🦆 Use NLP to go beyond vanilla word2vec

What have Spacy's sense2vec 2019 word vectors learned from Reddit?

qlamda: txt2ques generation model

An end-to-end solution on how MCQs can be generated using T5 transformer model, word embeddings and decoding strategies

Neural Sense Embeddings, replicating "SENSEMBED: Learning Sense Embeddings for Word and Relational Similarity", Iacobacci, Pilehvar and Navigli, 2015

Generate MCQ questions from context

A simple web application for searching Word2Vec embeddings derived from approximately 2,000 law reports published by the The Incorporated Council of Law Reporting for England & Wales (https://www.iclr.co.uk).

This is a much more in-depth project of text classification using SpaCy, where Amazon food reviews dataset was used

Chapter 5: Embeddings

🦜 Containerized HTTP API for industrial-strength NLP via spaCy and sense2vec

Easy to use and understand multiple-choice question generation algorithm using T5 Transformers.

🦆 Contextually-keyed word vectors