11–20 of 42 posts
Earlier quoted context omitted.
Yep. Sounds like a Markov chain to me.
Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event.
This is higher order Markov chain
What's the point? I tested it out for around 10 seconds and noticed that it's completely useless.
What I really want is some simple predictive text engine to make writing in English easier (because its not my first language), helping me find more complex words that I don't use because I don't know them well enough.
Your project is delightful -- thank you for sharing. I have explored this realm a bit before [0] [1], but in Python. The tool I made was for personal use, but streaming every keystroke through a network connection added a lot of unnecessary latency.
I used word surprisals (entropy) to calculate the most likely candidates, and gave a boost to words from my own writing (thus, the predictive engine was "fine-tuned" on my writing). The result is a dictionary of words with their probabilities of use. This can be applied to bigrams, too. Your project has me thinking: how could that be pruned, massively, to create the smallest possible structure. Your engine feels like the answer.
My use case is technical writing: you know what you want to say, including long words you have to repeat over again, but you want a quicker way of typing.
But: The demo is not usable for me on my mobile device, the keyboard is missing a tab key.
Markov chains have been a thing for a long time and predictive text was used before LLMs.
Barely got 1 or 2 suggestions while typing that. Am I holding it wrong?
This is cool. I don't want a 1GB+ download and an entire LLM running on my machine (or worse, on someone else machine) to find words. What I really want is some simple predictive text engine to make writing in English easier (because its not my first language), helping me find more complex words that I don't use because I don't know them well enough.
The goal was to mimic the functionality of the stock iOS keyboard. This approach also opens up the possibility of training (or fine-tuning) the model with specialized vocabularies for various professional fields, such as law, medicine, and engineering, to predict, correct, and autocomplete domain-specific terms. I'm exploring whether this addresses a genuine need or is a solution in search of a problem.
Additionally, I'm considering integrating a comprehensive grammar correction feature directly into the keyboard. While there are existing apps with this capability, they rely on online APIs. My aim is to offer a self-contained, local model as an alternative.