Chronological list of the "systemd for Administrators" series published on 0pointer.net/blog:
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import com.github.tminglei.slickpg.ExPostgresProfile | |
import slick.SlickException | |
import slick.ast.ColumnOption.PrimaryKey | |
import slick.ast.{ColumnOption, FieldSymbol, Insert, Node, Select} | |
import slick.compiler.{InsertCompiler, Phase, QueryCompiler} | |
import slick.dbio.{Effect, NoStream} | |
import slick.jdbc.InsertBuilderResult | |
import slick.lifted.Query | |
// format: off |
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#! /usr/bin/env python | |
# -*- coding: utf-8 -*- | |
# vim:fenc=utf-8 | |
# | |
# Copyright © 2019 Pi-Yueh Chuang <[email protected]> | |
# | |
# Distributed under terms of the MIT license. | |
"""An example of using tfp.optimizer.lbfgs_minimize to optimize a TensorFlow model. |
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: