📝🔍🏷️ tanaos-NER-v1: A small but performant Named Entity Recognition model

This model was created by Tanaos with the Artifex Python library.

This is a multilingual (it supports 16+ languages) Named Entity Recognition model based on FacebookAI/roberta-base and fine-tuned on a synthetic dataset 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

Via the Artifex library (pip install artifex)

from artifex import Artifex

ner = Artifex().named_entity_recognition

print(ner("John landed in Barcelona at 15:45."))
# >>> [{'entity_group': 'PERSON', 'score': np.float32(0.92174554), 'word': 'John', 'start': 0, 'end': 4}, {'entity_group': 'LOCATION', 'score': np.float32(0.9853817), 'word': ' Barcelona', 'start': 15, 'end': 24}, {'entity_group': 'TIME', 'score': np.float32(0.98645407), 'word': ' 15:45.', 'start': 28, 'end': 34}]

Via the Transformers library

from transformers import pipeline

ner = pipeline(
    task="token-classification",
    model="tanaos/tanaos-NER-v1",
    aggregation_strategy="first"
)

print(ner("John landed in Barcelona at 15:45."))
# >>> [{'entity_group': 'PERSON', 'score': np.float32(0.92174554), 'word': 'John', 'start': 0, 'end': 4}, {'entity_group': 'LOCATION', 'score': np.float32(0.9853817), 'word': ' Barcelona', 'start': 15, 'end': 24}, {'entity_group': 'TIME', 'score': np.float32(0.98645407), 'word': ' 15:45.', 'start': 28, 'end': 34}]

🧠 Model Description

• Base model: FacebookAI/roberta-base
• Task: Text classification (Named Entity Recognition)
• Languages: Multilingual (16+ languages)
• 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

pip install artifex

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

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.