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# Model Card for DistilBERT Product Classifier
## Model Overview
**Model Name**: DistilBERT Product Classifier
**Model Type**: Text Classification
**Base Model**: `distilbert-base-uncased`
**Dataset**: Kaggle Product Classification Dataset ([link to dataset](https://www.kaggle.com/datasets/lakritidis/product-classification-and-categorization?select=pricerunner_aggregate.csv))
**Purpose**: This model was fine-tuned for product classification based on raw product titles. It categorizes items into appropriate product categories, helping in scenarios such as data scraping from multiple e-commerce sites where categories may vary widely.
### Why This Model?
The primary goal for this model is to streamline data classification in price comparison and e-commerce applications. By taking product titles from different websites, it allows for accurate category predictions, which can be challenging given the variations across sites.
This model is ideal for use in:
- **E-commerce sites**: …
#Model Card for DistilBertForSequenceClassification_ProductClassifier
##Model Description
Purpose: This model is a product classifier, specifically designed to categorize raw product titles into predefined categories. It's optimized for use with price comparison websites where product categorization can vary greatly across different sources. By accurately categorizing titles from scraped data, the model aims to improve data consistency for these websites.
Model Architecture: Built upon the DistilBERT architecture, this model offers a lightweight and efficient alternative to the original BERT model while maintaining a high level of performance. It utilizes 6 attention heads and a hidden dimension size of 768, making it suitable for deployment on edge devices or resource-constrained systems.
##Training Data:
The model was fine-tuned using a dataset sourced from Kaggle's Product Classification and Categorization challenge. The data underwent preprocessing steps including:
###Cleanin…
