---
library_name: transformers
license: mit
base_model: salimalsazu/smart-category-detector-v1
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smart-category-detector-v1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# smart-category-detector-v1

**Lightweight Transformer-based classifier for short text categorization
(60 categories)**

`smart-category-detector-v1` is a multi-class text
classification model designed to predict one of **60 categories** from
short text inputs such as:

-   product titles
-   marketplace listings
-   event announcements
-   restaurant menu items with prices

The model is optimized for **fast inference and practical categorization
tasks**.

------------------------------------------------------------------------

# Model Details

  Field              Value
  ------------------ ---------------------------------
  Developer          Salim Al Sazu
  Hugging Face       `salimalsazu`
  Model Type         DistilBERT Fine-tuned
  Base Model         `distilbert-base-uncased`
  Task               Multi-class Text Classification
  Categories         60
  Training Samples   \~600,000
  Language           English
  License            MIT

------------------------------------------------------------------------

# Training Dataset

The model was trained on a **curated dataset of approximately 600,000
samples** containing short text entries mapped to one of 60 categories.

### Dataset Structure

  Column     Description
  ---------- -----------------------------
  text       Short input text
  category   Target classification label

Example:

Samsung Galaxy S24 Ultra 512GB Mobile -\> smartphones\
Dhaka International Book Fair 2026 -\> book_fair\
Jamboo Burger Tk 220 -\> burgers\
Lenovo ThinkPad X1 Carbon Laptop -\> laptops\
Chicken Biryani Tk 180 -\> biryani

------------------------------------------------------------------------

# Dataset Distribution

The dataset is divided into **three main groups**, each containing **20
categories**.

## Event Categories (20) --- \~200,000 samples

sports\
music\
tech_conference\
education_seminar\
business_summit\
startup_pitch\
job_fair\
art_exhibition\
cultural_festival\
religious_event\
political_rally\
charity_event\
workshop\
webinar\
networking_event\
book_fair\
food_festival\
fashion_show\
award_ceremony\
hackathon

------------------------------------------------------------------------

## Product Categories (20) --- \~200,000 samples

electronics\
smartphones\
laptops\
fashion_clothing\
shoes\
beauty_cosmetics\
grocery_food\
furniture\
home_appliances\
kitchen_items\
sports_equipment\
books\
toys\
baby_products\
health_supplements\
automotive\
gaming\
jewelry\
office_supplies\
pet_products

------------------------------------------------------------------------

## Restaurant / Menu Categories (20) --- \~200,000 samples

burgers\
pizza\
sandwich_wraps\
fries_sides\
fried_snacks\
street_food\
biryani\
rice_dishes\
noodles_pasta\
curries\
bbq_grill\
seafood_dishes\
breakfast_items\
soups_salads\
cakes_pastries\
ice_cream\
traditional_sweets\
coffee_tea\
soft_drinks\
shakes_smoothies

------------------------------------------------------------------------

# Example Predictions

  Input                                   Prediction
  --------------------------------------- -------------
  Samsung Galaxy S24 Ultra 512GB Mobile   smartphones
  Dhaka International Book Fair 2026      book_fair
  Jamboo Burger Tk 220                    burgers
  HP Pavilion RTX 4060 Gaming Laptop      laptops
  Beef Burger Combo Tk 350                burgers

------------------------------------------------------------------------

# Quick Start

``` python
from transformers import pipeline

clf = pipeline(
    "text-classification",
    model="salimalsazu/smart-category-detector-v1",
    top_k=5
)

print(clf("Samsung Galaxy S24 Ultra 512GB Mobile"))
print(clf("Dhaka International Book Fair 2026"))
print(clf("Jamboo Burger Tk 220"))
```

------------------------------------------------------------------------

# Limitations

-   Works best with **short text**
-   Designed primarily for **English text**
-   Mixed-language inputs may reduce accuracy
-   Limited to **60 predefined categories**
-   Unknown categories may be mapped to the closest label

------------------------------------------------------------------------

# Future Improvements

Potential improvements include:

-   adding real-world marketplace data
-   improving Bangla language support
-   increasing spelling robustness
-   adding an "unknown" label
-   publishing benchmark evaluation metrics

------------------------------------------------------------------------

# Citation

Salim Al Sazu\
smart-category-detector-v1