synthetic-text-anonymizer-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 Text Anonymizer 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 systems for Text Anonymization** — models that can identify and redact Personally Identifiable Information (PII) from text. Our flagship Text Anonymizer model, [tanaos-text-anonymizer-v1](https://huggingface.co/tanaos/tanaos-text-anonymizer-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 | | `LOCATION` | Geographical areas | | `DATE` | Absolute or relative dates, including years, months and/or days | | `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-text-anonymizer-dataset-v1") print(dataset["train"][0]) ``` ## Intended Use This dataset is meant for **training, fine-tuning, and evaluating** Named Entity Recognition models for Text Anonymization. Common use cases: - Developing models to automatically redact PII from text data. - Benchmarking the performance of Text Anonymization systems. - Researching techniques for improving NER and PII detection.