Update README.md

README.md

@@ -44,11 +44,11 @@ license:
 
 - **Homepage:** [Data](https://huggingface.co/datasets/thepurpleowl/codequeries)
 - **Repository:** [Code](https://github.com/thepurpleowl/codequeries-benchmark)
-- **Paper:** [Learning to Answer Semantic Queries over Code](https://arxiv.org/abs/2209.08372)
+- **Paper:** 
 
 ### Dataset Summary
 
-CodeQueries is a dataset to evaluate the ability of neural networks to answer semantic queries over code. Given a query and code, a model is expected to identify answer and supporting-fact spans in the code for the query. This is extractive question-answering over code, for questions with a large scope (entire files) and complexity including both single- and multi-hop reasoning. See the [paper]() for more details.
+CodeQueries is a dataset to evaluate the ability of neural networks to answer semantic queries over code. Given a query and code, a model is expected to identify answer and supporting-fact spans in the code for the query. This is extractive question-answering over code, for questions with a large scope (entire files) and complexity including both single- and multi-hop reasoning.
 
 ### Supported Tasks and Leaderboards
 
@@ -118,19 +118,3 @@ The dataset is created using [ETH Py150 Open dataset](https://github.com/google-
 ### Licensing Information
 
 The source code repositories used for preparing CodeQueries are based on the [ETH Py150 Open dataset](https://github.com/google-research-datasets/eth_py150_open) and are redistributable under the respective licenses. A Huggingface dataset for ETH Py150 Open is available [here](https://huggingface.co/datasets/eth_py150_open). The labeling prepared and provided by us as part of CodeQueries is released under the Apache-2.0 license.
-
-### Citation Information
-
-```
-@misc{https://doi.org/10.48550/arxiv.2209.08372,
-  doi = {10.48550/ARXIV.2209.08372},  
-  url = {https://arxiv.org/abs/2209.08372},  
-  author = {Sahu, Surya Prakash and Mandal, Madhurima and Bharadwaj, Shikhar and Kanade, Aditya and Maniatis, Petros and Shevade, Shirish},  
-  keywords = {Software Engineering (cs.SE), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},  
-  title = {Learning to Answer Semantic Queries over Code},  
-  publisher = {arXiv},  
-  year = {2022},  
-  copyright = {Creative Commons Attribution 4.0 International}
-}
-
-```