Advertising Aerospace & Defense Apparel Retail Apparel, Accessories & Luxury Goods Application Software Asset Management & Custody Banks Auto Parts & Equipment Biotechnology Building Products Casinos & Gaming Commodity Chemicals Communications Equipment Construction & Engineering Construction Machinery & Heavy Trucks Consumer Finance Data Processing & Outsourced Services Diversified Metals & Mining Diversified Support Services Electric Utilities Electrical Components & Equipment Electronic Equipment & Instruments Environmental & Facilities Services Gold Health Care Equipment Health Care Facilities Health Care Services Health Care Supplies Health Care Technology Homebuilding Hotels, Resorts & Cruise Lines Human Resource & Employment Services IT Consulting & Other Services Industrial Machinery Integrated Telecommunication Services Interactive Media & Services Internet & Direct Marketing Retail Internet Services & Infrastructure Investment Banking & Brokerage Leisure Products Life Sciences Tools & Services Movies & Entertainment Oil & Gas Equipment & Services Oil & Gas Exploration & Production Oil & Gas Refining & Marketing Oil & Gas Storage & Transportation Packaged Foods & Meats Personal Products Pharmaceuticals Property & Casualty Insurance Real Estate Operating Companies Regional Banks Research & Consulting Services Restaurants Semiconductors Specialty Chemicals Specialty Stores Steel Systems Software Technology Distributors Technology Hardware, Storage & Peripherals Thrifts & Mortgage Finance Trading Companies & Distributors
---
language: "en"
thumbnail: "https://huggingface.co/sampathkethineedi"
tags:
- distilbert
- pytorch
- tensorflow
- text-classification
- industry
- buisiness
- description
- multi-class 
- classification
liscence: "mit"
inference: false
---

# industry-classification

## Model description

DistilBERT Model to classify a business description into one of **62 industry tags**. 
Trained on 7000 samples of Business Descriptions and associated labels of companies in India.

## How to use

PyTorch and TF models available

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

tokenizer = AutoTokenizer.from_pretrained("sampathkethineedi/industry-classification")  
model = AutoModelForSequenceClassification.from_pretrained("sampathkethineedi/industry-classification")

industry_tags = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
industry_tags("Stellar Capital Services Limited is an India-based non-banking financial company ... loan against property, management consultancy, personal loans and unsecured loans.")

'''Ouput'''
[{'label': 'Consumer Finance', 'score': 0.9841355681419373}]
```

## Limitations and bias
Training data is only for Indian companies
