Update README

README.md

@@ -27,115 +27,6 @@ task_ids:
 
 # Dataset Card for Codequeries
 
-## 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)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-
-## Dataset Description
-
-- **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 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
-
-Query comprehension for code, Extractive question answering for code. Refer the [paper]().
-
-### Languages
-
-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:
-```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 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 -
-```
-  - 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]
-  - 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
-
-|              |train |validation |test |
-|--------------|:----:|:---------:|:---:|
-|ideal         | 9427 |       3270| 3245|
-|prefix        |   -  |         - | 3245|
-|sliding_window|   -  |         - | 3245|
-|file_ideal    |   -  |         - | 3245|
-|twostep       |   -  |         - | 3245|
-
-## 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.
-
-
-### Licensing Information
-
-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.# Dataset Card for Codequeries
-
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
 - [Dataset Description](#dataset-description)