🦆 Contextually-keyed word vectors
Easy to use and understand multiple-choice question generation algorithm using T5 Transformers.
🦜 Containerized HTTP API for industrial-strength NLP via spaCy and sense2vec
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).
Chapter 5: Embeddings
This is a much more in-depth project of text classification using SpaCy, where Amazon food reviews dataset was used
An end-to-end solution on how MCQs can be generated using T5 transformer model, word embeddings and decoding strategies
qlamda: txt2ques generation model
Neural Sense Embeddings, replicating "SENSEMBED: Learning Sense Embeddings for Word and Relational Similarity", Iacobacci, Pilehvar and Navigli, 2015
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