17
Can we talk about the moment you realized you were training your model wrong?
For months, I was fine-tuning a small language model on a dataset of about 10,000 support tickets, aiming for better sentiment analysis. I kept hitting a wall at 78% accuracy, no matter what I tried. The tip-off came last week when I read a paper that stressed cleaning your training data for label consistency. I went back and found that nearly 15% of my 'negative' samples were actually neutral or positive, because the original labels were just wrong. It made me wonder, are we all too quick to tweak architectures before checking our data quality first? Has anyone else had a similar 'garbage in, garbage out' wake-up call with their projects?
3 comments
Log in to join the discussion
Log In3 Comments
the_cole23d ago
And then you gotta wonder how many other interns are out there quietly messing up everyone's datasets.
5
west.anna1mo ago
Oh man, this hits way too close to home. Spent a solid week trying to fix a model that kept saying cats were dogs. Turns out the training images were just labeled wrong by some intern. Felt like a total clown for not checking the data first. That whole "garbage in, garbage out" thing is so painfully true. It's like trying to bake a cake with salt instead of sugar and then blaming the oven.
4