Tags
The Large Movie Review Dataset (aclImdb v1.0): 50,000 polar movie reviews from IMDB, split evenly for training and testing. For over a decade the default benchmark for binary sentiment classification — simple enough to load with one function call, large enough that word order and negation actually matter.
Provenance
Collected at Stanford and released alongside Maas, Daly, Pham, Huang, Ng and Potts, Learning Word Vectors for Sentiment Analysis, ACL 2011. The dataset page (and download) lives at ai.stanford.edu/~amaas/data/sentiment. The original motivation was learning sentiment-aware word representations; the review corpus it shipped with promptly outlived the method.
Backlinks (3)
1. Magit /wiki/ccs/vc/magit/
It feels a little weird presenting my notes to the world.
Alas, emacs has begun to consume me.
C-x g is magit-status
sections
Repository Status
top of window:
Head: main enh: week49, day1 tutorial, 5 problems
Merge: origin/main enh: week49, day1 tutorial, 5 problems
Head: current local branch Merge / Rebase: depends what has been done thus far.
also gives info on tags and the number of commits between that and HEAD
2. Wiki /wiki/
Knowledge is a paradox. The more one understand, the more one realises the vastness of his ignorance.
“We all die. The goal isn’t to live forever, the goal is to create something that will.” — Chuck Palahniuk
Originally the AI suffix stood for archived intellect, however these days it has concretised to becoming an Augmenting Infrastructure — a place from which to branch out in many directions.
Within this site you will find self-contained material in the form of project posts and blog posts, but also external links 1 to other work – my own as well as not.