metadata
language:
- en
license: apache-2.0
tags:
- t5-small
- text2text-generation
- natural language understanding
- conversational system
- task-oriented dialog
datasets:
- ConvLab/tm3
metrics:
- Dialog acts Accuracy
- Dialog acts F1
model-index:
- name: t5-small-nlu-tm3-context3
results:
- task:
type: text2text-generation
name: natural language understanding
dataset:
type: ConvLab/tm3
name: Taskmaster-3
split: test
revision: 910584e5451e2e439bb2a07b8544ecb42ff8835b
metrics:
- type: Dialog acts Accuracy
value: 89
name: Accuracy
- type: Dialog acts F1
value: 85.1
name: F1
widget:
- text: >-
system: OK. And where will you be seeing the movie?
user: In Creek's End, Oregon
system: Creek’s End, Oregon. Got it. Is there a particular movie you have
in mind?
user: Mulan, please. We are taking the kids
- text: >-
system: No problem. It looks like tonight’s remaining showtimes for Mulan
at AMC Mercado 24 are 5:00pm, 7:10pm, and 9:45pm. Which is best for you?
user: I would like the earliest time, 5:00pm
system: Great. And how many tickets?
user: three please
inference:
parameters:
max_length: 100
t5-small-nlu-tm3-context3
This model is a fine-tuned version of t5-small on Taskmaster-3 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