Update README.md

README.md

@@ -1,3 +1,44 @@
 ---
 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)