---
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](https://yale-seas.yale.edu/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) | table2: ..."` |
| `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

```python
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:

```python
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)](https://yale-seas.yale.edu/spider/)
- License: **Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0)**
- This derived dataset is released under the same license.

## Citation

```bibtex
@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",
}
```