examples VS notebooks

I just published a paper detailing this non-IID check and open-sourced its code in the cleanlab package — just one line of code will check for this and many other types of issues in your dataset.

I recently published a blog introducing Datalab and an open-source Python implementation that is easy-to-use for all data types (image, text, tabular, audio, etc). For data scientists, I’ve made a quick Jupyter tutorial to run Datalab on your own data.

Cool read! I just finished up a notebook where I show how noisy labels can drastically impact the performance of Open AI LLMs. I first fine-tune the well-known Davinci model (the backbone of ChatGPT) on the original data and report an accuracy of 63%. I then use the open-source package cleanlab to find examples that are incorrectly labeled and drop them from the training data. This step increases the fine-tuning accuracy to 66% (better accuracy with less data). Finally, I correct the mislabeled examples and fine-tuning accuracy jumps to 77%!

The entire field of data-centric AI is an active field that is pretty new --- it focuses on the data side of ML as opposed to just model optimization. Our company is building an open-source package cleanlab that is becoming the DCAI standard.

I recently published a paper introducing this novel method and an open-source Python implementation that is easy-to-use for all data types (image, text, tabular, audio, etc). For data scientists, I’ve made a quick Jupyter tutorial to run ActiveLab on your own data. For ML researchers, I’ve made all of our benchmarking code available for reproducibility so you can see for yourself how effective ActiveLab is in practice.

suggest which data is most informative to (re)label next (active learning) (link)

In one-line of python, cleanlab can automatically: 1) find mislabeled data + train robust models 2) detect outliers 3) estimate consensus + annotator-quality for datasets labeled by multiple annotators 4) suggest which data is best to label or re-label next (active learning)

Cool read! I just finished up a notebook where I show how noisy labels can drastically impact the performance of Open AI LLMs. I first fine-tune the well-known Davinci model (the backbone of ChatGPT) on the original data and report an accuracy of 63%. I then use the open-source package cleanlab to find examples that are incorrectly labeled and drop them from the training data. This step increases the fine-tuning accuracy to 66% (better accuracy with less data). Finally, I correct the mislabeled examples and fine-tuning accuracy jumps to 77%!