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--- |
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language: |
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- zh |
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license: apache-2.0 |
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tags: |
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- mt5-small |
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- text2text-generation |
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- natural language understanding |
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- conversational system |
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- task-oriented dialog |
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datasets: |
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- ConvLab/crosswoz |
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metrics: |
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- Dialog acts Accuracy |
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- Dialog acts F1 |
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model-index: |
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- name: mt5-small-nlu-all-crosswoz |
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results: |
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- task: |
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type: text2text-generation |
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name: natural language understanding |
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dataset: |
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type: ConvLab/crosswoz |
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name: CrossWOZ |
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split: test |
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revision: 4a3e56082543ed9eecb9c76ef5eadc1aa0cc5ca0 |
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metrics: |
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- type: Dialog acts Accuracy |
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value: 84.0 |
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name: Accuracy |
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- type: Dialog acts F1 |
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value: 90.1 |
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name: F1 |
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widget: |
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- text: "user: 你好，给我推荐一个评分是5分，价格在100-200元的酒店。" |
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- text: "system: 抱歉，为您搜索了一些经济型酒店都没有健身房。其他类型的一些酒店行吗？比如北京贵都大酒店、北京京仪大酒店这些也是很好的，就是价格高了一些。" |
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inference: |
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parameters: |
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max_length: 100 |
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--- |
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# mt5-small-nlu-all-crosswoz |
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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. |
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Refer to [ConvLab-3](https://github.com/ConvLab/ConvLab-3) for model description and usage. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 256 |
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- optimizer: Adafactor |
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- lr_scheduler_type: linear |
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- num_epochs: 10.0 |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0+cu102 |
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- Datasets 2.3.2 |
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- Tokenizers 0.12.1 |
