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The function addNewData.R modifies a data frame with a lookup table. This is useful where you want to supplement data loaded from file with other data, e.g. to add details, change treatment names, or similar. The function readNewData is also included. This function runs some checks on the new table to ensure it has correct variable names and val…
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Using <details> <summary> expandable content on GitHub with Markdown #details #summary #markdown #gfm #html
How to use <details><summary> expandable content on GitHub with Markdown
Firstly, what is <details><summary>?
The HTML Details Element (<details>) creates a disclosure widget in which
information is visible only when the widget is toggled into an "open" state.
A summary or label can be provided using the <summary> element.
https://developer.mozilla.org/en-US/docs/Web/HTML/Element/details.
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:
Although sources and sinks are inverse concepts, sources have a one-to-many relationship with downstream relations, while sinks have a one-to-one relationship with upstream relations. Relations have a zero-to-many relationship with downstream sinks, though, which gets in the way of implementing them as inverse dbt concepts (e.g. using pre- and post-hooks).
Something else to consider is that source and sink configuration might have different ownership than model development in the wild (e.g. data engineers vs. analytics engineers), so it'd be preferable not to tightly couple them.
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