Text Classification
Transformers
PyTorch
TensorFlow
English
distilbert
tensorflow
industry
buisiness
description
multi-class
classification
Instructions to use sampathkethineedi/industry-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sampathkethineedi/industry-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sampathkethineedi/industry-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sampathkethineedi/industry-classification") model = AutoModelForSequenceClassification.from_pretrained("sampathkethineedi/industry-classification") - Notebooks
- Google Colab
- Kaggle
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
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
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