hileon

Sublime Text 2 – Useful Shortcuts (Windows)

General

Ctrl+KB	toggle side bar
Ctrl+Shift+P	command prompt
Ctrl+`	python console
Ctrl+N	new file

Editing

rspeer

"""
This file contains code that, when run on Python 2.7.5 or earlier, creates
a string that should not exist: u'\Udeadbeef'. That's a single "character"
that's illegal in Python because it's outside the valid Unicode range.

It then uses it to crash various things in the Python standard library and
corrupt a database.

On Python 3... well, this file is full of syntax errors on Python 3. But
if you were to change the print statements and byte literals and stuff:

syhw

"""
A deep neural network with or w/o dropout in one file.

License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
"""

import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams

CMCDragonkai

History Data Structures

For stateful applications, there are 5 different ways of managing the history of state:

• No History - Living in the moment. - Examples: Any stateful application that doesn't discards all previous states upon mutation.
• Ad Hoc Snapshotting - Allows restoration to manually saved snapshots. - Examples: Memento Pattern.
• Singleton - Only remembers the previous snapshot, where undoing the undo is just another undo. - Examples: Xerox PARC Bravo.
• 1 Stack - Allows linear undo. - Examples: AtariWriter.
• 2 Stack - Allows linear undo and redo. - Examples: Browser History, Microsoft Word, Adobe Photoshop.

panovr

import argparse
import os
import shutil
import time

import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim

tamuhey

How to calculate the alignment between BERT and spaCy tokens effectively and robustly

site: https://tamuhey.github.io/tokenizations/

Natural Language Processing (NLP) has made great progress in recent years because of neural networks, which allows us to solve various tasks with end-to-end architecture. However, many NLP systems still require language-specific pre- and post-processing, especially in tokenizations. In this article, I describe an algorithm that simplifies calculating correspondence between tokens (e.g. BERT vs. spaCy), one such process. And I introduce Python and Rust libraries that implement this algorithm. Here are the library and the demo site links:

• repo: https://github.com/tamuhey/tokenizations