Build text classification models ( CBOW and Skip-gram) with FastText in python
FastText has been open-sourced by Facebook in 2016 and with its release, it became the fastest and most accurate library in Python for text classification and word representation. It is to be seen as a substitute for gensim package's word2vec. It includes the implementation of two extremely important methodologies in NLP i.e Continuous Bag of Words and Skip-gram model. Fasttext performs exceptionally well with supervised as well as unsupervised learning.
The tutorial will be divided in following four segments :
0-10 minutes: The talk will begin with explaining the difference between word embeddings generated by word2vec, Glove, Fasttext and how FastText beats all the other libraries with better accuracy and in lesser time.
10-25 minutes: The code will be shown and explained line by line for both the models (CBOW and Skip-gram) on a standard textual labeled dataset with the tips on hyper-parametric tuning to get the best possible results.
25-40 minutes: How to use the pre-trained word embeddings released by FastText on various languages and where to use them. Various use cases of what kind of problems can be solved using FastText in python.
40-45 minutes: For QA session.
This session was classified suitable for expert domain / expert python by the speaker.
