license: mit
datasets:
- mrqa
language:
- en
metrics:
- squad
library_name: adapter-transformers
pipeline_tag: question-answering
Description
This is the single-dataset adapter for the NaturalQuestions 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.
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
Usage
This model contains the same weights as https://huggingface.co/princeton-nlp/MADE/resolve/main/single_dataset_adapters/NaturalQuestions/model.pt. The only difference is that our repository follows the standard format of AdapterHub. Therefore, you could load this model as follows:
from transformers import RobertaForQuestionAnswering, RobertaTokenizerFast
model = RobertaForQuestionAnswering.from_pretrained("roberta-base")
model.load_adapter("UKP-SQuARE/NaturalQuestions_Adapter_RoBERTa", source="hf")
model.set_active_adapters("NaturalQuestions")
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base')
pipe = pipeline("question-answering", model=model, tokenizer=tokenizer)
pipe({"question": "What is the capital of Germany?", "context": "The capital of Germany is Berlin."})
Note you need the adapter-transformers library https://adapterhub.ml
Evaluation
Friedman et al. report an F1 score of 79.2 on NaturalQuestions.
Please refer to the original publication for more information.
Citation
Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)