metadata
language:
- en
license: apache-2.0
tags:
- t5-small
- text2text-generation
- natural language understanding
- conversational system
- task-oriented dialog
datasets:
- ConvLab/multiwoz21
metrics:
- Dialog acts Accuracy
- Dialog acts F1
model-index:
- name: t5-small-nlu-multiwoz21-context3
results:
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/multiwoz21
name: MultiWOZ 2.1
split: test
revision: 5f55375edbfe0270c20bcf770751ad982c0e6614
metrics:
- type: Dialog acts Accuracy
value: 82
name: Accuracy
- type: Dialog acts F1
value: 90.3
name: F1
widget:
- text: >-
system: There are 21 restaurants available in the centre of town. How
about a specific type of cuisine?
user: i need to know the food type and postcode and it should also have
mutliple sports
system: I am sorry I do not understand what you just said. Please repeat
in a way that makes sense.
user: get me the food type and the post code
- text: >-
user: I want to find a moderately priced restaurant.
system: I have many options available for you! Is there a certain area or
cuisine that interests you?
user: Yes I would like the restaurant to be located in the center of the
attractions.
inference:
parameters:
max_length: 100
t5-small-nlu-multiwoz21-context3
This model is a fine-tuned version of t5-small on MultiWOZ 2.1 with context window size == 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: 128
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 256
- 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