[N] Fine-Tuning OpenAI Language Models with Noisily Labeled Data (37% error reduction)

• label-errors

  7 176 0.0

  🛠️ Corrected Test Sets for ImageNet, MNIST, CIFAR, Caltech-256, QuickDraw, IMDB, Amazon Reviews, 20News, and AudioSet

  

  we be benchmarked the minimum (lower bound) of error detection across the ten most commonly used real world ML datasets and found the lower bound is at least 50% accurate. You can see these errors yourself here: labelerrors.com (all found with cleanlab studio, a more advanced version of the algorithms in confident learning) and this was nominated for best paper award at NeurIPS 2021.

• cleanlab

  69 8,592 9.4 Python

  The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

  

  If you have trained a speech-to-text model and are able to get its probabilistic predictions over the word/token at each position, then you can use the token_classification module in our open-source cleanlab library for this purpose.

• InfluxDB

  www.influxdata.com

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