I'm diving into Natural Language Processing (NLP) projects using Python and I'm curious about the best practices for efficiently labeling large-scale text data. What methods do you rely on? Are you using purely manual labeling tools like Label Studio or Prodigy, leveraging Active Learning frameworks like modAL or small-text, or perhaps you've developed your own custom batching methods? I'm keen to hear what Python-based approaches have worked well for you in real scenarios, especially when weighing the trade-off between accuracy and the cost of labeling.
2 Answers
For me, the best approach has been creating a custom supervised learning heuristic. I started with a small, well-balanced set of manually labeled examples. From there, I converted both the labeled and unlabeled text into vector embeddings with something like Sentence Transformers, then stored them in a vector database like pgvector. I created centroids for each class and performed similarity searches to find unlabeled examples that are similar (at least 0.9 similarity). I manually reviewed these high-confidence matches, added the good ones back into the seed set, and kept iterating. This data-centric approach has really shown me that the quality of my labeled data influences model performance more than just tuning the architecture.
I've found that Label Studio works really well, especially since it supports active learning. I usually set up a custom backend with a model that's trained for my specific task to get pre-annotations. If I don't have a model ready, I train on about 1000 samples; then I use that as a backend to repeat the process of reviewing the annotations and making necessary corrections—works like a charm!
Do you use a single centroid per class or multiple prototypes to cover subclusters? Also, how do you determine the similarity threshold in relation to your human acceptance rate?