pipeline-docs-data-extractor VS autolabel

Try this: https://github.com/refuel-ai/autolabel

Then the main challenge just becomes prompt design, which can sometimes be nebulous for NLP annotation.

Wanted to share an open source project we've been working on for the last few weeks: Autolabel is an open source Python library to label and enrich text datasets with LLMs (Large Language Models).

Yep! I totally understand the concerns around not being able to share data externally - the library currently supports open source, self-hosted LLMs through huggingface pipelines (https://github.com/refuel-ai/autolabel/blob/main/src/autolab...), and we plan to add more support here for models like llama cpp that can be run without many constrains on hardware

Label, clean and enrich text datasets with Large Language Models (https://github.com/refuel-ai/autolabel)

>> don't trust that there was no funny business going on in generating the results for this blog

All the datasets and labeling configs used for these experiments are available in our Github repo (https://github.com/refuel-ai/autolabel) as mentioned in the report. Hope these are useful!