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.

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.

Specifications

What is a .jsonl file?

How to use this file

Code examples

Related files