# t5-qg_squad1-en

## Model description
This model is a *Question Generation* model based on T5-small.
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_squad1-en")

model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qg_squad1-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 = "sv1 </s> {ANSWER} </s> {CONTEXT}"`

## Training data
The model was trained on SQuAD.


### Citation info

```bibtex
@article{scialom2020QuestEval,
  title={QuestEval: Summarization Asks for Fact-based Evaluation},
  author={Scialom, Thomas and Dray, Paul-Alexis and Gallinari, Patrick and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo and Wang, Alex},
  journal={arXiv preprint arXiv:2103.12693},
  year={2021}
}
```
{"language": "en", "license": "mit", "tags": ["qg", "question", "generation", "SQuAD", "metric", "nlg", "t5-small"], "datasets": ["squad"], "widget": [{"text": "sv1 </s> Louis 14 </s> Louis 14 was a French King."}]}
transformers pytorch jax t5 text2text-generation qg question generation SQuAD metric nlg t5-small en dataset:squad license:mit autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qg #question #generation #SQuAD #metric #nlg #t5-small #en #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

# t5-qg_squad1-en

## Model description
This model is a *Question Generation* model based on T5-small.
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 = "sv1 </s> {ANSWER} </s> {CONTEXT}"'

## Training data
The model was trained on SQuAD.


info


# t5-qg_squad1-en ## Model description
This model is a *Question Generation* model based on T5-small.
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 = "sv1 </s> {ANSWER} </s> {CONTEXT}"' ## Training data
The model was trained on SQuAD.


info
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qg #question #generation #SQuAD #metric #nlg #t5-small #en #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # t5-qg_squad1-en ## Model description
This model is a *Question Generation* model based on T5-small.
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 = "sv1 </s> {ANSWER} </s> {CONTEXT}"' ## Training data
The model was trained on SQuAD.


info
passage: TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qg #question #generation #SQuAD #metric #nlg #t5-small #en #dataset-squad #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# t5-qg_squad1-en## Model description
This model is a *Question Generation* model based on T5-small.
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 = "sv1 </s> {ANSWER} </s> {CONTEXT}"'## Training data
The model was trained on SQuAD.


info