jsonl2.5 KB

Named-Entity Recognition Dataset — BIO Tags (JSONL)

A token-classification dataset in JSON Lines — 16 tokenized sentences with aligned BIO tags for person, organisation, and location entities. All names, companies, and places are fictional. A fixture for NER model training and sequence-labelling tooling.

ML training data NLP datasets JSON parsing

Preview — first 17 linesjsonl

{"tokens": ["Maria", "Santos", "joined", "Acme", "Robotics", "in", "Berlin", "last", "spring", "."], "tags": ["B-PER", "I-PER", "O", "B-ORG", "I-ORG", "O", "B-LOC", "O", "O", "O"]}
{"tokens": ["The", "conference", "will", "be", "held", "in", "Lisbon", "next", "March", "."], "tags": ["O", "O", "O", "O", "O", "O", "B-LOC", "O", "O", "O"]}
{"tokens": ["Northwind", "Labs", "hired", "Kenji", "Tanaka", "as", "lead", "engineer", "."], "tags": ["B-ORG", "I-ORG", "O", "B-PER", "I-PER", "O", "O", "O", "O"]}
{"tokens": ["Priya", "Nair", "flew", "from", "Mumbai", "to", "Toronto", "on", "Friday", "."], "tags": ["B-PER", "I-PER", "O", "O", "B-LOC", "O", "B-LOC", "O", "O", "O"]}
{"tokens": ["Globex", "opened", "a", "new", "office", "in", "Nairobi", "."], "tags": ["B-ORG", "O", "O", "O", "O", "O", "B-LOC", "O"]}
{"tokens": ["Diego", "Alvarez", "and", "Sofia", "Rossi", "presented", "at", "the", "summit", "."], "tags": ["B-PER", "I-PER", "O", "B-PER", "I-PER", "O", "O", "O", "O", "O"]}
{"tokens": ["The", "team", "at", "Initech", "shipped", "the", "release", "from", "Austin", "."], "tags": ["O", "O", "O", "B-ORG", "O", "O", "O", "O", "B-LOC", "O"]}
{"tokens": ["Amara", "Okafor", "leads", "research", "at", "Umbrella", "Analytics", "."], "tags": ["B-PER", "I-PER", "O", "O", "O", "B-ORG", "I-ORG", "O"]}
{"tokens": ["Visitors", "toured", "the", "Hyperion", "campus", "near", "Seattle", "."], "tags": ["O", "O", "O", "B-ORG", "O", "O", "B-LOC", "O"]}
{"tokens": ["Lena", "Kowalski", "moved", "to", "Krakow", "to", "join", "Stark", "Industries", "."], "tags": ["B-PER", "I-PER", "O", "O", "B-LOC", "O", "O", "B-ORG", "I-ORG", "O"]}
{"tokens": ["The", "grant", "was", "awarded", "to", "Wayne", "Foundation", "in", "Gotham", "."], "tags": ["O", "O", "O", "O", "O", "B-ORG", "I-ORG", "O", "B-LOC", "O"]}
{"tokens": ["Omar", "Haddad", "reviewed", "the", "proposal", "on", "Monday", "."], "tags": ["B-PER", "I-PER", "O", "O", "O", "O", "O", "O"]}
{"tokens": ["Cyberdyne", "Systems", "relocated", "from", "Sunnyvale", "to", "Denver", "."], "tags": ["B-ORG", "I-ORG", "O", "O", "B-LOC", "O", "B-LOC", "O"]}
{"tokens": ["Fatima", "Zahra", "met", "with", "investors", "in", "Dubai", "."], "tags": ["B-PER", "I-PER", "O", "O", "O", "O", "B-LOC", "O"]}
{"tokens": ["The", "keynote", "was", "delivered", "by", "Ravi", "Menon", "."], "tags": ["O", "O", "O", "O", "O", "B-PER", "I-PER", "O"]}
{"tokens": ["Soylent", "Corp", "and", "Tyrell", "merged", "their", "London", "divisions", "."], "tags": ["B-ORG", "I-ORG", "O", "B-ORG", "O", "O", "B-LOC", "O", "O"]}

Specifications

Records: 16
Scheme: BIO
Entities: PER, ORG, LOC
Schema: tokens[], tags[]
Task: token classification

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

“Named-Entity Recognition Dataset — BIO Tags (JSONL)” is a deterministic Novus Examples fixture for ML training data, NLP datasets, JSON parsing. Labelled, synthetic datasets in the shapes ML pipelines expect — JSONL for text tasks, image annotations, embeddings, and sample weights — for testing data loaders, tokenizers, and training tooling.

Documented properties for this file: 16 records · schema: tokens[], tags[] · task: token classification. 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("ner-bio.jsonl") as f:
    rows = [json.loads(line) for line in f]
print(len(rows), rows[0])

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