readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - t5-small
  - text2text-generation
  - dialog state tracking
  - conversational system
  - task-oriented dialog
  datasets:
  - ConvLab/sgd
  metrics:
  - Joint Goal Accuracy
  - Slot F1
  
  model-index:
  - name: t5-small-dst-sgd
    results:
    - task:
        type: text2text-generation
        name: dialog state tracking
      dataset:
        type: ConvLab/sgd
        name: SGD
        split: test
        revision: 6e8c79b888b21cc658cf9c0ce128d263241cf70f
      metrics:
        - type: Joint Goal Accuracy
          value: 20.1
          name: JGA
        - type: Slot F1
          value: 58.5
          name: Slot F1
  
  widget:
  - text: "user: Hi, could you get me a restaurant booking on the 8th please?\nsystem: Any preference on the restaurant, location and time?\nuser: Could you get me a reservation at P.f. Chang's in Corte Madera at afternoon 12?"
  - text: "user: I need to book a dinner reservation for a date. Help me reserve a table at a restaurant.\nsystem: What time and location do you have in mind?\nuser: Something around 8 in the night should be fine. Oh, and look in the San Jose area."
  
  inference:
    parameters:
      max_length: 100
  
  ---
  
  # t5-small-dst-sgd
  
  This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd).
  
  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: 64
  - eval_batch_size: 64
  - seed: 42
  - gradient_accumulation_steps: 2
  - total_train_batch_size: 128
  - optimizer: Adafactor
  - lr_scheduler_type: linear
  - num_epochs: 10.0
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  