readme: ---
  license: cc-by-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - te_dx_jp
  model-index:
  - name: t5-base-TEDxJP-0front-1body-0rear
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # t5-base-TEDxJP-0front-1body-0rear
  
  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.
  It achieves the following results on the evaluation set:
  - Loss: 0.5110
  - Wer: 0.1852
  - Mer: 0.1786
  - Wil: 0.2694
  - Wip: 0.7306
  - Hits: 55023
  - Substitutions: 6739
  - Deletions: 2825
  - Insertions: 2397
  - Cer: 0.1459
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.0001
  - train_batch_size: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Wer    | Mer    | Wil    | Wip    | Hits  | Substitutions | Deletions | Insertions | Cer    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
  | 0.6898        | 1.0   | 1457  | 0.5259          | 0.2378 | 0.2201 | 0.3112 | 0.6888 | 54412 | 6955          | 3220      | 5183       | 0.2118 |
  | 0.5915        | 2.0   | 2914  | 0.4905          | 0.1893 | 0.1824 | 0.2734 | 0.7266 | 54815 | 6756          | 3016      | 2455       | 0.1588 |
  | 0.5414        | 3.0   | 4371  | 0.4812          | 0.1933 | 0.1850 | 0.2748 | 0.7252 | 54989 | 6684          | 2914      | 2885       | 0.1605 |
  | 0.4633        | 4.0   | 5828  | 0.4820          | 0.1847 | 0.1782 | 0.2685 | 0.7315 | 54999 | 6685          | 2903      | 2342       | 0.1451 |
  | 0.4275        | 5.0   | 7285  | 0.4831          | 0.1851 | 0.1785 | 0.2681 | 0.7319 | 55034 | 6630          | 2923      | 2405       | 0.1491 |
  | 0.3977        | 6.0   | 8742  | 0.4903          | 0.1836 | 0.1773 | 0.2676 | 0.7324 | 54996 | 6681          | 2910      | 2264       | 0.1451 |
  | 0.4236        | 7.0   | 10199 | 0.4941          | 0.1853 | 0.1788 | 0.2693 | 0.7307 | 54964 | 6706          | 2917      | 2343       | 0.1451 |
  | 0.3496        | 8.0   | 11656 | 0.5022          | 0.1861 | 0.1794 | 0.2693 | 0.7307 | 54979 | 6661          | 2947      | 2409       | 0.1516 |
  | 0.3439        | 9.0   | 13113 | 0.5081          | 0.1872 | 0.1802 | 0.2709 | 0.7291 | 55016 | 6738          | 2833      | 2519       | 0.1606 |
  | 0.3505        | 10.0  | 14570 | 0.5110          | 0.1852 | 0.1786 | 0.2694 | 0.7306 | 55023 | 6739          | 2825      | 2397       | 0.1459 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu116
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  