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:

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)

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. 1.YAML definitions specify each type's format, regex pattern, DuckDB cast expression, and example values
2. 2.Rust generators produce synthetic examples with:
3. 3.Locale-aware formatting (16+ locales for dates, addresses, phone numbers)
4. 4.Priority-weighted sampling (common formats appear more frequently)
5. 5.Edge case coverage (boundary values, unusual but valid formats)
6. 6.Checksum-valid values where applicable (credit cards via Luhn, IBANs, ISBNs)
7. 7.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}
}