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Update README.md

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@@ -3,7 +3,7 @@ language: en
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  tags:
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  - qa
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  - classification
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- - question
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  - answering
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  - SQuAD
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  - metric
@@ -16,7 +16,7 @@ datasets:
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  model-index:
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  - name: t5-weighter_cnndm-en
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  results:
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- - task:
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  name: Classification
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  type: Question Weighter
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  widget:
@@ -25,7 +25,7 @@ widget:
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  # t5-weighter_cnndm-en
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  ## Model description
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- This model is a *Classifier* model based on T5-small, that predicts if a question is asking about important facts or not.
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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.
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@@ -42,7 +42,6 @@ You can play with the model using the inference API, the text input format shoul
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  `text_input = "{ANSWER} </s> {QUESTION} </s> {CONTEXT}"`
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-
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  ## Training data
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  The model was trained on synthetic data as described in [Questeval: Summarization asks for fact-based evaluation](https://arxiv.org/abs/2103.12693).
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  tags:
4
  - qa
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  - classification
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+ - question
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  - answering
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  - SQuAD
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  - metric
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  model-index:
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  - name: t5-weighter_cnndm-en
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  results:
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+ - task:
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  name: Classification
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  type: Question Weighter
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  widget:
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  # t5-weighter_cnndm-en
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  ## Model description
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+ 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?).
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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.
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  `text_input = "{ANSWER} </s> {QUESTION} </s> {CONTEXT}"`
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  ## Training data
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  The model was trained on synthetic data as described in [Questeval: Summarization asks for fact-based evaluation](https://arxiv.org/abs/2103.12693).
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