AdaptKeyBERT: Stumbling Through Two Years of Keyword Extraction

Running Demo 1

Quick links (in case you want to skip my ramblings):

PyPI Package GitHub Repository

Alright, gather ‘round, word enthusiasts and syntax sorcerers! 🧙‍♂️📚 Remember that time you tried to explain machine learning to your grandma and ended up comparing neural networks to her knitting patterns? Well, buckle up, because we’re about to dive into a similar realm of “What was I thinking?” – the saga of AdaptKeyBERT.

It’s been two trips around the sun since I cobbled together this quirky little keyword extractor and sent it off into the wild world of NLP. And by “sent it off,” I mean I uploaded it to GitHub, patted myself on the back, and promptly got distracted by the next shiny AI puzzle (it was differential privacy and most recently extracting linguistic patterns from startup pitch decks). Classic.

What in the Name of Turing is AdaptKeyBERT?

For those of you who, like me, need a refresher (because who remembers what they coded two years ago!), AdaptKeyBERT is this simple-to-use contraption that’s supposed to pull keywords out of text. Here’s what it allegedly does:

  1. Domain Adaptability: It tries to understand field-specific jargon without having a meltdown.
  2. Few-Shot Learning: Because sometimes you only have like three examples and a prayer.
  3. Zero-Shot Capabilities: For when you’re feeling especially optimistic about AI’s mind-reading abilities.

Surprising Signs of Life

So, get this – apparently, while I’ve been off trying to teach neural networks to be safer against jailbreaks, AdaptKeyBERT has been doing… stuff? Real researchers have been using it. Let’s take a peek at what’s been happening:

A Few Unexpected Mentions

  • Some folks at IEEE gave AdaptKeyBERT a whirl on the DUC2001 dataset. It didn’t crash and burn, which is always a plus. (Check it out here)

  • There was this study on legal text classification where AdaptKeyBERT somehow didn’t embarrass itself completely. Zero-shot and still kicking? Color me surprised. (See for yourself)

  • Apparently, people are using it to analyze central banker speeches. (Economic adventure here)

What’s Next? (Besides Actually Working on It Again)

Look, I’m as surprised as anyone that AdaptKeyBERT is still chugging along. Since it seems determined to stick around, maybe we should think about where it could go next:

  1. Multilingual Mayhem: Teaching it to extract keywords in multiple languages. What could possibly go wrong? Although, this is probably already handled by its zero-shot functionality, just needs benchmarking.

  2. LLM Collaboration: Teaming up with large language models. Maybe they can explain to AdaptKeyBERT what it’s supposed to be doing.

  3. Browser-Based Brainiac: Unleashing a Vanilla JS version that turns your browser into a keyword-crunching machine. Because who needs server-side processing when you can make your laptop fans sound like a jet engine?

Wrapping Up: The Accidental Adventure Continues

Well, there you have it. AdaptKeyBERT: the little keyword extractor that could (sometimes). It’s been a wild ride watching this digital child of mine stumble through the NLP world while I wasn’t looking.

Who knows what the future holds for AdaptKeyBERT? More academic citations? Skynet? An identity crisis? Only time will tell.

P.S. If you’ve somehow used AdaptKeyBERT in your work and it didn’t set your research back by years, I’d love to hear about it! Or if you have ideas on how to make it less of a hot mess, I’m all ears. You can reach me at amanpriyanshusms2001[at]gmail[dot]com. Let’s continue to stumble forward in the name of science! 🚀🤪🧠