This is my attempt to dive into NLP which I have been trying to get my feet wet. The notebooks can be found in nbs directory.
[1] NLP Introduction to Python using spacy and textacy - The notebook is heavily inspired from the book by NLP in Python: Quick Start Guide by Nirant Kasliwal
[2] Entity Extraction from OpenShift Product Documentation - openshift-entities.py - This python scripts reads the file openshift_docs_compact_sample100.jsonl and uses spacy and textacy to extract the entities and writes them to openshift_docs_compact_sample100.out
[3] Product Named Entity Recognition(NER) - This notebook uses Spacy's Phrase Matcher Component to bootstrap custom domain specific product entity recognition and also shows how to add custom extension attributes to the entity.
[4] Advanced NLP with Spacy contains notebooks created based on Ines Montani Spacy Course.
[5] Keyword Phrase Extraction illustrated using OpenShift 4 titles. This notebook uses graph based single rank method(unsupervised) to extract the keyphrases found in OpenShift knowledge base titles.
[6] IMDB Reviews - Sentiment Classification using fastai. This notebook uses pretrained language model wikitext103 in order to perform sentiment classification on IMDB Reviews data set.