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--- |
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language: en |
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tags: |
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- qa |
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- question |
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- answering |
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- SQuAD |
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- metric |
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- nlg |
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- t5-small |
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license: mit |
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datasets: |
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- squad_v2 |
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model-index: |
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- name: t5-qa_squad2neg-en |
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results: |
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- task: |
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name: Question Answering |
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type: extractive-qa |
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widget: |
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- text: "Who was Louis 14? </s> Louis 14 was a French King." |
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--- |
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# t5-qa_squad2neg-en |
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## Model description |
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This model is a *Question Answering* model based on T5-small. |
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It is actually a component of [QuestEval](https://github.com/ThomasScialom/QuestEval) metric but can be used independently as it is, for QA only. |
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## How to use |
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```python |
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from transformers import T5Tokenizer, T5ForConditionalGeneration |
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tokenizer = T5Tokenizer.from_pretrained("ThomasNLG/t5-qa_squad2neg-en") |
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model = T5ForConditionalGeneration.from_pretrained("ThomasNLG/t5-qa_squad2neg-en") |
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``` |
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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): |
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`text_input = "{QUESTION} </s> {CONTEXT}"` |
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## Training data |
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The model was trained on: |
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- SQuAD-v2 |
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- 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). |
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### Citation info |
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```bibtex |
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@article{scialom2020QuestEval, |
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title={QuestEval: Summarization Asks for Fact-based Evaluation}, |
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author={Scialom, Thomas and Dray, Paul-Alexis and Gallinari, Patrick and Lamprier, Sylvain and Piwowarski, Benjamin and Staiano, Jacopo and Wang, Alex}, |
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journal={arXiv preprint arXiv:2103.12693}, |
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year={2021} |
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} |
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``` |
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