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README.md
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pipeline_tag: question-answering
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---
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This is the MADE encoder model created by Friedman et al. (2021). This encoder should be used along with the following dataset-specific adapters.
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- https://huggingface.co/UKP-SQuARE/MADE_HotpotQA_Adapter
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- https://huggingface.co/UKP-SQuARE/MADE_TriviaQA_Adapter
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- https://huggingface.co/UKP-SQuARE/MADE_NewsQA_Adapter
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- https://huggingface.co/UKP-SQuARE/MADE_NaturalQuestions_Adapter
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Friedman et al. (2021) reported the following results:
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- SQuAD v1.1: 92.4
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- Avg: 82.2
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The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is https://github.com/princeton-nlp/MADE
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Please refer to the original publication for more information.
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Citation
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Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)
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pipeline_tag: question-answering
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---
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# Description
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This is the MADE encoder model created by Friedman et al. (2021). This encoder should be used along with the following dataset-specific adapters.
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- https://huggingface.co/UKP-SQuARE/MADE_HotpotQA_Adapter
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- https://huggingface.co/UKP-SQuARE/MADE_TriviaQA_Adapter
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- https://huggingface.co/UKP-SQuARE/MADE_NewsQA_Adapter
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- https://huggingface.co/UKP-SQuARE/MADE_NaturalQuestions_Adapter
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The UKP-SQuARE team created this model repository to simplify the deployment of this model on the UKP-SQuARE platform. The GitHub repository of the original authors is https://github.com/princeton-nlp/MADE
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# Evaluation Results
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Friedman et al. (2021) reported the following results:
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- SQuAD v1.1: 92.4
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- Avg: 82.2
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Please refer to the original publication for more information.
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# Citation
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Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)
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