 6788 | 4005 | 2560 | 0.1784 | |
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| 0.4038 | 4.0 | 2984 | 0.3797 | 0.1963 | 0.1890 | 0.2776 | 0.7224 | 56028 | 6731 | 3749 | 2578 | 0.1748 | |
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| 0.3817 | 5.0 | 3730 | 0.3769 | 0.1944 | 0.1877 | 0.2758 | 0.7242 | 55926 | 6663 | 3919 | 2345 | 0.1730 | |
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| 0.3467 | 6.0 | 4476 | 0.3806 | 0.2111 | 0.2002 | 0.2876 | 0.7124 | 56082 | 6688 | 3738 | 3616 | 0.1916 | |
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| 0.3361 | 7.0 | 5222 | 0.3797 | 0.1977 | 0.1897 | 0.2780 | 0.7220 | 56173 | 6721 | 3614 | 2816 | 0.1785 | |
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| 0.3107 | 8.0 | 5968 | 0.3814 | 0.1993 | 0.1910 | 0.2792 | 0.7208 | 56167 | 6720 | 3621 | 2916 | 0.1839 | |
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| 0.3141 | 9.0 | 6714 | 0.3820 | 0.1991 | 0.1907 | 0.2787 | 0.7213 | 56201 | 6709 | 3598 | 2933 | 0.1859 | |
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| 0.3122 | 10.0 | 7460 | 0.3833 | 0.1983 | 0.1900 | 0.2778 | 0.7222 | 56229 | 6686 | 3593 | 2909 | 0.1823 | |
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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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