readme: ---
  license: cc-by-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - te_dx_jp
  model-index:
  - name: t5-base-TEDxJP-4front-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-4front-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: 0.4643
  - Wer: 0.1751
  - Mer: 0.1690
  - Wil: 0.2562
  - Wip: 0.7438
  - Hits: 55598
  - Substitutions: 6434
  - Deletions: 2555
  - Insertions: 2317
  - Cer: 0.1374
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
  | 0.6492        | 1.0   | 1457  | 0.4952          | 0.2272 | 0.2114 | 0.3015 | 0.6985 | 54739 | 6847          | 3001      | 4827       | 0.2013 |
  | 0.5556        | 2.0   | 2914  | 0.4456          | 0.1899 | 0.1818 | 0.2686 | 0.7314 | 55189 | 6420          | 2978      | 2864       | 0.1558 |
  | 0.4942        | 3.0   | 4371  | 0.4423          | 0.1814 | 0.1743 | 0.2614 | 0.7386 | 55493 | 6437          | 2657      | 2623       | 0.1457 |
  | 0.4326        | 4.0   | 5828  | 0.4361          | 0.1749 | 0.1690 | 0.2561 | 0.7439 | 55542 | 6419          | 2626      | 2249       | 0.1362 |
  | 0.3867        | 5.0   | 7285  | 0.4395          | 0.1752 | 0.1692 | 0.2559 | 0.7441 | 55542 | 6378          | 2667      | 2270       | 0.1374 |
  | 0.3501        | 6.0   | 8742  | 0.4487          | 0.1751 | 0.1691 | 0.2565 | 0.7435 | 55598 | 6448          | 2541      | 2323       | 0.1366 |
  | 0.3835        | 7.0   | 10199 | 0.4494          | 0.1744 | 0.1685 | 0.2556 | 0.7444 | 55594 | 6416          | 2577      | 2274       | 0.1378 |
  | 0.3013        | 8.0   | 11656 | 0.4580          | 0.1744 | 0.1685 | 0.2563 | 0.7437 | 55570 | 6467          | 2550      | 2248       | 0.1366 |
  | 0.3126        | 9.0   | 13113 | 0.4598          | 0.1749 | 0.1689 | 0.2564 | 0.7436 | 55571 | 6447          | 2569      | 2281       | 0.1376 |
  | 0.3089        | 10.0  | 14570 | 0.4643          | 0.1751 | 0.1690 | 0.2562 | 0.7438 | 55598 | 6434          | 2555      | 2317       | 0.1374 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu116
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  