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https://github.com/maxent-ai/zeroshot_topics

Topic Inference with Zeroshot models
https://github.com/maxent-ai/zeroshot_topics

bert data-science huggingface hypernymy-extraction keybert keyword-extraction knowledge-graph labelled-data labelling linguistics machine-learning nli nlp taxonomy text text-classification transformers weak-supervision weakly-supervised-learning zeroshot-learning

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Topic Inference with Zeroshot models

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README 

        
zeroshot_topics

===============



.. image:: https://static.pepy.tech/personalized-badge/zeroshot_topics?period=total&units=international_system&left_color=black&right_color=orange&left_text=Downloads



.. contents:: **Table of Contents**

    :backlinks: none



Introduction

------------



Hand-labelled training sets are expensive and time consuming to create usually. 

Some datasets call for domain expertise (eg: medical/finance datasets etc). 

Given these factors around costs and inflexibility of hand-labelling it would be nice 

if there are tools which can help us get started quickly with minimal labelled dataset - enter weak supervision. 



**But what if you do not have any labelled data at all? is there a way to still label your data automatically in some way?**

That's where **zeroshot_topics** might be useful! to help you to be up and running quickly. 



*zeroshot_topics* let's you do exactly that! it leverages the power of zeroshot-classifiers, transformers & knowledge graphs to automatically suggest labels/topics from your text data. all you need to do is point it towards your data. 



Algorithm

---------



The algorithm contains, 4 stages: 



.. image:: assets/zstm.png



1. **Keyword & Keyphrase extraction**: This is done with the help of `KeyBERT `_. but really any sort of keyword extractor can be used.

2. **Keyword/Keyphrase expansion via knowledge graphs/Taxanomy**: Then we expand the important keywords we discovered by using some sort of taxanomy/knowledge graph like wordnet, conceptnet etc. 

3. **Trace the Hypernyms for the keywords**: Identify the Hypernyms(the root/parent word) and use this as the psuedo-label for the zeroshot classifier. 

4. **Zeroshot classification**: Use the Hypernyms and documents to label via zeroshot classifiers. 



Note: Currently, this tends to work well on short-texts in general, in the future I intend to experiment and see how we can support long texts as well. 



Installation

------------



zeroshot_topics is distributed on `PyPI `_ as a universal

wheel and is available on Linux/macOS and Windows and supports

Python 3.7+ and PyPy.



.. code-block:: bash



    $ pip install zeroshot_topics



Usage

------



.. code-block:: python 



    from zeroshot_topics import ZeroShotTopicFinder



    zsmodel = ZeroShotTopicFinder()

    

    text = """can you tell me anything else okay great tell me everything you know about George_Washington. 

    he was the first president he was well he I'm trying to well he fought in the Civil_War he was a general 

    in the Civil_War and chopped down his father's cherry tree when he was a little boy he that's it."""

    

    zsmodel.find_topic(text, n_topic=2)



    # Output - Topics: ['War', 'Head Of State']

    



Roadmap

-------



Some things that i plan to add in the coming days, if there's some interest in this work by the community. 



- Support custom keyword extractors.

- Support Custom Knowledge-graphs & taxonomy.

- Support Custom Zeroshot-classifiers in the pipeline.

- Add Usecase examples & improve documentation.

- Optimise the overall library and make it a faster.

- Support Long Text documents.



License

-------



zeroshot_topics is distributed under the terms of



- `MIT License `_

- `Apache License, Version 2.0 `_