--- language: - en license: apache-2.0 tags: - t5-small - text2text-generation - natural language generation - conversational system - task-oriented dialog datasets: - ConvLab/sgd - ConvLab/tm1 - ConvLab/tm2 - ConvLab/tm3 - ConvLab/multiwoz21 metrics: - Slot Error Rate - sacrebleu model-index: - name: t5-small-nlg-multiwoz21_sgd_tm1_tm2_tm3 results: - task: type: text2text-generation name: natural language generation dataset: type: ConvLab/multiwoz21 name: MultiWOZ 2.1 split: test revision: 5f55375edbfe0270c20bcf770751ad982c0e6614 metrics: - type: Slot Error Rate value: 3.2 name: SER - type: sacrebleu value: 35.6 name: BLEU - task: type: text2text-generation name: natural language generation dataset: type: ConvLab/sgd name: SGD split: test revision: 6e8c79b888b21cc658cf9c0ce128d263241cf70f metrics: - type: Slot Error Rate value: 8.3 name: SER - type: sacrebleu value: 29.9 name: BLEU - task: type: text2text-generation name: natural language generation dataset: type: ConvLab/tm1, ConvLab/tm2, ConvLab/tm3 name: TM1+TM2+TM3 split: test metrics: - type: Slot Error Rate value: 2.0 name: SER - type: sacrebleu value: 51.3 name: BLEU widget: - text: "[inform][restaurant]([area][centre],[food][Indian],[choice][nine]);[request][restaurant]([price range][])\n\nsystem: " example_title: "MultiWOZ 2.1" - text: "sgd: [confirm][Restaurants_2]([number_of_seats][2],[restaurant_name][P.f. Chang's],[location][Corte Madera],[time][12 pm],[date][March 8th])\n\nsystem: " example_title: "Schema-Guided Dialog" - text: "tm1: [inform][pizza_ordering]([name.store][Domino's])\n\nsystem: " example_title: "Taskmaster-1" - text: "tm2: [inform][restaurant-search]([name.restaurant][Via 313, the Violet Crown Social Club],[price_range][$8 per slice])\n\nsystem: " example_title: "Taskmaster-2" - text: "tm3: [inform][movie]([name.movie][Star Wars],[name.movie][The Grudge])\n\nsystem: " example_title: "Taskmaster-3" inference: parameters: max_length: 100 --- # t5-small-nlg-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: 8 - total_train_batch_size: 512 - 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