autolabel

Why we included this project

If your team is still hand-labeling text samples to train a supervised model, this Python library hands that job to an LLM of your choosing. You point it at your own dataset and pick the model, whether GPT-4, Claude, or a local one; it handles the labeling, cleans noisy rows, and enriches the text through your own pipeline. Confidence scores and error analysis come built in, so you can see which rows the model likely got wrong instead of accepting every label at face value. The bundled benchmark tooling is useful if you run annotation jobs repeatedly or maintain training sets that keep changing, since it lets you compare how different models perform before you commit to one. For data engineers and ML practitioners who want programmatic control over a labeling pipeline rather than a hosted annotation UI, that is the appeal.