tanaos/synthetic-NER-dataset-v1

📝🔍🏷️ Tanaos NER Training Dataset

This dataset was created synthetically by Tanaos with the Artifex Python library.

The dataset is designed to train and evaluate Named Entity Recognition (NER) systems — models that identify and classify named entities in text into predefined categories such as PERSON, ORG, LOCATION, DATE, and more. It can be used to train NER models from scratch or fine-tune existing ones.

Our flagship NER model, tanaos-NER-v1, was trained on this dataset.

📖 Dataset Summary

The dataset contains text samples labeled with named entity tags. Each sample consists of a sentence or paragraph with entities annotated, word-by-word, according to the following categories:

Entity	Description
O	No entity is present
PERSON	Individual people, fictional characters
ORG	Companies, institutions, agencies
LOCATION	Geographical areas
DATE	Absolute or relative dates, including years, months and/or days
TIME	Specific time of the day
PERCENT	Percentage expressions
NUMBER	Numeric measurements or expressions
FACILITY	Buildings, airports, highways, etc.
PRODUCT	Objects, vehicles, food, etc. bearing a specific name
WORK_OF_ART	Titles of creative works
LANGUAGE	Natural or programming languages
NORP	National, religious or political groups
ADDRESS	Full addresses
PHONE_NUMBER	Telephone numbers

Punctuation and special characters are not labeled.

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⚙️ How to Use

from datasets import load_dataset

dataset = load_dataset("tanaos/synthetic-NER-dataset-v1")

print(dataset["train"][0])

🧠 Intended Use

This dataset is meant for training, fine-tuning, and evaluating models for Named Entity Recognition (NER) tasks.

Common use cases:

• Identifying and classifying named entities in text
• Enhancing information extraction systems
• Improving text understanding in AI applications