jsonl347 B

SAMPLE PII NER Spans (JSONL)

Named-entity spans over fictional SAMPLE PII sentences — for redaction/NER tooling tests.

NLP datasets ML training data JSON parsing

Preview — first 3 linesjsonl

{"text": "Jordan Rivera emailed support@meridiansupply.example about order NV-10482.", "entities": [{"start": 0, "end": 13, "label": "PER"}, {"start": 22, "end": 52, "label": "EMAIL"}, {"start": 64, "end": 72, "label": "ORDER"}]}
{"text": "Ship to 100 Sample Street, Portland, OR 97201.", "entities": [{"start": 8, "end": 46, "label": "ADDR"}]}

Specifications

Records: 2
Task: NER
Note: fictional SAMPLE PII spans

What is a .jsonl file?

JSONL (JSON Lines) is a text format where each line is a complete, independent JSON value, allowing records to be streamed and appended without parsing the whole file. It is not itself a JSON array and each line must stand alone. It is common in logging, machine learning datasets, and data pipelines.

How to use this file

Use an example JSONL to test line-by-line streaming parsers, append-and-resume ingestion, and batch pipelines that process one record per line.

How to use this file for testing

“SAMPLE PII NER Spans (JSONL)” is a deterministic Novus Examples fixture for NLP datasets, ML training data, JSON parsing. Sentiment, NER, chat, instruction-tuning, QA, summarization, and translation data in JSON Lines and JSON — for testing NLP loaders, tokenizers, and format converters.

Documented properties for this file: 2 records · task: NER. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.

AI/ML fixtures are fully synthetic with documented schemas — no real people or data. Test data loaders, tokenizers, annotation converters, embedding/vector stores, or eval-metric parsers against the known structure and fixed seeds.

These are labelled, training-shaped fixtures with a documented schema. Test your data loader, tokenizer, or format converter against it; every label and value is synthetic.

Code examples

import json

with open("sample-pii-ner.jsonl") as f:
    rows = [json.loads(line) for line in f]
print(len(rows), rows[0])

Generated by generation/ai_wave_b.py. Free for any use, no attribution required — license.