README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - t5-small
  - text2text-generation
  - dialogue generation
  - conversational system
  - task-oriented dialog
datasets:
  - ConvLab/multiwoz21
metrics:
  - LM loss
model-index:
  - name: t5-small-goal2dialogue-multiwoz21
    results:
      - task:
          type: text2text-generation
          name: dialogue generation
        dataset:
          type: ConvLab/multiwoz21
          name: MultiWOZ 2.1
          split: validation
          revision: 5f55375edbfe0270c20bcf770751ad982c0e6614
        metrics:
          - type: Language model loss
            value: 1.5253684520721436
            name: LM loss
      - task:
          type: text2text-generation
          name: dialogue generation
        dataset:
          type: ConvLab/multiwoz21
          name: MultiWOZ 2.1
          split: test
          revision: 5f55375edbfe0270c20bcf770751ad982c0e6614
        metrics:
          - type: Language model loss
            value: 1.515929937362671
            name: LM loss
widget:
  - text: >-
      You are traveling to Cambridge and looking forward to try local
      restaurants. You are looking for a particular attraction. Its name is
      called nusha. Make sure you get postcode and address. You are also looking
      for a place to dine. The restaurant should be in the expensive price range
      and should serve indian food. The restaurant should be in the centre. Make
      sure you get address
  - text: >-
      You want to book a taxi. The taxi should go to pizza hut fen ditton and
      should depart from saint john's college. The taxi should leave after
      17:15. Make sure you get car type and contact number
inference:
  parameters:
    max_length: 1024

t5-small-goal2dialogue-multiwoz21

This model is a fine-tuned version of t5-small on MultiWOZ 2.1.

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: 32
• eval_batch_size: 64
• seed: 42
• distributed_type: multi-GPU
• gradient_accumulation_steps: 4
• total_train_batch_size: 128
• 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