readme: ---
  license: cc-by-sa-4.0
  tags:
  - generated_from_trainer
  datasets:
  - te_dx_jp
  model-index:
  - name: t5-base-TEDxJP-0front-1body-3rear
    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-3rear
  
  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: 0.4700
  - Wer: 0.1779
  - Mer: 0.1718
  - Wil: 0.2600
  - Wip: 0.7400
  - Hits: 55384
  - Substitutions: 6510
  - Deletions: 2693
  - Insertions: 2287
  - Cer: 0.1398
  
  ## 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    |
  |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:------:|:-----:|:-------------:|:---------:|:----------:|:------:|
  | 0.6519        | 1.0   | 1457  | 0.4991          | 0.2099 | 0.1985 | 0.2891 | 0.7109 | 54721 | 6807          | 3059      | 3689       | 0.1855 |
  | 0.5507        | 2.0   | 2914  | 0.4589          | 0.1827 | 0.1764 | 0.2653 | 0.7347 | 55094 | 6566          | 2927      | 2305       | 0.1504 |
  | 0.5097        | 3.0   | 4371  | 0.4493          | 0.1797 | 0.1734 | 0.2615 | 0.7385 | 55330 | 6503          | 2754      | 2352       | 0.1428 |
  | 0.4457        | 4.0   | 5828  | 0.4458          | 0.1757 | 0.1702 | 0.2581 | 0.7419 | 55319 | 6463          | 2805      | 2078       | 0.1376 |
  | 0.3913        | 5.0   | 7285  | 0.4486          | 0.1774 | 0.1716 | 0.2600 | 0.7400 | 55324 | 6525          | 2738      | 2195       | 0.1414 |
  | 0.3641        | 6.0   | 8742  | 0.4553          | 0.1764 | 0.1706 | 0.2595 | 0.7405 | 55397 | 6566          | 2624      | 2202       | 0.1378 |
  | 0.4101        | 7.0   | 10199 | 0.4596          | 0.1770 | 0.1711 | 0.2596 | 0.7404 | 55360 | 6528          | 2699      | 2202       | 0.1387 |
  | 0.3305        | 8.0   | 11656 | 0.4654          | 0.1783 | 0.1722 | 0.2606 | 0.7394 | 55358 | 6528          | 2701      | 2288       | 0.1393 |
  | 0.317         | 9.0   | 13113 | 0.4671          | 0.1782 | 0.1720 | 0.2604 | 0.7396 | 55386 | 6524          | 2677      | 2307       | 0.1400 |
  | 0.3232        | 10.0  | 14570 | 0.4700          | 0.1779 | 0.1718 | 0.2600 | 0.7400 | 55384 | 6510          | 2693      | 2287       | 0.1398 |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu116
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  