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--- |
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license: mit |
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datasets: |
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- mrqa |
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language: |
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- en |
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metrics: |
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- squad |
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library_name: adapter-transformers |
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pipeline_tag: question-answering |
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--- |
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# Description |
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This is the single-dataset adapter for the HotpotQA partition of the MRQA 2019 Shared Task Dataset. The adapter was created by Friedman et al. (2021) and should be used with the `roberta-base` encoder. |
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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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# Usage |
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This model contains the same weights as https://huggingface.co/princeton-nlp/MADE/resolve/main/single_dataset_adapters/HotpotQA/model.pt. The only difference is that our repository follows the standard format of AdapterHub. Therefore, you could load this model as follows: |
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``` |
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from transformers import RobertaForQuestionAnswering, RobertaTokenizerFast |
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model = RobertaForQuestionAnswering.from_pretrained("roberta-base") |
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model.load_adapter("UKP-SQuARE/HotpotQA_Adapter_RoBERTa", source="hf") |
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model.set_active_adapters("HotpotQA") |
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tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base') |
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pipe = pipeline("question-answering", model=model, tokenizer=tokenizer) |
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pipe({"question": "What is the capital of Germany?", "context": "The capital of Germany is Berlin."}) |
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``` |
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Note you need the adapter-transformers library https://adapterhub.ml |
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# Evaluation |
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Friedman et al. report an F1 score of **78.5 on HotpotQA**. |
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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) |
