# taoyds/spider

scripts and baselines for Spider: Yale complex and cross-domain semantic parsing and text-to-SQL challenge

Repository: https://github.com/taoyds/spider
Canonical: https://ross.abutalabs.com/products/taoyds-spider
Homepage: https://yale-lily.github.io/spider
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2024-05-29T03:16:42+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2900, "days_push": 826, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1097, forks 227 (observed 2026-08-28T04:03:34.590634+00:00)

## What it is
Spider is a large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-SQL, with 10,181 questions and 5,693 SQL queries across 200 databases. This repository contains the evaluation scripts, preprocessing code, and baseline models from the EMNLP 2018 paper.

## Use cases
- train and evaluate text-to-SQL models
- benchmark semantic parsing systems on cross-domain databases
- evaluate generated SQL queries against gold labels
- build natural language interfaces for relational databases
- compare baseline text-to-SQL model performance
- research generalization to unseen database schemas

## When to choose
- you need a standard benchmark for text-to-SQL or semantic parsing research
- you want official evaluation scripts for the Spider challenge leaderboard
- you need a large cross-domain dataset of natural language questions paired with SQL queries

## When to avoid
- you need a production text-to-SQL system rather than a research dataset
- you want a maintained end-to-end model rather than baselines and evaluation code
- you need a simpler single-domain text-to-SQL dataset

## Facets
- artifact type: dataset
- maturity: stable
- function: nlp, machine-learning, parser
- domain: databases, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: text-to-sql, semantic-parsing, benchmark, dataset, evaluation-scripts, baselines, evaluation, natural-language-processing

## Member repositories
- taoyds/spider (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:34.590634+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:46:37.318486+00:00, confidence not recorded.
  - readme: https://github.com/taoyds/spider (fetched 2026-08-28T04:03:34.590634+00:00, sha 499923a3c29b)
  - homepage: https://yale-lily.github.io/spider (fetched 2026-08-29T12:50:04.809545+00:00, sha 616b4b706624)
- Data as of 2026-08-30T08:39:29.467469+00:00.