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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-1context |
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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-1context |
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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: 0.5061 |
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- Wer: 0.1990 |
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- Mer: 0.1913 |
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- Wil: 0.2823 |
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- Wip: 0.7177 |
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- Hits: 55830 |
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- Substitutions: 6943 |
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- Deletions: 3598 |
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- Insertions: 2664 |
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- Cer: 0.1763 |
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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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| 0.7277 | 1.0 | 746 | 0.5799 | 0.2384 | 0.2256 | 0.3188 | 0.6812 | 54323 | 7170 | 4878 | 3777 | 0.2371 | |
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| 0.6278 | 2.0 | 1492 | 0.5254 | 0.2070 | 0.1997 | 0.2905 | 0.7095 | 55045 | 6885 | 4441 | 2412 | 0.1962 | |
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| 0.5411 | 3.0 | 2238 | 0.5076 | 0.2022 | 0.1950 | 0.2858 | 0.7142 | 55413 | 6902 | 4056 | 2463 | 0.1805 | |
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| 0.53 | 4.0 | 2984 | 0.5020 | 0.1979 | 0.1911 | 0.2814 | 0.7186 | 55599 | 6849 | 3923 | 2362 | 0.1761 | |
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| 0.5094 | 5.0 | 3730 | 0.4999 | 0.1987 | 0.1915 | 0.2828 | 0.7172 | 55651 | 6944 | 3776 | 2465 | 0.1742 | |
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| 0.4783 | 6.0 | 4476 | 0.5016 | 0.1985 | 0.1914 | 0.2826 | 0.7174 | 55684 | 6947 | 3740 | 2490 | 0.1753 | |
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| 0.4479 | 7.0 | 5222 | 0.5035 | 0.1976 | 0.1905 | 0.2819 | 0.7181 | 55726 | 6961 | 3684 | 2468 | 0.1733 | |
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| 0.4539 | 8.0 | 5968 | 0.5022 | 0.1967 | 0.1896 | 0.2807 | 0.7193 | 55795 | 6938 | 3638 | 2477 | 0.1729 | |
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| 0.4632 | 9.0 | 6714 | 0.5034 | 0.1991 | 0.1913 | 0.2824 | 0.7176 | 55844 | 6942 | 3585 | 2687 | 0.1758 | |
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| 0.4201 | 10.0 | 7460 | 0.5061 | 0.1990 | 0.1913 | 0.2823 | 0.7177 | 55830 | 6943 | 3598 | 2664 | 0.1763 | |
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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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