Implement Semantic Search with ML and BERT

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prerequisites
intermediate Python • basic PyTorch • natural language processing tokenization, lemmatization, and cleaning of text data
skills learned
create an inverted index with Faiss library • encode and search documents with a sentence-transformers library • complete question-answering tasks with pretrained BERT models
Olesya Bondarenko
1 week · 9-15 hours per week · INTERMEDIATE
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Look inside
In this liveProject, you’ll apply premade transfer learning models to improve the context understanding of your search. You’ll implement BERT (Bidirectional Encoder Representations from Transformers) to create a semantic search engine. BERT excels in many traditional NLP tasks, like search, summarization and question answering. It will allow your search engine to find documents with terms that are contextually related to what your user is searching for, rather than just exact word occurrence.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

project author

Olesya Bondarenko
Olesya Bondarenko has a multidisciplinary background and experience in natural language processing (NLP), machine learning, deep learning, statistics, time-series analysis, process automation, engineering R&D and new product prototyping. Currently, she is a data scientist at Strong Analytics, a leading provider of customized AI solutions, where she specializes in developing NLP systems. Prior to joining Strong, she worked with several startups leading research and development efforts in the areas of conversational AI, data-leveraged scientific discovery solutions, and a variety of automated analytic and data collection tools. Olesya received her PhD in electrical engineering from the University of California San Diego where she designed and prototyped novel optical devices, as well as custom instrumentation for their analysis.

prerequisites

This liveProject is for intermediate Python programmers familiar with the basics of manipulations with strings, lists and dictionaries. To begin this liveProject you will need to be familiar with:

TOOLS
  • Intermediate Python
  • Basic understanding of conda environments
  • Basic PyTorch
TECHNIQUES
  • Reading data from and writing to JSON files
  • Manipulations with tuples, lists and dictionaries using loops and comprehensions
  • Natural language processing tokenization, lemmatization, and cleaning of text data
  • Basic NumPy array operations
  • Basic understanding of tensor operations and machine learning with PyTorch

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