readme: ---
  language:
  - en
  license: apache-2.0
  tags:
  - t5-small
  - text2text-generation
  - dialog state tracking
  - conversational system
  - task-oriented dialog
  datasets:
  - ConvLab/multiwoz21
  - ConvLab/sgd
  - ConvLab/tm1
  - ConvLab/tm2
  - ConvLab/tm3
  metrics:
  - Joint Goal Accuracy
  - Slot F1
  
  model-index:
  - name: t5-small-dst-multiwoz21_sgd_tm1_tm2_tm3
    results:
    - task:
        type: text2text-generation
        name: dialog state tracking
      dataset:
        type: ConvLab/multiwoz21
        name: MultiWOZ 2.1
        split: test
        revision: 5f55375edbfe0270c20bcf770751ad982c0e6614
      metrics:
        - type: Joint Goal Accuracy
          value: 53.1
          name: JGA
        - type: Slot F1
          value: 91.9
          name: Slot F1
    - task:
        type: text2text-generation
        name: dialog state tracking
      dataset:
        type: ConvLab/sgd
        name: SGD
        split: test
        revision: 6e8c79b888b21cc658cf9c0ce128d263241cf70f
      metrics:
        - type: Joint Goal Accuracy
          value: 20.6
          name: JGA
        - type: Slot F1
          value: 60.0
          name: Slot F1
    - task:
        type: text2text-generation
        name: dialog state tracking
      dataset:
        type: ConvLab/tm1, ConvLab/tm2, ConvLab/tm3
        name: TM1+TM2+TM3
        split: test
      metrics:
        - type: Joint Goal Accuracy
          value: 48.6
          name: JGA
        - type: Slot F1
          value: 81.0
          name: Slot F1
  
  widget:
  - text: "multiwoz21: user: I would like a taxi from Saint John's college to Pizza Hut Fen Ditton.\nsystem: What time do you want to leave and what time do you want to arrive by?\nuser: I want to leave after 17:15."
    example_title: "MultiWOZ 2.1"
  - text: "sgd: 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?"
    example_title: "Schema-Guided Dialog"
  - text: "tm1: user: Hi there, could you please help me with an order of Pizza?\nsystem: Sure, where would you like to order you pizza from?\nuser: I would like to order a pizza from Domino's."
    example_title: "Taskmaster-1"
  - text: "tm2: user: I need help finding a hotel in New Orleans.\nsystem: Okay.\nuser: I need something that's around $300 a night and it's a five star rating."
    example_title: "Taskmaster-2"
  - text: "tm3: user: Hi, I'm hoping to see a movie tonight.\nsystem: Great, I can assist with that. What genre of film do you prefer.\nuser: I usually like comedies."
    example_title: "Taskmaster-3"
  
  inference:
    parameters:
      max_length: 100
  
  ---
  
  # t5-small-dst-multiwoz21_sgd_tm1_tm2_tm3
  
  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), [Schema-Guided Dialog](https://huggingface.co/datasets/ConvLab/sgd), [Taskmaster-1](https://huggingface.co/datasets/ConvLab/tm1), [Taskmaster-2](https://huggingface.co/datasets/ConvLab/tm2), and [Taskmaster-3](https://huggingface.co/datasets/ConvLab/tm3).
  
  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
  