README.md

metadata

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 dataset by @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:

  {
    '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:

  {
    '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:

  {
    "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