meridian-online/finetype-training

FineType Training Dataset

Synthetic training and evaluation data for FineType — a semantic type classifier that detects the format of text values (dates, IPs, emails, UUIDs, etc.) from a taxonomy of 151 types.

• Model: noon-org/finetype-char-cnn
• GitHub: noon-org/finetype

Dataset Description

Each example is a (text, classification) pair where:

• text — a string value (e.g., "2024-01-15", "192.168.1.1", "hello@example.com")
• classification — the semantic type label in domain.category.type format (e.g., datetime.date.iso, technology.internet.ip_v4, identity.person.email)

Schema

{
  "classification": "datetime.component.day_of_week",
  "text": "Thursday"
}

One JSON object per line (NDJSON format).

Dataset Versions

Three versions of the dataset are provided, corresponding to training iterations:

Version	Train	Test	Types	Notes
v1	74,500	14,900	149	Initial balanced dataset, 500 per type
v2	75,500	15,100	151	Added 2 types, improved generators
v3	205,500	41,100	151	Extended with tiered model training data

Recommended: Use train.ndjson and test.ndjson (v1) for the flat model, train_v3.ndjson and test_v3.ndjson for tiered models.

Label Distribution

By Domain (v1 train)

Domain	Types	Examples	Description
datetime	46	23,000	Dates, times, timestamps, epochs, components
technology	34	17,000	IPs, MACs, UUIDs, hashes, URLs, file paths
identity	25	12,000	Emails, phones, credit cards, names, SSNs
representation	19	9,000	JSON, CSV, XML, integers, floats, booleans
geography	16	8,000	Coordinates, postal codes, country codes
container	11	5,500	Arrays, key-value pairs, structured formats

All types are balanced at 500 examples per type in v1.

Generation Methodology

Data is generated using type-specific Rust generators defined in the FineType taxonomy:

1. YAML definitions specify each type's format, regex pattern, DuckDB cast expression, and example values
2. Rust generators produce synthetic examples with:
   • Locale-aware formatting (16+ locales for dates, addresses, phone numbers)
   • Priority-weighted sampling (common formats appear more frequently)
   • Edge case coverage (boundary values, unusual but valid formats)
   • Checksum-valid values where applicable (credit cards via Luhn, IBANs, ISBNs)
3. Validation ensures every generated value matches the type's regex pattern and DuckDB cast expression

Generator Quality

• All generators validated against type definitions via finetype check
• Taxonomy alignment verified: every type has a generator, every generator has a type
• 155 automated tests covering generation, inference, and column disambiguation

Usage

Load with Python

import json

with open("train.ndjson") as f:
    data = [json.loads(line) for line in f]

texts = [d["text"] for d in data]
labels = [d["classification"] for d in data]

Load with DuckDB

SELECT * FROM read_json_auto('train.ndjson', format='newline_delimited');

Load with Nushell

open train.ndjson | lines | each { from json }

Limitations

• Synthetic data: All examples are machine-generated, not sampled from real-world datasets. Real-world data may contain formatting variations not covered by generators.
• English-centric: While locale-aware for dates and addresses, the dataset primarily targets English-language data patterns.
• Balanced distribution: Real-world data is highly imbalanced (some types are far more common than others). The balanced training set may not reflect deployment distributions.

Citation

@dataset{finetype_training2026,
  title = {FineType Training Data: Synthetic Examples for Semantic Type Classification},
  author = {Cameron, Hugh},
  year = {2026},
  url = {https://huggingface.co/datasets/noon-org/finetype-training},
  license = {MIT}
}