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  This is a QA model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co/roberta-large) and the fine-tuning data is [QAMR](https://github.com/uwnlp/qamr).
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  ## Usage
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  - To use the QA model independently, follow the [huggingface documentation on AutoModelForQuestionAnswering](https://huggingface.co/transformers/task_summary.html?highlight=automodelforquestionanswering#extractive-question-answering).
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  - To use it as part of the event extraction system, please check out [our Github repo](https://github.com/veronica320/Zeroshot-Event-Extraction).
 
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  This is a QA model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co/roberta-large) and the fine-tuning data is [QAMR](https://github.com/uwnlp/qamr).
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+ ## Demo
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+ To see how the model works, type a question and a context separated in the right-hand-side textboxs under "Hosted inference API".
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+ Example:
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+ - Question: `Who was killed?`
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+ - Context: `A car bomb exploded Thursday in a crowded outdoor market in the heart of Jerusalem, killing at least two people, police said.`
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+ - Answer: `people`
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  ## Usage
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  - To use the QA model independently, follow the [huggingface documentation on AutoModelForQuestionAnswering](https://huggingface.co/transformers/task_summary.html?highlight=automodelforquestionanswering#extractive-question-answering).
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  - To use it as part of the event extraction system, please check out [our Github repo](https://github.com/veronica320/Zeroshot-Event-Extraction).