Datasets:

SuperMax991
/

spider-text2sql

license: cc-by-sa-4.0
task_categories:
  - text2text-generation
  - table-question-answering
language:
  - en
  - sql
tags:
  - text-to-sql
  - sql
  - spider
  - flan-t5
  - seq2seq
  - nlp
size_categories:
  - 1K<n<10K

SPIDER Text-to-SQL — Easy Access Version

A clean, HuggingFace-native version of the SPIDER Text-to-SQL benchmark. The original SPIDER dataset requires manually downloading a ZIP file from the Spider website. This version makes it instantly accessible via load_dataset.

What's Included

Each row contains the question, gold SQL, the database identifier, and a pre-parsed compact schema string — everything needed to train or evaluate a Text-to-SQL model without any additional preprocessing.

Column	Description
`db_id`	Database identifier (e.g. `"concert_singer"`)
`question`	Natural language question
`query`	Gold standard SQL answer
`db_schema`	Compact schema: `"table: col (type), col (type)
`question_toks`	Tokenized question words (list of strings)

Splits

Split	Source file	Examples
train	`train_spider.json`	7,000
test	`train_others.json`	1,034

Note: Following standard SPIDER practice, train_others.json is used as the held-out evaluation set. The original SPIDER test set is withheld for the official leaderboard.

Usage

from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/spider-text2sql")

train = dataset["train"]
test  = dataset["test"]

# Access fields
example = train[0]
print(example["question"])   # "How many heads of the departments are older than 56?"
print(example["query"])      # "SELECT count(*) FROM head WHERE age > 56"
print(example["db_id"])      # "department_management"
print(example["db_schema"])  # "department: Department_ID (number), ... | head: ..."

Schema Format

The db_schema column uses a compact linear format widely used in the Text-to-SQL literature:

table1: col1 (type), col2 (type), col3 (type) | table2: col4 (type), col5 (type)

This format is:

Human-readable and model-friendly
Fits within typical 512-token input limits for most seq2seq models
Derived directly from the official SPIDER tables.json

Fine-tuning Example (Flan-T5 prompt format)

This dataset pairs naturally with prompt-based fine-tuning:

def build_prompt(example):
    return (
        f"Translate to SQL: {example['question']}\n"
        f"Database schema:\n{example['db_schema']}"
    )

# example["query"] is the target output

Difference from Original SPIDER

	Original SPIDER	This Dataset
Download method	Manual ZIP from website	`load_dataset(...)` ✅
Schema included	Separate `tables.json`	✅ Pre-joined per example
Complex `sql` dict	✅ Included	❌ Omitted (noisy for most use cases)
`query_toks_no_value`	✅ Included	❌ Omitted
Ready to train	Requires preprocessing	✅ Yes

Source & License

Original dataset: SPIDER (Yu et al., 2018)
License: Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0)
This derived dataset is released under the same license.

Citation

@inproceedings{yu-etal-2018-spider,
    title     = "{S}pider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-{SQL} Task",
    author    = "Yu, Tao and others",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    year      = "2018",
    url       = "https://aclanthology.org/D18-1425",
}