---
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