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pandeyankit84/dataviz-99-nl2vis-dataset

DataViz-99: NL2VIS Dataset Card Dataset Description DataViz-99 is a closed-label instruction-tuning dataset for natural language to visualization (NL2VIS) tasks. Built from real benchmark data (nvBench + Spider), it teaches models to convert natural language queries into structured chart specifications. Dataset Summary Metric Value Total Examples 17,457 Format JSONL (prompt/completion pairs) Languages English, Hindi, Spanish, French… See the full description on the dataset page: https://huggingface.co/datasets/pandeyankit84/dataviz-99-nl2vis-dataset.

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Dataset Card

DataViz-99: NL2VIS Dataset Card

Dataset Description

DataViz-99 is a closed-label instruction-tuning dataset for natural language to visualization (NL2VIS) tasks. Built from real benchmark data (nvBench + Spider), it teaches models to convert natural language queries into structured chart specifications.

Dataset Summary

Metric	Value
Total Examples	17,457
Format	JSONL (prompt/completion pairs)
Languages	English, Hindi, Spanish, French
Databases	166 real SQL schemas
License	CC-BY-4.0

Dataset Sources

Primary Sources

1.nvBench (TsinghuaDatabaseGroup)
2.7,247 visualization entries
3.25,750 (NL, VIS) pairs
4.105 domains (sports, healthcare, finance, etc.)

1.Spider (Yale LILY)
2.166 database schemas
3.Exact column names and types
4.Real-world SQL databases

Dataset Structure

Data Fields

json

{
  "prompt": "You are a data visualization assistant...\n\nSchema:\nTable: sales\nColumns: date, revenue, region\n\nRequest: Show revenue by region\n\nChart spec:",
  "completion": "bar || region || revenue || NONE || SUM"
}

Output Format (Closed-Label)

CHART_TYPE || X_COLUMN || Y_COLUMN || GROUP_COLUMN || AGGREGATE

Valid Values

Field	Valid Options
CHART_TYPE	bar, line, pie, scatter, histogram, heatmap, area, boxplot
X_COLUMN	Column name from schema or NONE
Y_COLUMN	Column name from schema or NONE
GROUP_COLUMN	Column name from schema or NONE
AGGREGATE	SUM, COUNT, AVG, MIN, MAX, NONE

Special Outputs

REFUSE: <reason>     # When request cannot be fulfilled
CLARIFY: <question>  # When more information needed

Dataset Composition

Category	Count	Percentage
Real nvBench + Spider	13,657	78.2%
Hard Negatives	1,500	8.6%
Chart Balance	800	4.6%
Multilingual	600	3.4%
Edge Cases	500	2.9%
Adversarial	400	2.3%

By Chart Type

Chart	Count
bar	~11,000
line	~2,000
pie	~1,500
scatter	~1,200
histogram	~800
heatmap	~500
boxplot	~300
area	~200

By Language

Language	Count
English	~16,200
Hindi	~400
Spanish	~400
French	~400

Data Collection Process

Step 1: nvBench Processing

—Downloaded nvBench.json from TsinghuaDatabaseGroup
—Extracted 7,247 visualization entries
—Mapped chart types to closed vocabulary

Step 2: Spider Schema Resolution

—Downloaded Spider database schemas (tables.json)
—Linked nvBench entries to exact Spider column names
—Validated column existence in schemas

Step 3: COUNT(*) Normalization

—Identified 60.1% of examples with COUNT(column) vs COUNT(*) ambiguity
—Normalized to consistent format (y=NONE, aggregate=COUNT)
—Following methodology from Fernandosr85's 98% submission

Step 4: Hard Negative Generation

—Non-existent column requests → REFUSE
—Invalid chart type requests → REFUSE
—Ambiguous requests → CLARIFY
—Off-topic requests → REFUSE

Step 5: Data Augmentation

—Edge cases (NULL, empty, boundary)
—Multilingual (Hindi, Spanish, French)
—Chart balancing (underrepresented types)
—Adversarial (near-miss traps)

Preprocessing

Cleaning Steps

1.Removed invalid chart types (kept only 8 supported)
2.Normalized column names to Spider schemas
3.Dropped examples with missing required fields
4.Deduplicated near-identical entries

Quality Filters

—Minimum query length: 10 characters
—Maximum 2 NL variations per nvBench entry
—Schema validation against Spider

Considerations for Using the Data

Intended Use

—Fine-tuning LLMs for NL2VIS tasks
—Business intelligence chart generation
—Data visualization assistants

Limitations

—English-centric (85% of examples)
—8 chart types only (no advanced visualizations)
—Requires explicit schema in prompt

Biases

—Overrepresentation of bar charts (nvBench artifact)
—Limited to tabular data (no time-series specific handling)
—Business/academic domains predominant

Additional Information

Dataset Curators

—Ankit Pandey (@pandeyankit99)

Licensing

CC-BY-4.0 (following nvBench and Spider licenses)

Citation

bibtex

@dataset{dataviz99,
  author = {Pandey, Ankit},
  title = {DataViz-99: NL2VIS Dataset for AutoScientist Challenge},
  year = {2026},
  publisher = {Kaggle/HuggingFace},
  note = {Built from nvBench and Spider benchmarks}
}