readme: ---
  license: cc-by-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - te_dx_jp
  model-index:
  - name: t5-base-TEDxJP-5front-1body-5rear
    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-5front-1body-5rear
  
  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: 0.4383
  - Wer: 0.1697
  - Mer: 0.1641
  - Wil: 0.2500
  - Wip: 0.7500
  - Hits: 55852
  - Substitutions: 6314
  - Deletions: 2421
  - Insertions: 2228
  - Cer: 0.1328
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
  | 0.6185        | 1.0   | 1457  | 0.4683          | 0.1948 | 0.1863 | 0.2758 | 0.7242 | 54959 | 6658          | 2970      | 2956       | 0.1682 |
  | 0.5149        | 2.0   | 2914  | 0.4280          | 0.1773 | 0.1713 | 0.2591 | 0.7409 | 55376 | 6468          | 2743      | 2238       | 0.1426 |
  | 0.4705        | 3.0   | 4371  | 0.4173          | 0.1743 | 0.1682 | 0.2552 | 0.7448 | 55680 | 6418          | 2489      | 2351       | 0.1387 |
  | 0.4023        | 4.0   | 5828  | 0.4114          | 0.1713 | 0.1656 | 0.2515 | 0.7485 | 55751 | 6313          | 2523      | 2230       | 0.1335 |
  | 0.3497        | 5.0   | 7285  | 0.4162          | 0.1722 | 0.1662 | 0.2522 | 0.7478 | 55787 | 6331          | 2469      | 2323       | 0.1365 |
  | 0.3246        | 6.0   | 8742  | 0.4211          | 0.1714 | 0.1655 | 0.2513 | 0.7487 | 55802 | 6310          | 2475      | 2284       | 0.1367 |
  | 0.3492        | 7.0   | 10199 | 0.4282          | 0.1711 | 0.1652 | 0.2514 | 0.7486 | 55861 | 6350          | 2376      | 2325       | 0.1341 |
  | 0.2788        | 8.0   | 11656 | 0.4322          | 0.1698 | 0.1641 | 0.2502 | 0.7498 | 55883 | 6342          | 2362      | 2265       | 0.1327 |
  | 0.2801        | 9.0   | 13113 | 0.4362          | 0.1710 | 0.1652 | 0.2514 | 0.7486 | 55828 | 6351          | 2408      | 2288       | 0.1352 |
  | 0.2773        | 10.0  | 14570 | 0.4383          | 0.1697 | 0.1641 | 0.2500 | 0.7500 | 55852 | 6314          | 2421      | 2228       | 0.1328 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu116
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  