artitw/text2class
Multi-class text categorization using state-of-the-art pre-trained contextualized language models, e.g. BERT
This tool helps you automatically sort unstructured text into predefined categories, such as customer feedback into topics or articles into departments. You provide raw text examples with their correct categories, and the tool learns to classify new, unlabelled text. This is useful for data scientists, machine learning engineers, and researchers who need to categorize large volumes of text efficiently.
No commits in the last 6 months. Available on PyPI.
Use this if you need to build a text classification model quickly with only a few hundred examples per category, without extensive text preprocessing.
Not ideal if you need to perform more complex natural language tasks beyond multi-class text categorization, such as sentiment analysis or named entity recognition.
Stars
23
Forks
2
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 24, 2023
Commits (30d)
0
Dependencies
3
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