{"info":{"_postman_id":"cf066b0b-4d3c-443f-8324-95eb0d9bdc4d","name":"Heartfulness Literature - Content Similarity Service","description":"

Simple experiment on heartfulness.org mission literature dataset.

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Applied Flair Word + Document Embeddings on a small subset of the given mission literature dataset. Then computed cosine similarity on the embedding vectors. Top 'k' elements from resulting vector are mapped with the content id's and sent back as 'Similar Content' in an REST API.

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Github Repo

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https://github.com/rithinch/heartfulness-similar-content-service

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Tech Stack

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Tech Stack includes Python (pytorch, flair, pandas) + Azure Machine Learning Service for training in cloud and model deployment as webservice.

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Pass in a content id (0-1999) and the service will return top 10 similar paragraphs with scores (including itself on top).

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For simplicity and demonstration purposes, content id's are kept the same as in the data.csv file.

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Senario 3 - Pass in a query (words/sentences/paragraph)\nthe service will return top 10 similar paragraphs (JSON) with cosine similarity scores.

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