# t5-weighter_cnndm-en

## Model description
This model is a *Classifier* model based on T5-small, that predicts if a answer / question couple is considered as important fact or not (Is this answer enough relevant to appear in a plausible summary?).
It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is.


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

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

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

## Training data
The model was trained on synthetic data as described in [Questeval: Summarization asks for fact-based evaluation](https://arxiv.org/abs/2103.12693).

### Citation info

```bibtex
@article{scialom2021questeval,
  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", "classification", "question", "answering", "SQuAD", "metric", "nlg", "t5-small"], "datasets": ["squad", "cnndm"], "widget": [{"text": "a Buckingham Palace guard </s> Who felt on a manhole? </s> This is the embarrassing moment a Buckingham Palace guard slipped and fell on a manhole cover in front of hundreds of shocked tourists as he took up position in his sentry box. [...] The Guard comprises two detachments, one each for Buckingham Palace and St James\u2019s Palace, under the command of the Captain of The Queen\u2019s Guard."}]}
transformers pytorch jax t5 text2text-generation qa classification question answering SQuAD metric nlg t5-small en dataset:squad dataset:cnndm arxiv:2103.12693 license:mit autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qa #classification #question #answering #SQuAD #metric #nlg #t5-small #en #dataset-squad #dataset-cnndm #arxiv-2103.12693 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

# t5-weighter_cnndm-en

## Model description
This model is a *Classifier* model based on T5-small, that predicts if a answer / question couple is considered as important fact or not (Is this answer enough relevant to appear in a plausible summary?).
It is actually a component of QuestEval metric but can be used independently as it is.


## 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> {QUESTION} </s> {CONTEXT}"'

## Training data
The model was trained on synthetic data as described in Questeval: Summarization asks for fact-based evaluation.

info


# t5-weighter_cnndm-en ## Model description
This model is a *Classifier* model based on T5-small, that predicts if a answer / question couple is considered as important fact or not (Is this answer enough relevant to appear in a plausible summary?).
It is actually a component of QuestEval metric but can be used independently as it is. ## 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> {QUESTION} </s> {CONTEXT}"' ## Training data
The model was trained on synthetic data as described in Questeval: Summarization asks for fact-based evaluation.

info
TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qa #classification #question #answering #SQuAD #metric #nlg #t5-small #en #dataset-squad #dataset-cnndm #arxiv-2103.12693 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # t5-weighter_cnndm-en ## Model description
This model is a *Classifier* model based on T5-small, that predicts if a answer / question couple is considered as important fact or not (Is this answer enough relevant to appear in a plausible summary?).
It is actually a component of QuestEval metric but can be used independently as it is. ## 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> {QUESTION} </s> {CONTEXT}"' ## Training data
The model was trained on synthetic data as described in Questeval: Summarization asks for fact-based evaluation.

info
passage: TAGS
#transformers #pytorch #jax #t5 #text2text-generation #qa #classification #question #answering #SQuAD #metric #nlg #t5-small #en #dataset-squad #dataset-cnndm #arxiv-2103.12693 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
# t5-weighter_cnndm-en## Model description
This model is a *Classifier* model based on T5-small, that predicts if a answer / question couple is considered as important fact or not (Is this answer enough relevant to appear in a plausible summary?).
It is actually a component of QuestEval metric but can be used independently as it is.## 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> {QUESTION} </s> {CONTEXT}"'## Training data
The model was trained on synthetic data as described in Questeval: Summarization asks for fact-based evaluation.

info