13 articles in this category
It starts with a simple plan: use Tool A for initial labeling, export to Tool B for review, then push into production. The tools all claim to support ...
So your team has been running comparative annotation benchmarks for a while. You stack two tools side by side, measure time per document, maybe look a...
You're three months into a new annotation project. The data keeps flowing, but your weekly QA meeting turns into a blame game: the web platform's sche...
So you've got annotations in BRAT, a batch in Prodigy, and a request to merge them into a new platform. The instinct is to write a script that maps ea...
So you've got a stack that uses Prodigy for active learning and Label Studio for consensus review. Or maybe your NLP team adopted spaCy's annotation f...
You have a deadline. The model needs more labeled data. Your annotators are flying through examples—but when you check the agreement scores, they're a...
Annotation workflows are supposed to make data usable. But the moment you hit a sentence like "The bank refused the loan because of the riverbank...
You built a pipeline that pulls labels from two commercial annotation tools plus an in-house model. The output should be richer, but instead you are d...
Annotation projects live or die by pipeline. Pick the flawed rhythm and you drown in rework. Incremental vs. That run fails fast. run is not just pref...
Here is a scene that plays out in data teams everywhere: two annotation systems, both well-chosen, both trusted by their users, producing labels that ...
A few years ago, a startup I advised was tagging 50 customer support tickets a day in a Google Sheet. It worked fine—two annotators, a shared color co...
You are staring at two annotaal dashboards. One staff has labeled 10,000 records in a week—but their inter-annotator agreement (IAA) is a paltry 0.62 ...
Annotation is the quiet bottleneck of supervised learning. You might be a solo researcher labeling 5,000 images for a niche dataset. Or you might be a...