readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: flan-t5-large-da-multiwoz_fs0.05
    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. -->
  
  # flan-t5-large-da-multiwoz_fs0.05
  
  This model is a fine-tuned version of [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3984
  - Accuracy: 37.0884
  - Num: 367
  - Gen Len: 15.5232
  
  ## 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: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 48
  - seed: 1799
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Num | Gen Len |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|:-------:|
  | 1.734         | 0.56  | 100  | 0.6414          | 20.7578  | 367 | 12.6294 |
  | 0.7022        | 1.12  | 200  | 0.4979          | 28.9542  | 367 | 14.5041 |
  | 0.6029        | 1.69  | 300  | 0.4452          | 34.0597  | 367 | 14.7302 |
  | 0.5516        | 2.25  | 400  | 0.4306          | 34.5725  | 367 | 14.6703 |
  | 0.5069        | 2.81  | 500  | 0.4162          | 36.2341  | 367 | 14.4142 |
  | 0.5128        | 3.37  | 600  | 0.4061          | 33.6886  | 367 | 14.5286 |
  | 0.4721        | 3.93  | 700  | 0.4003          | 35.4136  | 367 | 14.6567 |
  | 0.48          | 4.49  | 800  | 0.3984          | 37.0884  | 367 | 15.5232 |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  