readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - t5-small
  - text2text-generation
  - natural language understanding
  - conversational system
  - task-oriented dialog
  datasets:
  - ConvLab/multiwoz21
  metrics:
  - Dialog acts Accuracy
  - Dialog acts F1
  
  model-index:
  - name: t5-small-nlu-multiwoz21-context3
    results:
    - task:
        type: text2text-generation
        name: natural language understanding
      dataset:
        type: ConvLab/multiwoz21
        name: MultiWOZ 2.1
        split: test
        revision: 5f55375edbfe0270c20bcf770751ad982c0e6614
      metrics:
        - type: Dialog acts Accuracy
          value: 82.0
          name: Accuracy
        - type: Dialog acts F1
          value: 90.3
          name: F1
  
  widget:
  - text: "system: There are 21 restaurants available in the centre of town. How about a specific type of cuisine?\nuser: i need to know the food type and postcode and it should also have mutliple sports\nsystem: I am sorry I do not understand what you just said. Please repeat in a way that makes sense. \nuser: get me the food type and the post code"
  - text: "user: I want to find a moderately priced restaurant. \nsystem: I have many options available for you! Is there a certain area or cuisine that interests you?\nuser: Yes I would like the restaurant to be located in the center of the attractions. "
  
  inference:
    parameters:
      max_length: 100
  
  ---
  
  # t5-small-nlu-multiwoz21-context3
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [MultiWOZ 2.1](https://huggingface.co/datasets/ConvLab/multiwoz21) with context window size == 3.
  
  Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage.
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 0.001
  - train_batch_size: 128
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 256
  - optimizer: Adafactor
  - lr_scheduler_type: linear
  - num_epochs: 10.0
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.2+cu102
  - Datasets 1.18.3
  - Tokenizers 0.11.0
  