2/2 Markdown rendering everywhere: lessons, questions, hints and menus are now displayed with MD formatting, making everything easier to read in the REPL.
The built-in Julia Basics course has been adapted to use these new features.
Check it out in action here: youtu.be/aEboVhipEfg
#JuliaLang
Swirl.jl now supports multi-step code questions and full Markdown rendering! 🌀
1/2 Multi-step questions: you can now design exercises that require multiple code lines, with separate hints for each step. Perfect for walking learners through multi-part problems or short coding tasks.
🌀 Swirl.jl is here!
An interactive learning platform that teaches JuliaLang in the REPL.
✨ Features
🎓 Interactive code checks
📚 Structured lessons
💾 Auto-save
🎮 Reset anytime
For now, Julia Basics is the only course. It will be expanded and new courses are on the way.
github.com/atantos/Swir...
comparison.parameters → tunable parameters used for the building of the "comparison" object.
comparison.effect_sizes → pairwise differences & Cohen’s d
comparison.summary → number of collocates & mean scores
comparison.results → collocates & scores per year (see the table below for the year 1814)
What this call does
1️⃣ Splits the corpus into subcorpora by :year.
2️⃣ Analyzes each subcorpus using LLR to compute collocates of node_word.
3️⃣ Aggregates everything into structured outputs👇
#JuliaLang #CorpusLinguistics #AssociationAnalysis #TextAssociations.jl #NLP #DigitalHumanities
See atantos.github.io/TextAssociat... for the whole data analysis scenario and the whole output.
#JuliaLang #CorpusLinguistics #AssociationAnalysis #TextAssociations.jl #NLP #DigitalHumanities
julia>comparison = compare_subcorpora(
corpus_df,
:year, # Split corpus by year
node_word, # Word to analyze
LLR; # Association (Log-Likelihood Ratio)
windowsize = 5,
minfreq = 1
)
#JuliaLang #CorpusLinguistics #AssociationAnalysis #TextAssociations.jl #NLP #DigitalHumanities
If you’re exploring linguistic change across Austen’s novels (1811–1817), you might want to ask questions like:
👉 Did the verb “might” become more associated with epistemic verbs after 1814?
👉 Which collocates changed most between 1811 and 1817?
This single line in JuliaLang does all the lifting 👇
Here is the mental model for TextAssociations.jl users:
Raw Text / Corpus
↓
ContingencyTable
↓
CorpusContingencyTable
↓
assoc_score(::Type{<:AssociationMetric}, x::AssociationDataFormat; kwargs...)
↓
Metric evaluator (eval_pmi, …)
↓
Result as DataFrame (or Vector if scores_only=true)
Here’s a 37-second sneak peek showing how the TextAssociations.jl package computes a Bayesian-smoothed version of PMI for the word "might" across Jane Austen’s novels, from text normalization and corpus loading to contingency-table construction and metric calculation.
#JuliaLang #CorpusLinguistics
And here is the resulting DataFrame
#JuliaLang #TextAssociations.jl
In the attached snapshot, contingency tables are generated capturing words occurring within a 5-word window around "innovation" and PMI scores quantify their association strength.
The top 5 collocates are then displayed in a DataFrame. #TextAssociations #JuliaLang
Finally announcing Bonito.jl! 🎉 A Julia web framework that went from "neglected prototype" to something I'm genuinely proud of.
Reactive UIs, rich widgets, Makie integration, deploy anywhere (Jupyter/VSCode/web/static HTML)
makie.org/website/blog...
#JuliaLang
The package is now publicly available as an early release (not yet registered in the Julia package registry), and you can already explore its functionality on GitHub:
🔗 github.com/atantos/Text...
Feedback, ideas, and experiments are welcome. 7/7
Many well-known and lesser-known association measures, together with tools for collocation network analysis, are now coming together under one roof complemented by powerful corpus-based functionality. 6/n
revealing what is salient within a given dataset, discourse, or community.
A new #JuliaLang package, TextAssociations.jl, is taking shape, laying the groundwork for exploring how words connect and co-occur in a transparent, data-driven way. 5/n
under one roof, alongside corpus-based functionality.
While embeddings and RAG systems operate within pretrained semantic spaces, association measures remain essential for interpretable, corpus-based insights. They quantify how often words co-occur beyond what chance would predict, 1/4
A new Julia package called TextAssociations is taking shape, building a foundation for studying how words connect and co-occur in a transparent, data-driven way.
Many familiar and less familiar association measures as well as the building collocation network analysis are finally coming together 3/n
While embeddings and RAG systems operate on pretrained semantic spaces, association measures remain essential for interpretable, corpus-based evidence. They quantify how often words co-occur compared to chance, showing what is salient within a specific dataset or community. 2/n
In language-related and digital humanities research, we are often concerned not only with meaning, but also with pattern and context — how words cluster within a discourse or register, across time periods, or within particular language varieties such as dialects and learner corpora. 1/n
Thank you for your kind words!
I’ve created a GitHub repo where I’m translating the #Python code from @rasbt's Build a LLM from Scratch into #JuliaLang. I’d love for Julia enthusiasts to explore it and I’d be eager to see contributions and improvements. Don’t hesitate to ⭐ it! #LLMs #AI
github.com/atantos/Buil...
If you haven't found it already be sure to subscribe to the #JuliaDispatch podcast by @chrisrackauckas.bsky.social and Michael Tiemann! This week's special guest is Παναγιώτης Γεωργακόπουλος to discuss the #PlutoJL project.
youtu.be/TknY3rE1TeE?...
Here is the Medium link:
atantos.medium.com/computing-at...
And if you are not a Medium member, here is the friend link:
atantos.medium.com/computing-at...
#LLMs #Python #JuliaLang #Transformers #Attention #GenAI 2/2
Continuing my journey of translating
@rasbt
's "Build a LLM (from Scratch)" Python code to Julia, my latest Medium article focuses on computing attention weights for all input tokens as discussed in section 3.3.2. #LLMs #Python #JuliaLang #Transformers #Attention #GenAI 1/2
Thanks a lot for sharing my medium article with the Julia discourse — I really appreciate it! 😊 I'm glad to hear there's interest, and I'll take a look at the thread and respond later today!
