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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: "The Eagle </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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## 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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## How to use |
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```python |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qg_webnlg_synth-en") |
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model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qg_webnlg_synth-en") |
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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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`text_input = "{ANSWER} </s> {CONTEXT}"` |
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where CONTEXT is a structured table that is linearised this way: |
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`CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ £ 2 0 - 2 5 ]"` |
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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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### Citation info |
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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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``` |
