synthetic-NER-dataset-v1

<p align="center"> <img src="https://raw.githubusercontent.com/tanaos/.github/master/assets/logo.png" width="250px" alt="Tanaos – Train task specific LLMs without training data, for offline NLP and Text Classification"> </p> # Tanaos NER Training Dataset This dataset was created synthetically by Tanaos with the [Artifex](https://github.com/tanaos/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](https://huggingface.co/tanaos/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. --- ## How to Use ```python 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