README.md

---
language: en
license: mit
tags:
- text-classification
- topic-classification
- small-model
- synthetic-data
- tanaos
- artifex
base_model:
- FacebookAI/roberta-base
datasets:
- tanaos/synthetic-topic-classification-dataset-v1
library_name: transformers
---

<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-topic-classification-v1: A small but performant topic classification model

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

This is a **topic classification 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-topic-classification-dataset-v1) to classify text into one of 15 different intent categories:

| Topic | Description |
|--------|-------------|
| `politics` | elections, policies, scandals, ideology. |
| `health` | physical health, mental health, fitness, diets, medical advice. |
| `technology` | gadgets, software, AI, cybersecurity. |
| `entertainment` | movies, TV shows, music, celebrities, streaming platforms. |
| `money_finance` | investing, budgeting, crypto, real estate. |
| `relationships_dating` | romance, breakups, marriage, family drama. |
| `education_learning` | schools, universities, self-study, online courses.,
| `work_careers` | job hunting, workplace culture, remote work, career advice. |
| `science` | research, space, climate, biology, physics, chemistry and the scientific method. |
| `society_culture` | identity, inequality, norms, language, and society. |
| `gaming` | video games, esports, hardware, mods, and gaming culture. |
| `lifestyle_hobbies` | travel, food, fashion, DIY, productivity systems. |
| `sports` | teams, athletes, events, scores, and sports culture. |
| `automotive` | cars, motorcycles, reviews, maintenance, and industry news. |
| `other` | miscellaneous topics not covered by the other categories. |

## 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

topic_classification = Artifex().topic_classification()

topic = topic_classification("What do you think about the latest AI advancements?")
print(topic)

# >>> [{'label': 'technology', 'score': 0.9910}]
```

## Model Description

- **Base model:** `FacebookAI/roberta-base`
- **Task:** Text classification (topic classification)
- **Languages:** English
- **Fine-tuning data:** A synthetic, custom dataset of 10,000 utterances, each belonging to one of 15 different topic 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


topic_classification = Artifex().topic_classification()

topic_classification.train(
    domain="general",
    classes={
        "politics": "elections, policies, scandals, ideology",
        "health": "physical health, mental health, fitness, diets, medical advice.",
        "technology": "gadgets, software, AI, cybersecurity.",
        "entertainment": "movies, TV shows, music, celebrities, streaming platforms.",
        "money_finance": "investing, budgeting, crypto, real estate.",
        "relationships_dating": "romance, breakups, marriage, family drama.",
        "education_learning": "schools, universities, self-study, online courses.",
        "work_careers": "job hunting, workplace culture, remote work, career advice.",
        "science": "research, space, climate, biology, physics, chemistry and the scientific method.",
        "society_culture": "identity, inequality, norms, language, and society.",
        "gaming": "video games, esports, hardware, mods, and gaming culture.",
        "lifestyle_hobbies": "travel, food, fashion, DIY, productivity systems.",
        "sports": "teams, athletes, events, scores, and sports culture.",
        "automotive": "cars, motorcycles, reviews, maintenance, and industry news.",
        "other": "miscellaneous topics not covered by the other categories."
    },
    num_samples=10000
)
```

## Intended Uses

This model is intended to:
- Classify conversations, reviews, articles, or any text into one of the predefined topic categories.
- Be used in applications such as chatbots, content categorization, and sentiment analysis.
- Serve as a lightweight alternative for topic classification tasks.

Not intended for:
- Use cases requiring extremely high accuracy or domain-specific knowledge without further fine-tuning.