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---
title: Product Category Classification BERT
emoji: 🌍
colorFrom: green
colorTo: indigo
sdk: streamlit
sdk_version: 1.39.0
app_file: app.py
pinned: false
short_description: Identify Category based on Title and Description
---
# Retail Product Classification Streamlit App
This Streamlit app is a product classification tool built using a BERT model fine-tuned on a retail product dataset. The model can classify products into one of 21 categories based on their title and description.
## Dataset
The model was trained using the [Kaggle Retail Product Classification dataset](https://www.kaggle.com/competitions/retail-products-classification/data). The dataset consists of various product descriptions and their corresponding categories. The training goal was to classify products into 21 distinct categories.
### Categories and Index Mapping
| Category | Index |
|-----------------------------------|-------|
| Electronics | 0 |
| Sports & Outdoors | 1 |
| Cell Phones & Accessories | 2 |
| Automotive | 3 |
| Toys & Games | 4 |
| Tools & Home Improvement | 5 |
| Health & Personal Care | 6 |
| Beauty | 7 |
| Grocery & Gourmet Food | 8 |
| Office Products | 9 |
| Arts, Crafts & Sewing | 10 |
| Pet Supplies | 11 |
| Patio, Lawn & Garden | 12 |
| Clothing, Shoes & Jewelry | 13 |
| Baby | 14 |
| Musical Instruments | 15 |
| Industrial & Scientific | 16 |
| Baby Products | 17 |
| Appliances | 18 |
| All Beauty | 19 |
| All Electronics | 20 |
## Model Training
The model used for this app is a BERT base model (`bert-base-uncased`) fine-tuned using the Hugging Face `transformers` library. The model was trained to classify products into the 21 categories listed above. The fine-tuning was carried out using the following training arguments:
'''python
training_args = TrainingArguments(
output_dir='./results',
evaluation_strategy='epoch',
save_strategy='epoch',
logging_strategy="steps",
logging_steps=10,
per_device_train_batch_size=32,
per_device_eval_batch_size=16,
num_train_epochs=6,
weight_decay=0.01,
learning_rate=2e-5,
lr_scheduler_type='cosine',
warmup_steps=250,
logging_dir='./logs',
report_to="tensorboard",
load_best_model_at_end=True,
save_total_limit=3,
gradient_accumulation_steps=2,
seed=42,
eval_accumulation_steps=10,
)'''
## How to run the APP
Provide any Retail product title and Description in the given text boxes and click on classify product. The app would return the appropriate category |