README.md

---
language:
- en
tags:
- text-classification
- e-commerce
- product-classification
- distilbert
license: apache-2.0
datasets:
- lakritidis/product-classification-and-categorization
model-index:
- name: DistilBERT-ProductClassifier
  results:
  - task:
      type: text-classification
      name: Product Category Classification
    dataset:
      name: Product Classification and Categorization
      type: lakritidis/product-classification-and-categorization
    metrics:
    - type: accuracy
      value: 96.17
      name: Accuracy
base_model:
- lxs1/DistilBertForSequenceClassification_6h_768dim
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
---

# Model Card for DistilBERT-ProductClassifier

This is a fine-tuned version of the DistilBERT model, specifically trained for product classification tasks within the e-commerce domain. The model distinguishes between various categories like "CPUs," "Digital Cameras," "Dishwashers," and more, making it useful for organizing and categorizing products in online retail platforms.

## Model Details

### Model Description

The DistilBERT-ProductClassifier model is trained on an e-commerce dataset to classify products into specific categories. It is optimized for efficient text classification tasks and is designed to work well with limited computational resources. This model leverages the compact DistilBERT architecture, making it suitable for real-time applications, including mobile and web environments.

- **Developed by:** Adnan AI Labs
- **Model type:** DistilBERT fine-tuned for text classification
- **Language:** English
- **License:** Apache 2.0
- **Finetuned from model:** [DistilBERT](https://huggingface.co/distilbert-base-uncased)

## Model Sources

- **Repository:** [Adnan-AI-Labs/DistilBERT-ProductClassifier](https://huggingface.co/Adnan-AI-Labs/DistilBERT-ProductClassifier)

## Uses

### Direct Use

The model is intended for classifying products in text-based product listings for e-commerce platforms. Users can use this model to categorize products automatically, reducing the need for manual tagging and improving product discoverability.

### Out-of-Scope Use

This model should not be used for tasks unrelated to product classification or outside the e-commerce context. It is not designed for sentiment analysis, general text generation, or other unrelated NLP tasks.

## Bias, Risks, and Limitations

The model was trained on e-commerce data and may not perform well on products or categories outside the provided training scope. Additionally, some categories may have less representation in the training data, potentially affecting classification accuracy for those classes.

### Recommendations

For use cases involving other languages or highly specialized product categories not included in the training data, further fine-tuning may be necessary. Users should validate the model's output before using it for high-stakes decisions.

## How to Get Started with the Model

Use the code below to get started with the model for product classification:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Adnan-AI-Labs/DistilBERT-ProductClassifier")
model = AutoModelForSequenceClassification.from_pretrained("Adnan-AI-Labs/DistilBERT-ProductClassifier")

# Create a pipeline for text classification
classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)

# Example usage
product_description = "High-resolution digital camera with 20MP sensor."
result = classifier(product_description)
print(result)