Forecasters correctly predicted that Joe Biden will win, but vastly underestimated the strength of support for Donald Trump. A quick post-mortem suggests that the polls and forecasts became too bullish after August 31.

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Despite the political drama in the home stretch of the US presidential election, two months-long forecasts have held surprisingly steady in projecting a clear win for Joe Biden on Nov 3.

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Joe Biden and Donald Trump at their final presidential debate on Oct 23. Screen-grab via C-Span.

Trump’s down in the polls, and forecasts by experts point to a decisive loss on November 3. He doesn’t even seem to be doing as well on Twitter as he did in 2016. Is it game over for the White House incumbent?

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With all eyes on the outcome of the Nov 3 vote, which metric, poll or forecaster can accurately predict the outcome of a highly volatile race for the White House? Probably none in isolation, but an aggregate of reputable forecasts might help.

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Screen-cap of first 2020 US Presidential Debate on September 29: C-Span’s YouTube live-feed.


A practical use case on fine tuning a Distilbert model on a custom dataset, and testing its performance against more commonly used models like Logistic Regression and XGBoost

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Illustration of web app by: Chua Chin Hon


Small batch machine translation of speeches and news articles (English-to-Chinese and vice versa) in under-30 lines of code, using Hugging Face’s version of MarianMT.

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Illustration: Chua Chin Hon


AI text generation is one of the most exciting fields in NLP, but also a daunting one for beginners. This post aims to speed up the learning process for newcomers by combining and adapting several existing tutorials into a practical end-to-end walkthrough with notebooks and sample data for a conversational chatbot that can be used in an interactive app.

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Singlish phrases such as “blur as a sotong” can be bewildering for non-Singaporeans. Can a transformer model make sense of it? Photo: Chua Chin Hon


Compared to sentiment analysis or classification, text summarisation is a far less ubiquitous NLP task due to the time and resources needed to execute it well. Hugging Face’s transformers pipeline has changed that. Here’s a quick demo of how you can summarise short and long speeches easily.

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Screen grabs from PAP.org.sg (left) and WP.sg (right).


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Elderly voters queuing up to vote on July 10, 2020, in an election that was shaped by the Covid-19 in form, but not necessarily in substance. Photo: Chua Chin Hon


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A voting booth in Bedok during the 2015 General Election. Photo: Chua Chin Hon

About

Chua Chin Hon

Data Science | Media | Politics

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