Hey everyone! I'm diving into NLP projects and I'm curious about how you handle large-scale text labeling efficiently using Python. Do you stick to pure manual labeling with tools like Label Studio or Prodigy? Maybe you use active learning frameworks like modAL or small-text? Or have you created your own batching or heuristics? I'm eager to hear about the practical Python-based approaches that really work for you, especially when balancing accuracy with labeling costs.
1 Answer
Label Studio has great support for active learning! I've been using it with a custom backend where I use a well-trained model if it's available. I start with a pre-annotation (the model's predictions), and if I don't have a good model, I train on around 1000 samples to set up the backend. Then, I review the annotations the model generates, either accepting or correcting them. It makes the process smoother!
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