---
language: multilingual
license: mit
tags:
- named-entity-recognition
- NER
- roberta
- small-model
- synthetic-data
- tanaos
- artifex
base_model:
- FacebookAI/roberta-base
datasets:
- tanaos/synthetic-NER-dataset-v1
library_name: transformers
task:
  type: token-classification
  description: "Named Entity Recognition (NER) for extracting entities such as PERSON, ORG, LOCATION, DATE, etc."
---

<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-v1: A small but performant Named Entity Recognition model

This model was created by Tanaos with the [Artifex Python library](https://github.com/tanaos/artifex).

This is a **Named Entity Recognition model** based on [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) and fine-tuned on [a synthetic dataset](https://huggingface.co/datasets/tanaos/synthetic-NER-dataset-v1) to recognize and classify entities in text into the following 14 entity categories:

| Entity | Description |
|--------|-------------|
| `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 |

These entities were chosen to cover a wide range of common named entity types that are useful in various NLP applications, regardless of the specific application domain, in order to create a versatile and general-purpose Named Entity Recognition model, applicable across various industries and use cases.

## How to Use

Use this model through the [Artifex library](https://github.com/tanaos/artifex):

install Artifex with

```bash
pip install artifex
```

use the model with

```python
from artifex import Artifex

ner = Artifex().named_entity_recognition()

named_entities = ner("John landed in Barcelona at 15:45.")
print(named_entities)

# >>> [[{'entity_group': 'PERSON', 'score': 0.92174554, 'word': 'John', 'start': 0, 'end': 4}, {'entity_group': 'LOCATION', 'score': 0.9853817, 'word': ' Barcelona', 'start': 15, 'end': 24}, {'entity_group': 'TIME', 'score': 0.98645407, 'word': ' 15:45.', 'start': 28, 'end': 34}]]
```

## Model Description

- **Base model:** `FacebookAI/roberta-base`
- **Task:** Text classification (Named Entity Recognition)
- **Languages:** English
- **Fine-tuning data:** A synthetic, custom dataset of around 10,000 passages, each containing multiple named entities across 14 categories.

## Training Details

This model was trained using the [Artifex Python library](https://github.com/tanaos/artifex)

```bash
pip install artifex
```

by providing the following instructions and generating 10,000 synthetic training samples:

```python
from artifex import Artifex

ner = Artifex().named_entity_recognition()

ner.train(
    named_entities={
        "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",
    },
    domain="general",
    num_samples=10000
)
```

## Intended Uses

This model is intended to:
- Extract and classify named entities from text in a variety of applications, such as chatbots, information extraction systems, and data analysis tools.
- Be used in multilingual contexts, supporting over 16 languages.
- Serve as a general-purpose NER model applicable across various industries and use cases.

Not intended for:
- Highly specialized domains requiring custom entity types not covered by the 14 categories in this model.
- Idioms, slang, or very informal text where entity recognition may be less reliable.