Datasets:
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---
language:
- en
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
tags:
- sentence-transformers
task_categories:
- feature-extraction
- sentence-similarity
pretty_name: SQL Questions
dataset_info:
- config_name: mined-negative
features:
- name: query
dtype: string
- name: positive
dtype: string
- name: negative_1
dtype: string
- name: negative_2
dtype: string
- name: negative_3
dtype: string
- name: negative_4
dtype: string
- name: negative_5
dtype: string
- name: negative_6
dtype: string
- name: negative_7
dtype: string
- name: negative_8
dtype: string
- name: negative_9
dtype: string
- name: negative_10
dtype: string
splits:
- name: train
num_bytes: 6646432
num_examples: 5254
download_size: 2124847
dataset_size: 6646432
- config_name: pair
features:
- name: query
dtype: string
- name: positive
dtype: string
splits:
- name: train
num_bytes: 1010333
num_examples: 5269
download_size: 461323
dataset_size: 1010333
- config_name: triplet
features:
- name: query
dtype: string
- name: positive
dtype: string
- name: negative
dtype: string
splits:
- name: train
num_bytes: 15730842
num_examples: 52608
download_size: 1119260
dataset_size: 15730842
configs:
- config_name: mined-negative
data_files:
- split: train
path: mined-negative/train-*
- config_name: pair
data_files:
- split: train
path: pair/train-*
- config_name: triplet
data_files:
- split: train
path: triplet/train-*
---
# Dataset card for SQL Questions
This dataset is a reformatting of the [`sql_questions_triplets`](https://huggingface.co/datasets/sergeyvi4ev/sql_questions_triplets) dataset by [@sergeyvi4ev](https://huggingface.co/sergeyvi4ev), such that the dataset can be directly used to train Sentence Transformer models.
## Dataset Subsets
### `pair` subset
* Columns: "query", "positive"
* Column types: `str`, `str`
* Examples:
```python
{
'query': 'How many zip codes are under Barre, VT?',
'positive': '"Barre, VT" is the CBSA_name',
}
```
* Collection strategy: Reading the SQL Questions dataset and selecting all query-positive pairs.
* Deduplified: Yes
### `triplet` subset
* Columns: "query", "positive", "negative"
* Column types: `str`, `str`, `str`
* Examples:
```python
{
'query': 'How many zip codes are under Barre, VT?',
'positive': '"Barre, VT" is the CBSA_name',
'negative': "coordinates refers to latitude, longitude; latitude = '18.090875; longitude = '-66.867756'"
}
```
* Collection strategy: Reading the SQL Questions dataset and selecting all possible triplet pairs.
* Deduplified: No
### `mined-negative` subset
* Columns: "query", "positive", "negative_1", "negative_2", "negative_3", "negative_4", "negative_5", "negative_6", "negative_7", "negative_8", "negative_9", "negative_10"
* Column types: `str`, `str`, `str`, `str`, `str`, `str`, `str`, `str`, `str`, `str`, `str`, `str`
* Examples:
```python
{
"query": "How many zip codes are under Barre, VT?",
"positive": "\"Barre, VT\" is the CBSA_name",
"negative_1": "coordinates refers to latitude, longitude; latitude = '18.090875; longitude = '-66.867756'",
"negative_2": "name of county refers to county",
"negative_3": "median age over 40 refers to median_age > 40",
"negative_4": "\"PHILLIPS\" is the county; 'Montana' is the name of state",
"negative_5": "name of the CBSA officer refers to CBSA_name; position of the CBSA officer refers to CBSA_type;",
"negative_6": "population greater than 10000 in 2010 refers to population_2010 > 10000;",
"negative_7": "postal points refer to zip_code; under New York-Newark-Jersey City, NY-NJ-PA refers to CBSA_name = 'New York-Newark-Jersey City, NY-NJ-PA';",
"negative_8": "the largest water area refers to MAX(water_area);",
"negative_9": "\"Wisconsin\" is the state; largest land area refers to Max(land_area); full name refers to first_name, last_name; postal code refers to zip_code",
"negative_10": "\"Alabama\" and \"Illinois\" are both state; Ratio = Divide (Count(state = 'Alabama'), Count(state = 'Illinois'))"
}
```
* Collection strategy: Reading the SQL Questions dataset, filtering away the 15 samples that did not have 10 negative pairs, and formatting them in the described columns.
* Deduplified: No |