Initial upload: v1/v2/v3 training and test sets with dataset card

.gitattributes

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 # Video files - compressed
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README.md

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+---
+language:
+  - en
+license: mit
+task_categories:
+  - text-classification
+tags:
+  - semantic-type-detection
+  - data-profiling
+  - synthetic-data
+  - text-classification
+size_categories:
+  - 100K<n<1M
+pretty_name: FineType Training Data
+---
+
+# FineType Training Dataset
+
+Synthetic training and evaluation data for [FineType](https://github.com/noon-org/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](https://huggingface.co/noon-org/finetype-char-cnn)
+- **GitHub:** [noon-org/finetype](https://github.com/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
+
+```json
+{
+  "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
+
+```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
+
+```sql
+SELECT * FROM read_json_auto('train.ndjson', format='newline_delimited');
+```
+
+### Load with Nushell
+
+```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
+
+```bibtex
+@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}
+}
+```

train_v3.ndjson

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+size 16068357