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---
language:
- zh
license: apache-2.0
tags:
- mt5-small
- text2text-generation
- natural language generation
- conversational system
- task-oriented dialog
datasets:
- ConvLab/crosswoz
metrics:
- Slot Error Rate
- sacrebleu
model-index:
- name: mt5-small-nlg-all-crosswoz
results:
- task:
type: text2text-generation
name: natural language generation
dataset:
type: ConvLab/crosswoz
name: CrossWOZ
split: test
revision: 4a3e56082543ed9eecb9c76ef5eadc1aa0cc5ca0
metrics:
- type: Slot Error Rate
value: 6.9
name: SER
- type: sacrebleu
value: 21.0
name: BLEU
widget:
- text: "[Inform][酒店]([价格][100-200元],[评分][5分]);[greet][General]([][]);[Request][酒店]([名称][])\n\nuser: "
- text: "[Recommend][酒店]([名称][北京京仪大酒店],[名称][北京贵都大酒店]);[Inform][酒店]([酒店设施-健身房-否][]);[NoOffer][酒店]([][])\n\nsystem: "
inference:
parameters:
max_length: 100
---
# mt5-small-nlg-all-crosswoz
This model is a fine-tuned version of [mt5-small](https://huggingface.co/mt5-small) on [CrossWOZ](https://huggingface.co/datasets/ConvLab/crosswoz) both user and system utterances.
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: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adafactor
- lr_scheduler_type: linear
- num_epochs: 10.0
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1 |