Update README

README.md

@@ -25,7 +25,7 @@ task_ids:
 - extractive-qa
 ---
 
-# Dataset Card for [Dataset Name]
+# Dataset Card for Codequeries
 
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
@@ -54,43 +54,60 @@ task_ids:
 
 ## Dataset Description
 
-- **Homepage:**
+- **Homepage:** [Codequeires](https://huggingface.co/datasets/thepurpleowl/codequeries)
 - **Repository:** [Code repo](https://github.com/adityakanade/natural-cubert/)
+- **Leaderboard:** [Code repo](https://github.com/adityakanade/natural-cubert/)
 - **Paper:**
 
 ### Dataset Summary
 
 CodeQueries allows to explore extractive question-answering methodology over code 
-by providing semantic queries as question and answer pairs over code context involving 
-complex concepts and long chains of reasoning.
+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]().
 
 ### Supported Tasks and Leaderboards
 
-[More Information Needed]
+Query comprehension for code, Extractive question answering for code. Refer the [paper]().
 
 ### Languages
 
-The code section have taken from `python` files.
+The dataset contains code context from `python` files.
 
 ## Dataset Structure
 
-### Data Instances
-
-All splits of all settings have same format. An example looks as follows -
-```
+### 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:
+```python
+from datasets import load_dataset
+
+ds = load_dataset("thepurpleowl/codequeries", "<ideal/file_ideal/prefix/twostep>", split="train")
+print(next(iter(ds)))
+#OUTPUT:
+{
+ 'code': "import mod189 from './mod189';\nvar value=mod189+1;\nexport default value;\n",
+ 'repo_name': 'MirekSz/webpack-es6-ts',
+ 'path': 'app/mods/mod190.js',
+ 'language': 'JavaScript',
+ 'license': 'isc',
+ 'size': 73
+}
 ```
 
-### Data Fields
+### Data Splits and Data Fields
+Detailed information on the data splits for proposed settings can be found in the paper.
 
-- examples
+In general, data splits in  all prpoposed settings have examples in following fields -
+```
   - query_name (query name to uniquely identify the query)
-  - context_blocks (code blocks supplied as input to the model for prediction)
-  - answer_spans (code in answer spans)
-  - supporting_fact_spans (code in supporting-fact spans)
   - code_file_path (relative source file path w.r.t. ETH Py150 corpus)
-  - example_type (positive(1) or negative(0) example type)
-  - subtokenized_input_sequence (example subtokens)
+  - context_blocks (code blocks as context with metadata)  [`prefix` setting doesn't have this field]
+  - answer_spans (answer spans with metadata)
+  - supporting_fact_spans (supporting-fact spans with metadata)
+  - example_type (1(positive)) or 0(negative)) example type)
+  - single_hop (True or False - for query type)
+  - subtokenized_input_sequence (example subtokens)  [`prefix` setting has the corresponding token ids]
   - label_sequence (example subtoken labels)
+  - relevance_label (0 (not relevant) or 1 (relevant) - relevance label of a block)
+```
 
 ### Data Splits
 
@@ -104,62 +121,115 @@ All splits of all settings have same format. An example looks as follows -
 
 ## Dataset Creation
 
-### Curation Rationale
+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.
 
-[More Information Needed]
 
-### Source Data
+### Licensing Information
 
-#### Initial Data Collection and Normalization
+Codequeries dataset is licensed under the [Apache-2.0](https://opensource.org/licenses/Apache-2.0) License.
+
+### Citation Information
 
 [More Information Needed]
 
-#### Who are the source language producers?
+### Contributions
 
-[More Information Needed]
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.# Dataset Card for Codequeries
 
-### Annotations
+## Table of Contents
+- [Table of Contents](#table-of-contents)
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [How to use](#how-to-use)
+  - [Data Splits and Data Fields](#data-splits-and-data-fields)
+- [Dataset Creation](#dataset-creation)
+- [Additional Information](#additional-information)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
 
-#### Annotation process
+## Dataset Description
 
-[More Information Needed]
+- **Homepage:** [Codequeires](https://huggingface.co/datasets/thepurpleowl/codequeries)
+- **Repository:** [Code repo](https://github.com/adityakanade/natural-cubert/)
+- **Paper:**
 
-#### Who are the annotators?
+### Dataset Summary
 
-[More Information Needed]
+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]().
 
-### Personal and Sensitive Information
+### Supported Tasks and Leaderboards
 
-[More Information Needed]
+Query comprehension for code, Extractive question answering for code.
 
-## Considerations for Using the Data
+### Languages
 
-### Social Impact of Dataset
+The dataset contains code context from `python` files.
 
-[More Information Needed]
+## Dataset Structure
 
-### Discussion of Biases
+### 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:
+```python
+import datasets
+
+# instead `twostep`, other settings are <ideal/file_ideal/prefix>.
+ds = datasets.load_dataset("thepurpleowl/codequeries", "twostep", split=datasets.Split.TEST)
+print(next(iter(ds)))
+
+#OUTPUT:
+{'query_name': 'Unused import',
+ 'code_file_path': 'rcbops/glance-buildpackage/glance/tests/unit/test_db.py',
+ 'context_block': {'content': '# vim: tabstop=4 shiftwidth=4 softtabstop=4\n\n# Copyright 2010-2011 OpenStack, LLC\ ...',
+                    'metadata': 'root',
+                    'header': "['module', '___EOS___']",
+                    'index': 0},
+ 'answer_spans': [{'span': 'from glance.common import context',
+                   'start_line': 19,
+                   'start_column': 0,
+                   'end_line': 19,
+                   'end_column': 33}
+                 ],
+ 'supporting_fact_spans': [],
+ 'example_type': 1,
+ 'single_hop': False,
+ 'subtokenized_input_sequence': ['[CLS]_', 'Un', 'used_', 'import_', '[SEP]_', 'module_', '\\u\\u\\uEOS\\u\\u\\u_', '#', ' ', 'vim', ':', ...],
+ 'label_sequence': [4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, ...],
+ 'relevance_label': 1
+}
+```
 
-[More Information Needed]
+### Data Splits and Data Fields
+Detailed information on the data splits for proposed settings can be found in the paper.
 
-### Other Known Limitations
+In general, data splits in  all prpoposed settings have examples in following fields -
+```
+  - query_name (query name to uniquely identify the query)
+  - code_file_path (relative source file path w.r.t. ETH Py150 corpus)
+  - context_blocks (code blocks as context with metadata)  [`prefix` setting doesn't have this field and `twostep` has `context_block`]
+  - answer_spans (answer spans with metadata)
+  - supporting_fact_spans (supporting-fact spans with metadata)
+  - example_type (1(positive)) or 0(negative)) example type)
+  - single_hop (True or False - for query type)
+  - subtokenized_input_sequence (example subtokens)  [`prefix` setting has the corresponding token ids]
+  - label_sequence (example subtoken labels)
+  - relevance_label (0 (not relevant) or 1 (relevant) - relevance label of a block)   [only `twostep` setting has this field]
+```
 
-[More Information Needed]
 
-## Additional Information
+## Dataset Creation
 
-### Dataset Curators
+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.
 
-[More Information Needed]
 
+## Additional Information
 ### Licensing Information
 
-[More Information Needed]
+Codequeries dataset is licensed under the [Apache-2.0](https://opensource.org/licenses/Apache-2.0) License.
 
 ### Citation Information
 
 [More Information Needed]
-
-### Contributions
-
-Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.