Update README.md

README.md

@@ -1,3 +1,40 @@
 ---
 license: mit
+datasets:
+- mrqa
+language:
+- en
+metrics:
+- squad
+library_name: adapter-transformers
+pipeline_tag: question-answering
 ---
+
+This is the MADE Adapter for TriviaQA created by Friedman et al. (2021). This adapter should be used with this encoder: https://huggingface.co/UKP-SQuARE/MADE_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
+
+This model contains the same weights as https://huggingface.co/princeton-nlp/MADE/resolve/main/made_tuned_adapters/TriviaQA/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("UKP-SQuARE/MADE_Encoder")
+model.load_adapter("UKP-SQuARE/MADE_TriviaQA_Adapter",  source="hf")
+model.set_active_adapters("TriviaQA")
+
+tokenizer = RobertaTokenizerFast.from_pretrained('UKP-SQuARE/MADE_Encoder')
+
+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
+
+
+Please refer to the original publication for more information.
+
+Citation: 
+Single-dataset Experts for Multi-dataset Question Answering (Friedman et al., EMNLP 2021)