README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - t5-small
  - text2text-generation
  - dialog state tracking
  - conversational system
  - task-oriented dialog
datasets:
  - ConvLab/tm1
  - ConvLab/tm2
  - ConvLab/tm3
metrics:
  - Joint Goal Accuracy
  - Slot F1
model-index:
  - name: t5-small-dst-tm1_tm2_tm3
    results:
      - 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: 48.5
            name: JGA
          - type: Slot F1
            value: 81.1
            name: Slot F1
widget:
  - text: |-
      tm1: user: Hi there, could you please help me with an order of Pizza?
      system: Sure, where would you like to order you pizza from?
      user: I would like to order a pizza from Domino's.
  - text: >-
      tm2: user: I need help finding a hotel in New Orleans.

      system: Okay.

      user: I need something that's around $300 a night and it's a five star
      rating.
  - text: |-
      tm3: user: Hi, I'm hoping to see a movie tonight.
      system: Great, I can assist with that. What genre of film do you prefer.
      user: I usually like comedies.
inference:
  parameters:
    max_length: 100

t5-small-dst-tm1_tm2_tm3

This model is a fine-tuned version of t5-small on Taskmaster-1, Taskmaster-2, and Taskmaster-3.

Refer to 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