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