DistilBERT Sequence Classification - Industry (distilbert_sequence_classifier_industry)

Description

distilbert_sequence_classifier_industry 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.

Predicted Entities

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

Live Demo Open in Colab Download Copy S3 URI

How to use

document_assembler = DocumentAssembler() \
.setInputCol('text') \
.setOutputCol('document')

tokenizer = Tokenizer() \
.setInputCols(['document']) \
.setOutputCol('token')

sequenceClassifier = DistilBertForSequenceClassification \
.pretrained('distilbert_sequence_classifier_industry', 'en') \
.setInputCols(['token', 'document']) \
.setOutputCol('class') \
.setMaxSentenceLength(512)

pipeline = Pipeline(stages=[
document_assembler, 
tokenizer,
sequenceClassifier    
])

example = spark.createDataFrame([['Stellar Capital Services Limited is an India-based non-banking financial company ... loan against property, management consultancy, personal loans and unsecured loans.']]).toDF("text")
result = pipeline.fit(example).transform(example)

val document_assembler = DocumentAssembler() 
.setInputCol("text") 
.setOutputCol("document")

val tokenizer = Tokenizer() 
.setInputCols("document") 
.setOutputCol("token")

val tokenClassifier = DistilBertForSequenceClassification.pretrained("distilbert_sequence_classifier_industry", "en")
.setInputCols("document", "token")
.setOutputCol("class")
.setMaxSentenceLength(512)

val pipeline = new Pipeline().setStages(Array(document_assembler, tokenizer, sequenceClassifier))

val example = Seq("Stellar Capital Services Limited is an India-based non-banking financial company ... loan against property, management consultancy, personal loans and unsecured loans.").toDS.toDF("text")

val result = pipeline.fit(example).transform(example)

import nlu
nlu.load("en.classify.distilbert_sequence.industry").predict("""Stellar Capital Services Limited is an India-based non-banking financial company ... loan against property, management consultancy, personal loans and unsecured loans.""")

Model Information

Data Source

https://huggingface.co/sampathkethineedi/industry-classification