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Autolabel is a Python library to label, clean and enrich datasets with Large Language Models (LLMs). Autolabel data for NLP tasks such as classification, question-answering and named entity recognition, entity matching and more. Seamlessly use commercial and open-source LLMs from providers such as OpenAI, Anthropic, HuggingFace, Google and more.
Features
- Autolabel data for NLP tasks such as classification, question-answering and named entity-recognition, entity matching and more
- Seamlessly use commercial and open source LLMs from providers such as OpenAI, Anthropic, HuggingFace, Google and more
- Leverage research-proven LLM techniques to boost label quality, such as few-shot learning and chain-of-thought prompting
- Confidence estimation and explanations out of the box for every single output label
- Caching and state management to minimize costs and experimentation time
- You can get started with Autolabel by simpling bringing the dataset you want to label, picking your favorite LLM and writing a few lines of code
Project Samples
Categories
Large Language Models (LLM)License
MIT LicenseFollow Autolabel
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