Update README.md

README.md

@@ -6,7 +6,7 @@ language:
 language_creators:
 - found
 license:
-- mit
+- apache-2.0
 multilinguality:
 - monolingual
 pretty_name: codequeries
@@ -15,10 +15,9 @@ size_categories:
 source_datasets:
 - original
 tags:
-- code
+- neural modeling of code
 - code question answering
-- code semantic parsing
-- codeqa
+- code semantic understanding
 task_categories:
 - question-answering
 task_ids:
@@ -44,17 +43,16 @@ task_ids:
 ## Dataset Description
 
 - **Homepage:** [Codequeires](https://huggingface.co/datasets/thepurpleowl/codequeries)
-- **Repository:** [Code repo](https://github.com/adityakanade/natural-cubert/)
+- **Repository:** [Code repo](point to the right repo that we will be open sourcing)
 - **Paper:**
 
 ### Dataset Summary
 
-CodeQueries allows to explore extractive question-answering methodology over code 
-by providing semantic natural language queries as question and code spans as answer or supporting fact. Given a query, finding the answer/supporting fact spans in code context involves analysis complex concepts and long chains of reasoning. The dataset is provided with five separate settings; details on the setting can be found in the [paper]().
+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. [paper]().
 
 ### Supported Tasks and Leaderboards
 
-Query comprehension for code, Extractive question answering for code.
+Extractive question answering for code, semantic understanding of code.
 
 ### Languages
 
@@ -62,12 +60,12 @@ The dataset contains code context from `python` files.
 
 ## Dataset Structure
 
-### How to use
-The dataset can directly used with huggingface datasets. You can load and iterate through the dataset for the proposed five settings with the following two lines of code:
+### How to Use
+The dataset can be directly used with the huggingface datasets package. You can load and iterate through the dataset for the proposed five settings with the following two lines of code:
 ```python
 import datasets
 
-# instead `twostep`, other settings are <ideal/file_ideal/prefix>.
+# in addition to `twostep`, the other supported settings are <ideal/file_ideal/prefix>.
 ds = datasets.load_dataset("thepurpleowl/codequeries", "twostep", split=datasets.Split.TEST)
 print(next(iter(ds)))
 
@@ -96,7 +94,7 @@ print(next(iter(ds)))
 ### Data Splits and Data Fields
 Detailed information on the data splits for proposed settings can be found in the paper.
 
-In general, data splits in  all prpoposed settings have examples in following fields -
+In general, data splits in all the proposed settings have examples with the following fields -
 ```
   - query_name (query name to uniquely identify the query)
   - code_file_path (relative source file path w.r.t. ETH Py150 corpus)
@@ -113,13 +111,13 @@ In general, data splits in  all prpoposed settings have examples in following fi
 
 ## Dataset Creation
 
-The dataset is created by using [ETH Py150 Open corpus](https://github.com/google-research-datasets/eth_py150_open) as source for code contexts. To get natural language queries and corresponding answer/supporting spans in ETH Py150 Open corpus files, CodeQL was used.
+The dataset is created by using [ETH Py150 Open corpus](https://github.com/google-research-datasets/eth_py150_open) as source for code contexts. To get semantic queries and corresponding answer/supporting-fact spans in ETH Py150 Open corpus files, CodeQL was used.
 
 
 ## Additional Information
 ### Licensing Information
 
-Codequeries dataset is licensed under the [Apache-2.0](https://opensource.org/licenses/Apache-2.0) License.
+The CodeQueries dataset is licensed under the [Apache-2.0](https://opensource.org/licenses/Apache-2.0) License.
 
 ### Citation Information