| --- |
| license: mit |
| datasets: |
| - mrqa |
| language: |
| - en |
| metrics: |
| - squad |
| library_name: adapter-transformers |
| pipeline_tag: question-answering |
| --- |
| |
| # Description |
| This is the MADE encoder model created by Friedman et al. (2021). This encoder should be used along with the following dataset-specific adapters. |
| - https://huggingface.co/UKP-SQuARE/MADE_HotpotQA_Adapter |
| - https://huggingface.co/UKP-SQuARE/MADE_TriviaQA_Adapter |
| - https://huggingface.co/UKP-SQuARE/MADE_SQuAD_Adapter |
| - https://huggingface.co/UKP-SQuARE/MADE_SearchQA_Adapter |
| - https://huggingface.co/UKP-SQuARE/MADE_NewsQA_Adapter |
| - 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 |
| Friedman et al. (2021) reported the following results: |
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| - SQuAD v1.1: 92.4 |
| - HotoptQA: 81.5 |
| - TriviaQA: 80.5 |
| - NewsQA: 72.1 |
| - SearchQA: 85.8 |
| - NaturalQuestions: 80.9 |
| - Avg: 82.2 |
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| Please refer to the original publication for more information. |
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| # Citation |
| Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021) |
