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+ ---
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+ language: en
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+ tags:
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+ - qa
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+ - question
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+ - generation
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+ - SQuAD
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+ - data2text
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+ - metric
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+ - nlg
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+ - t5-small
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+ license: mit
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+ datasets:
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+ - squad_v2
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+ model-index:
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+ - name: t5-qg_webnlg_synth-en
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+ results:
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+ - task:
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+ name: Data Question Generation
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+ type: Text To Text Generation
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+ widget:
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+ - text: "coffee shop </s> name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"
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+ ---
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+ # t5-qg_webnlg_synth-en
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+
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+ ## Model description
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+ This model is a *Data Question Generation* model based on T5-small, that generates questions given a structured table as input and the conditioned answer.
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+ It is actually a component of [QuestEval](https://github.com/recitalAI/QuestEval) metric but can be used independently as it is, for QG only.
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+
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+
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+ ## How to use
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+ ```python
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+ from transformers import T5Tokenizer, T5ForConditionalGeneration
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+
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+ tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qg_webnlg_synth-en")
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+
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+ model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qg_webnlg_synth-en")
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+ ```
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+
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+ You can play with the model using the inference API, the text input format should follow this template (accordingly to the training stage of the model):
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+
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+ `text_input = "{ANSWER} </s> {CONTEXT}"`
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+
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+ where CONTEXT is a structured table that is linearised this way:
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+
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+ `CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"`
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+
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+
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+ ## Training data
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+ The model was trained on synthetic data as described in [Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation](https://arxiv.org/abs/2104.07555).
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+
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+ ### Citation info
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+
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+ ```bibtex
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+ @article{rebuffel2021data,
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+ title={Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation},
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+ author={Rebuffel, Cl{\\'e}ment and Scialom, Thomas and Soulier, Laure and Piwowarski, Benjamin and Lamprier, Sylvain and Staiano, Jacopo and Scoutheeten, Geoffrey and Gallinari, Patrick},
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+ journal={arXiv preprint arXiv:2104.07555},
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+ year={2021}
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+ }
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+ }
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+ ```