# t5-qa_squad2neg-en

## Model description
This model is a *Question Answering* 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 QA only.


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

tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qa_squad2neg-en")

model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qa_squad2neg-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 = "{QUESTION} </s> {CONTEXT}"`

## Training data
The model was trained on: 
- SQuAD-v2
- SQuAD-v2 neg: in addition to the training data of SQuAD-v2, for each answerable example, a negative sampled example has been added with the label *unanswerable*  to help the model learning when the question is not answerable given the context. For more details, see the [paper](https://arxiv.org/abs/2103.12693).


### 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": ["qa", "question", "answering", "SQuAD", "metric", "nlg", "t5-small"], "datasets": ["squad_v2"], "widget": [{"text": "Who was Louis 14? </s> Louis 14 was a French King."}]}
transformers pytorch jax t5 text2text-generation qa question answering SQuAD metric nlg t5-small en dataset:squad_v2 arxiv:2103.12693 license:mit autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qa #question #answering #SQuAD #metric #nlg #t5-small #en #dataset-squad_v2 #arxiv-2103.12693 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

# t5-qa_squad2neg-en

## Model description
This model is a *Question Answering* model based on T5-small. 
It is actually a component of QuestEval metric but can be used independently as it is, for QA 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 = "{QUESTION} </s> {CONTEXT}"'

## Training data
The model was trained on: 
- SQuAD-v2
- SQuAD-v2 neg: in addition to the training data of SQuAD-v2, for each answerable example, a negative sampled example has been added with the label *unanswerable*  to help the model learning when the question is not answerable given the context. For more details, see the paper.


info



# t5-qa_squad2neg-en ## Model description
This model is a *Question Answering* model based on T5-small. 
It is actually a component of QuestEval metric but can be used independently as it is, for QA 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 = "{QUESTION} </s> {CONTEXT}"' ## Training data
The model was trained on: 
- SQuAD-v2
- SQuAD-v2 neg: in addition to the training data of SQuAD-v2, for each answerable example, a negative sampled example has been added with the label *unanswerable*  to help the model learning when the question is not answerable given the context. For more details, see the paper.


info
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qa #question #answering #SQuAD #metric #nlg #t5-small #en #dataset-squad_v2 #arxiv-2103.12693 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # t5-qa_squad2neg-en ## Model description
This model is a *Question Answering* model based on T5-small. 
It is actually a component of QuestEval metric but can be used independently as it is, for QA 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 = "{QUESTION} </s> {CONTEXT}"' ## Training data
The model was trained on: 
- SQuAD-v2
- SQuAD-v2 neg: in addition to the training data of SQuAD-v2, for each answerable example, a negative sampled example has been added with the label *unanswerable*  to help the model learning when the question is not answerable given the context. For more details, see the paper.


info
passage: TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qa #question #answering #SQuAD #metric #nlg #t5-small #en #dataset-squad_v2 #arxiv-2103.12693 #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# t5-qa_squad2neg-en## Model description
This model is a *Question Answering* model based on T5-small. 
It is actually a component of QuestEval metric but can be used independently as it is, for QA 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 = "{QUESTION} </s> {CONTEXT}"'## Training data
The model was trained on: 
- SQuAD-v2
- SQuAD-v2 neg: in addition to the training data of SQuAD-v2, for each answerable example, a negative sampled example has been added with the label *unanswerable*  to help the model learning when the question is not answerable given the context. For more details, see the paper.


info