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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-3context |
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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-3context |
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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: 0.4926 |
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- Wer: 0.1968 |
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- Mer: 0.1894 |
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- Wil: 0.2793 |
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- Wip: 0.7207 |
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- Hits: 55899 |
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- Substitutions: 6836 |
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- Deletions: 3636 |
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- Insertions: 2590 |
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- Cer: 0.1733 |
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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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| 0.7082 | 1.0 | 746 | 0.5637 | 0.2626 | 0.2430 | 0.3355 | 0.6645 | 54301 | 7195 | 4875 | 5358 | 0.2552 | |
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| 0.6213 | 2.0 | 1492 | 0.5150 | 0.2068 | 0.1994 | 0.2899 | 0.7101 | 55107 | 6861 | 4403 | 2462 | 0.1866 | |
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| 0.5331 | 3.0 | 2238 | 0.4945 | 0.2038 | 0.1958 | 0.2858 | 0.7142 | 55551 | 6845 | 3975 | 2705 | 0.1816 | |
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| 0.5185 | 4.0 | 2984 | 0.4880 | 0.2003 | 0.1929 | 0.2831 | 0.7169 | 55639 | 6860 | 3872 | 2563 | 0.1779 | |
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| 0.4963 | 5.0 | 3730 | 0.4858 | 0.1988 | 0.1912 | 0.2810 | 0.7190 | 55837 | 6838 | 3696 | 2662 | 0.1772 | |
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| 0.4625 | 6.0 | 4476 | 0.4885 | 0.1964 | 0.1894 | 0.2799 | 0.7201 | 55785 | 6875 | 3711 | 2448 | 0.1720 | |
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| 0.4416 | 7.0 | 5222 | 0.4898 | 0.1962 | 0.1890 | 0.2788 | 0.7212 | 55870 | 6819 | 3682 | 2522 | 0.1726 | |
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| 0.4287 | 8.0 | 5968 | 0.4894 | 0.1968 | 0.1894 | 0.2790 | 0.7210 | 55889 | 6804 | 3678 | 2580 | 0.1743 | |
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| 0.4457 | 9.0 | 6714 | 0.4909 | 0.1964 | 0.1891 | 0.2792 | 0.7208 | 55919 | 6858 | 3594 | 2586 | 0.1739 | |
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| 0.4068 | 10.0 | 7460 | 0.4926 | 0.1968 | 0.1894 | 0.2793 | 0.7207 | 55899 | 6836 | 3636 | 2590 | 0.1733 | |
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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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