fix readme

README.md

@@ -1,6 +1,6 @@
 ---
 dataset_info:
-- config_name: ru_pauq_tl
+- config_name: ru_os
   features:
   - name: id
     dtype: string
@@ -22,18 +22,14 @@ dataset_info:
     sequence: string
   - name: query_toks_no_values
     sequence: string
-  - name: masked_query
+  - name: template
     dtype: string
   splits:
   - name: train
-    num_bytes: 8188471
-    num_examples: 6558
+    num_examples: 8800
   - name: test
-    num_bytes: 2284950
-    num_examples: 1979
-  download_size: 315047611
-  dataset_size: 10473421
-- config_name: en_pauq_tl
+    num_examples: 1076
+- config_name: en_os
   features:
   - name: id
     dtype: string
@@ -55,18 +51,14 @@ dataset_info:
     sequence: string
   - name: query_toks_no_values
     sequence: string
-  - name: masked_query
+  - name: template
     dtype: string
   splits:
   - name: train
-    num_bytes: 7433812
-    num_examples: 6559
+    num_examples: 8800
   - name: test
-    num_bytes: 2017972
-    num_examples: 1975
-  download_size: 315047611
-  dataset_size: 9451784
-- config_name: ru_pauq_iid
+    num_examples: 1076
+- config_name: ru_trl
   features:
   - name: id
     dtype: string
@@ -88,18 +80,14 @@ dataset_info:
     sequence: string
   - name: query_toks_no_values
     sequence: string
-  - name: masked_query
+  - name: template
     dtype: string
   splits:
   - name: train
-    num_bytes: 9423175
-    num_examples: 8800
+    num_examples: 7890
   - name: test
-    num_bytes: 1069135
-    num_examples: 1074
-  download_size: 315047611
-  dataset_size: 10492310
-- config_name: en_pauq_iid
+    num_examples: 1971
+- config_name: en_trl
   features:
   - name: id
     dtype: string
@@ -121,27 +109,71 @@ dataset_info:
     sequence: string
   - name: query_toks_no_values
     sequence: string
-  - name: masked_query
+  - name: template
     dtype: string
   splits:
   - name: train
-    num_bytes: 8505951
-    num_examples: 8800
+    num_examples: 7890
   - name: test
-    num_bytes: 964008
-    num_examples: 1076
-  download_size: 315047611
-  dataset_size: 9469959
-license: cc-by-4.0
-task_categories:
-- translation
-- text2text-generation
-language:
-- ru
-tags:
-- text-to-sql
-size_categories:
-- 10K<n<100K
+    num_examples: 1974
+- config_name: ru_tsl
+  features:
+  - name: id
+    dtype: string
+  - name: db_id
+    dtype: string
+  - name: source
+    dtype: string
+  - name: type
+    dtype: string
+  - name: question
+    dtype: string
+  - name: query
+    dtype: string
+  - name: sql
+    sequence: string
+  - name: question_toks
+    sequence: string
+  - name: query_toks
+    sequence: string
+  - name: query_toks_no_values
+    sequence: string
+  - name: template
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 7900
+  - name: test
+    num_examples: 1969
+- config_name: en_tsl
+  features:
+  - name: id
+    dtype: string
+  - name: db_id
+    dtype: string
+  - name: source
+    dtype: string
+  - name: type
+    dtype: string
+  - name: question
+    dtype: string
+  - name: query
+    dtype: string
+  - name: sql
+    sequence: string
+  - name: question_toks
+    sequence: string
+  - name: query_toks
+    sequence: string
+  - name: query_toks_no_values
+    sequence: string
+  - name: template
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 7900
+  - name: test
+    num_examples: 1974
 ---
 # Dataset Card for [Dataset Name]
 
@@ -149,10 +181,23 @@ size_categories:
 - [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)
@@ -169,55 +214,88 @@ Link to databases: https://drive.google.com/file/d/1Xjbp207zfCaBxhPgt-STB_RxwNo2
 
 ### Dataset Summary
 
-The Russian version of the [Spider](https://yale-lily.github.io/spider) - Yale Semantic Parsing and Text-to-SQL Dataset.
-Major changings:
+[More Information Needed]
 
-- Adding (not replacing) new Russian language values in DB tables. Table and DB names remain the original.
-- Localization of natural language questions into Russian. All DB values replaced by new.
-- Changing in SQL-queries filters.
-- Filling empty table with values.
-- Complementing the dataset with the new samples of underrepresented types.
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
 
 ### Languages
 
-Russian
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-The translation from English to Russian is undertaken by a professional human translator with SQL-competence. A verification of the translated questions and their conformity with the queries, and an updating of the databases are undertaken by 4 computer science students.
-Details are in the [section 3](https://aclanthology.org/2022.findings-emnlp.175.pdf).
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
 
 ## Additional Information
 
+### Dataset Curators
+
+[More Information Needed]
+
 ### Licensing Information
 
-The presented dataset have been collected in a manner which is consistent with the terms of use of the original Spider, which is distributed under the CC BY-SA 4.0 license. 
+[More Information Needed]
 
 ### Citation Information
 
-[Paper link](https://aclanthology.org/2022.findings-emnlp.175.pdf)
-
-```
-@inproceedings{bakshandaeva-etal-2022-pauq,
-    title = "{PAUQ}: Text-to-{SQL} in {R}ussian",
-    author = "Bakshandaeva, Daria  and
-      Somov, Oleg  and
-      Dmitrieva, Ekaterina  and
-      Davydova, Vera  and
-      Tutubalina, Elena",
-    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
-    month = dec,
-    year = "2022",
-    address = "Abu Dhabi, United Arab Emirates",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2022.findings-emnlp.175",
-    pages = "2355--2376",
-    abstract = "Semantic parsing is an important task that allows to democratize human-computer interaction. One of the most popular text-to-SQL datasets with complex and diverse natural language (NL) questions and SQL queries is Spider. We construct and complement a Spider dataset for Russian, thus creating the first publicly available text-to-SQL dataset for this language. While examining its components - NL questions, SQL queries and databases content - we identify limitations of the existing database structure, fill out missing values for tables and add new requests for underrepresented categories. We select thirty functional test sets with different features that can be used for the evaluation of neural models{'} abilities. To conduct the experiments, we adapt baseline architectures RAT-SQL and BRIDGE and provide in-depth query component analysis. On the target language, both models demonstrate strong results with monolingual training and improved accuracy in multilingual scenario. In this paper, we also study trade-offs between machine-translated and manually-created NL queries. At present, Russian text-to-SQL is lacking in datasets as well as trained models, and we view this work as an important step towards filling this gap.",
-}
-```
+[More Information Needed]
 
 ### Contributions
 
-Thanks to [@gugutse](https://github.com/Gugutse), [@runnerup96](https://github.com/runnerup96), [@dmi3eva](https://github.com/dmi3eva), [@veradavydova](https://github.com/VeraDavydova), [@tutubalinaev](https://github.com/tutubalinaev) for adding this dataset.
+Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.