Fast Open-Source Search & Clustering engine × for Vectors & 🔜 Strings × in C++, C, Python, JavaScript, Rust, Java, Objective-C, Swift, C#, GoLang, and Wolfram 🔍

https://huyenchip.com/ml-interviews-book/

A repository for research on medium sized language models.

contains files and scripts for training InferSent algorithm

A Graph-Based Blocking Approach for Entity Matching Using Contrastively Learned Embeddings

🎁[ChatGPT4NLU] A Comparative Study on ChatGPT and Fine-tuned BERT

•Scraped LinkedIn data using Selenium, cleaned and created schema in Excel. •Analyzed data using SQL, and presented insights via Power BI dashboard. •Used natural language processing to improve ski…

Versatile Generative Language Model

Personal Data Engineering Projects

The corresponding code from our paper "DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations". Do not hesitate to open an issue if you run into any trouble!

Identifying authorship of ancient hebrew texts via word embeddings (skip-gram, LSTM, BERT), unsupervised clustering and evaluation.

Code, notebooks and examples with ECG: Ensemble Clustering for Graphs

Hierarchical graph clustering

Data Engineering with Python, published by Packt

The first type of clustering algorithm discussed in this course used the spatial distribution of points to determine cluster centers and membership. The most prominent implementation of this concep…

A clustering algorithm that automatically determines the number of clusters and works without hyperparameter fine-tuning.

Three modules of extractive text summarization, including implementation of Kmeans clustering using BERT sentence embedding

Combining BERT with Static Word Embedding for Categorizing Social Media

Code and source for paper ``How to Fine-Tune BERT for Text Classification?``

Uniform Manifold Approximation and Projection

Sentence Embeddings in NLI with Iterative Refinement Encoders

A very simple framework for state-of-the-art Natural Language Processing (NLP)

Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org

Performed entity resolution/record linkage using different types of word embedding techniques on E-Commerce datasets.

This project shows how to perform customers segmentation using Machine Learning algorithms. Three techniques will be presented and compared: KMeans, Agglomerative Clustering ,Affinity Propagation a…

Implementation of ExCut: Explainable Embedding-based Clustering over Knowledge Graphs

A Locality Sensitive Hashing (LSH) library with an emphasis on large, highly-dimensional datasets.

deep entity resolution lite version