
mrm8488/t5-base-finetuned-wikiSQL
Text2Text Generation
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A large crowd-sourced dataset for developing natural language interfaces for relational databases
An example of 'validation' looks as follows.
This example was too long and was cropped:
{
"phase": 1,
"question": "How would you answer a second test question?",
"sql": {
"agg": 0,
"conds": {
"column_index": [2],
"condition": ["Some Entity"],
"operator_index": [0]
},
"human_readable": "SELECT Header1 FROM table WHERE Another Header = Some Entity",
"sel": 0
},
"table": "{\"caption\": \"L\", \"header\": [\"Header1\", \"Header 2\", \"Another Header\"], \"id\": \"1-10015132-9\", \"name\": \"table_10015132_11\", \"page_i..."
}
The data fields are the same among all splits.
phase
: a int32
feature.question
: a string
feature.header
: a list
of string
features.page_title
: a string
feature.page_id
: a string
feature.types
: a list
of string
features.id
: a string
feature.section_title
: a string
feature.caption
: a string
feature.rows
: a dictionary feature containing:feature
: a string
feature.name
: a string
feature.human_readable
: a string
feature.sel
: a int32
feature.agg
: a int32
feature.conds
: a dictionary feature containing:column_index
: a int32
feature.operator_index
: a int32
feature.condition
: a string
feature.name | train | validation | test |
---|---|---|---|
default | 56355 | 8421 | 15878 |
@article{zhongSeq2SQL2017,
author = {Victor Zhong and
Caiming Xiong and
Richard Socher},
title = {Seq2SQL: Generating Structured Queries from Natural Language using
Reinforcement Learning},
journal = {CoRR},
volume = {abs/1709.00103},
year = {2017}
}
Thanks to @lewtun, @ghomasHudson, @thomwolf for adding this dataset.