Update README.md

README.md

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 ---
 license: mit
+datasets:
+- natural_questions
+pipeline_tag: question-answering
 ---
+# AdANNS: A Framework for Adaptive Semantic Search 💃
+_Aniket Rege*, Aditya Kusupati*, Sharan Ranjit S, Alan Fan, Qinqqing Cao, Sham Kakade, Prateek Jain, Ali Farhadi_
+
+GitHub: https://github.com/RAIVNLab/AdANNS
+
+Arxiv: https://arxiv.org/abs/2305.19435
+
+We provide four BERT-Base models finetuned on Natural Questions with [Matryoshka Representation Learning](https://github.com/RAIVNLab/MRL) (MRL).
+
+A vanilla pretrained BERT-Base has a 768-d representation (information bottleneck). As we train with MRL, we enforce the network to learn representations at 
+multiple granularities nested within a 768-d embedding. The granularities at which we finetune BERT-Base with Matroyshka Loss are specified in the folder name,
+e.g. for `dpr-nq-d768_384_192_96_48`, we have d=[48, 96, 192, 384, 768]. 
+
+## Citation
+If you find this project useful in your research, please consider citing:
+```
+@inproceedings{rege2023adanns,
+      title={AdANNS: A Framework for Adaptive Semantic Search}, 
+      author={Aniket Rege and Aditya Kusupati and Sharan Ranjit S and Alan Fan and Qingqing Cao and Sham Kakade and Prateek Jain and Ali Farhadi},
+      year={2023},
+      booktitle = {Advances in Neural Information Processing Systems},
+      month     = {December},
+      year      = {2023},
+}
+```