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All guidesHow to run model-assisted labeling and active learning on NER data with a 🤗Hugging Face model
Not all data impacts model performance equally. In fact, a huge roadblock many teams face is being able to leverage automation to speed up labeling to go through even faster data-centric iterations.
In this guide, we'll be showing you how you can efficiently improve models in development and production by using a third-party model, such as 🤗Hugging Face, to guide and identify targeted improvements in your training data to boost model performance.
Try the Colab: Google Colab Notebook
- Learn more about Active Learning and the benefits of prioritizing high-value data
- Learn more about how to import your model predictions & metrics in Labelbox
- Google Colab Notebook (used in the video) to run MAL and active learning on NER data with the Hugging Face model
