tamgu

I implemented many different algorithms for searching and splitting strings using SMID methods as well: https://github.com/naver/tamgu/blob/06aedf2f14895925d7b5a8e2... I used a different algorithm than the ones presented here.

I'm always surprise by this kind of article or comments of people who don't know anything of how LLMs work or what they can do. The problem is that it requires, as it is the case for most tools, some learning curve. Prompting is not always straightforward, and after using these models for a while, you start discerning what should be prompted and what won't work. The best example I have is a documentation that I wrote in Word that I wanted to translate in Mardown on a GitHub site (see https://github.com/naver/tamgu/tree/master/documentations). I split my document into 50 chapters of raw text (360 pages) and I asked chatGPT to add Mardown tags to each of the chapters. Not only did it work very well, but I also asked the same system to automatically translate in French, Spanish, Greek and Korean each of these chapters, keeping the Markdown i

Recently I had to share a documentation written in Word, which I had to format in Markdown to put it on Github. I transformed my document into a text file for each chapter and then I asked chatGPT to transform each of these files into a Markdown page. And I also asked it to improve the English. Then I asked chatGPT to translate each of these files into different languages. The result is here: https://github.com/naver/tamgu/tree/master/documentations Basically, I did in a couple of hours, something that would have taken weeks of tedious work

Lately, I had a manual written in Word that needed to be stored on Github for easier access and translated into different languages. What I did was to extract each chapter as a raw text file and then asked Chat to convert it to Markdown. Not only did it recognize the different headers in the different chapters, but it also recognized the pieces of code and the different keywords that were described in the manual (this a programming language). When I asked it to translate it to French and Spanish, it detected the programming language keywords and did not translated them into French or Spanish, as it would be the case with deepl.com for instance, which usually had a hard time keeping Markdown codes intact. You can see the result here: https://github.com/naver/tamgu/tree/master/documentations

you might want to try this one: https://github.com/naver/tamgu/wiki/7.-Lisp-Interpreter

In my lab, we use more and more a programming language such as tamgu, which has been implemented to benefit from pipes with an integrated editor. (https://github.com/naver/tamgu/blob/master/docs/English/tamg...)