In this post, we'll see how to use state-of-the-art language models to perform downstream NLP tasks with Transformers.

In this post, we will use Pytorch to train a NLP Sincereness Detector which will detect whether a question is asked sincerely or not.

(source: https://commons.wikimedia.org/wiki/File:Pytorch_logo.png)

In this post, I will show you how to use BERT as a feature extractor (embedding extractor) and perform text classification on the outputs of BERT.

(source: BERT: Pre-training of Deep Bidirectional Transformers forLanguage Understanding https://arxiv.org/pdf/1810.04805v2.pdf)

In this post, I am not going to discuss the details of the theory behinds these RNNs. Instead, I am going to show you how you can actually apply this RNNs to your application.

Sentiment Analysis refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis on tweets can be extremely useful. By analyzing the tweets, we can find the sentiment of people on certain affair, understand people's opinion. Also it can help us make right strategies/reactions. In this post, our main goal is to build a model to identify tweets with racist or sexist sentiment.