Update README.md

README.md

@@ -11,12 +11,26 @@ GitHub: https://github.com/RAIVNLab/AdANNS
 
 Arxiv: https://arxiv.org/abs/2305.19435
 
+<p align="center">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/64af72d4a609b29cc7b5919b/QYOqZ25qc9pTWlFR0D0VF.png" alt="drawing" width="600"/>
+Adaptive representations can be utilized effectively in the decoupled components of clustering and
+searching for a better accuracy-compute trade-off (AdANNS-IVF).
+</p>
+
 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]. 
 
+You can easily load an mrl-nq model as follows:
+```
+from transformers import BertModel
+import torch
+
+model = BertModel.from_pretrained('dpr-nq-d768_384_192_96_48')
+```
+
 ## Citation
 If you find this project useful in your research, please consider citing:
 ```
@@ -28,4 +42,4 @@ If you find this project useful in your research, please consider citing:
       month     = {December},
       year      = {2023},
 }
-```
+```