autolabel

Autolabel is a Python library that automates the labeling, cleaning, and enrichment of text datasets by routing them through large language models via a JSON configuration file. Users define a task type (classification, named entity recognition, question answering, and others), specify labeling guidelines and few-shot examples, select a model provider, then call a LabelingAgent to run the labeling pipeline against a CSV dataset. It connects to Claude through Anthropic's API, supporting models such as claude-3-opus-20240229 alongside OpenAI, Google, and HuggingFace-hosted models. A built-in plan step previews the final prompt and estimates cost before any labels are generated, a practical safeguard for large datasets. The library also includes a benchmarking suite that runs identical prompts across all supported models and outputs a results.csv for direct comparison. The primary audience is machine learning engineers and data scientists who need annotated training data at lower cost and faster turnaround than manual labeling workflows.

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