Clearly I suck at this.
Going forward I have set up calendar events to remind me to do this once a day. Mostly in the morning so I can recap yesterdays discoveries.
NLP MODELS: BUDGET
I was able to update my scripts and models for my personal monthly budget and I am happy to say it only took 75 trainings to get a > 90% model. Nearly all my transactions where labeled properly and fed the budget breakdown instantly. 2025 should be the year of quick budgeting. Which is fantastic considering this monthly task used to take me hours back in 2016.
And to make sure the model can keep learning I implemented a task at the beginning of each run to grab the 75 most recently classified transactions and add them to the training pool for the pre-trained model. The model is then trained again with the added data and used to classify the current months transactions. This ensures that any incorrect classifications from the prior month, after I manually fix them, are added into the models dataset.
An ever learning monthly budgeter.
NLP MODELS: WORK
My goal for the year is to achieve a training dataset of over 75,000 entries. That’s just less than 25,000 new entries from todays starting point. 75,000 entries multiplied by my variance function (AKA the Ponzi Scheme) should net me a training set of more than a million combinations. Elevating the trained models more and more.