# t5-qg_webnlg_synth-en

## Model description
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. 
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QG only.


## How to use
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration

tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qg_webnlg_synth-en")

model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qg_webnlg_synth-en")
```

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):

`text_input = "{ANSWER} </s> {CONTEXT}"`

where `CONTEXT is a structured table that is linearised this way:

`CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ Â£ 2 0 - 2 5 ]"`


## Training data
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).

### Citation info

```bibtex
@article{rebuffel2021data,
  title={Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation},
  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},
  journal={arXiv preprint arXiv:2104.07555},
  year={2021}
}
```
{"language": "en", "license": "mit", "tags": ["qa", "question", "generation", "SQuAD", "data2text", "metric", "nlg", "t5-small"], "datasets": ["squad_v2"], "widget": [{"text": "The Eagle </s> name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ \u00c2\u00a3 2 0 - 2 5 ]"}]}
transformers pytorch jax t5 text2text-generation qa question generation SQuAD data2text metric nlg t5-small en dataset:squad_v2 arxiv:2104.07555 license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qa #question #generation #SQuAD #data2text #metric #nlg #t5-small #en #dataset-squad_v2 #arxiv-2104.07555 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# t5-qg_webnlg_synth-en

## Model description
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. 
It is actually a component of QuestEval metric but can be used independently as it is, for QG only.


## How to use


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):

'text_input = "{ANSWER} </s> {CONTEXT}"'

where 'CONTEXT is a structured table that is linearised this way:

'CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ Â£ 2 0 - 2 5 ]"'


## Training data
The model was trained on synthetic data as described in Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation.

info


# t5-qg_webnlg_synth-en ## Model description
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. 
It is actually a component of QuestEval metric but can be used independently as it is, for QG only. ## How to use


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):

'text_input = "{ANSWER} </s> {CONTEXT}"'

where 'CONTEXT is a structured table that is linearised this way:

'CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ Â£ 2 0 - 2 5 ]"' ## Training data
The model was trained on synthetic data as described in Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation.

info
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qa #question #generation #SQuAD #data2text #metric #nlg #t5-small #en #dataset-squad_v2 #arxiv-2104.07555 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # t5-qg_webnlg_synth-en ## Model description
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. 
It is actually a component of QuestEval metric but can be used independently as it is, for QG only. ## How to use


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):

'text_input = "{ANSWER} </s> {CONTEXT}"'

where 'CONTEXT is a structured table that is linearised this way:

'CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ Â£ 2 0 - 2 5 ]"' ## Training data
The model was trained on synthetic data as described in Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation.

info
passage: TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qa #question #generation #SQuAD #data2text #metric #nlg #t5-small #en #dataset-squad_v2 #arxiv-2104.07555 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# t5-qg_webnlg_synth-en## Model description
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. 
It is actually a component of QuestEval metric but can be used independently as it is, for QG only.## How to use


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):

'text_input = "{ANSWER} </s> {CONTEXT}"'

where 'CONTEXT is a structured table that is linearised this way:

'CONTEXT = "name [ The Eagle ] , eatType [ coffee shop ] , food [ French ] , priceRange [ Â£ 2 0 - 2 5 ]"'## Training data
The model was trained on synthetic data as described in Data-QuestEval: A Referenceless Metric for Data to Text Semantic Evaluation.

info