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It reflects the main purpose of the code, which is to perform semantic search on a dataset of text documents using FAISS for indexing and the Universal Sentence Encoder for generating embeddings.
This is a simple model designed to recommend courses based on a Power Summary. This summary integrates the course description, outline, objectives, and difficulty level. Utilizing text embeddings from the Universal Sentence Encoder, this tool aims to provide accurate course identification with ease.
A Natural Language Processing (NLP) model with TensorFlow to segment text lines of abstracts from medical research papers in order to improve readability.
Experiments in the field of Semantic Search using BM-25 Algorithm, Mean of Word Vectors, along with state of the art Transformer based models namely USE and SBERT.
Comprehensive movie data analysis using sentiment analysis, earnings prediction, and a robust movie recommender. Utilizes advanced NLP and machine learning techniques.
This code provides an implementation of clustering text data using the Universal Sentence Encoder (USE) and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. You can provide a list of queries (sentences or words) and it will cluster them for your SEO needs (or other use cases).
Implementing Text Similarity for US Patents using modern day Word2Vec and USE(Universal Sentence Encoding) and some classical algos. like Jaro Winkler and Jaccard