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README.md
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---
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language:
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- en
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license: apache-2.0
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tags:
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- t5-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/tm3
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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: t5-small-nlu-tm3-context3
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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/tm3
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name: Taskmaster-3
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split: test
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revision: 910584e5451e2e439bb2a07b8544ecb42ff8835b
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metrics:
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- type: Dialog acts Accuracy
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value: 89.0
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name: Accuracy
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- type: Dialog acts F1
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value: 85.1
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name: F1
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widget:
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- text: "system: OK. And where will you be seeing the movie?\nuser: In Creek's End, Oregon\nsystem: Creek’s End, Oregon. Got it. Is there a particular movie you have in mind?\nuser: Mulan, please. We are taking the kids"
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- 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?\nuser: I would like the earliest time, 5:00pm\nsystem: Great. And how many tickets?\nuser: three please"
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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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# t5-small-nlu-tm3-context3
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This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on [Taskmaster-3](https://huggingface.co/datasets/ConvLab/tm3) with context window size == 3.
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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: 128
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- eval_batch_size: 64
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- seed: 42
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- gradient_accumulation_steps: 2
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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 4.18.0
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- Pytorch 1.10.2+cu102
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- Datasets 1.18.3
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- Tokenizers 0.11.0
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