Training label cleaning (TLC), which consists in devising ranking functions that sort the original training examples in terms of how likely it is that the ...
Dec 31, 2012 · Abstract: In text classification (TC) and other tasks involving supervised learning, labelled data may be scarce or expensive to obtain.
Nov 24, 2023 · I am building a multi class text classifier using distilbert. I have approx 96 categories. I am looking for suggestions on how to improve the accuracy.
This provides a convenient means for the human annotator to revise the training set so as to improve its quality. ... We also evaluate the degradation in ...
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Apr 14, 2022 · Silver labeled data is often pretty noisy. Cleaning up data matters more than people tend to think. Look at actual data to get an idea. How well ...
Dec 19, 2020 · Cleaning text might be useful for BOW models, especially for smaller datasets, because it reduces dimensionality, but as you see it reduces model accuracy for ...
Missing: Training Label
Oct 11, 2021 · Should this be cleaned and replaced before training the model? Because the model can guess that a text will fall into automotive category if ...
Fixing the label issues manually may be time-consuming, but cleanlab can filter these noisy examples and train a model on the remaining clean data for you ...
Jan 31, 2024 · I am building a text classification for salary prediction for data science jobs. I want to achieve at least 70 percent accuracy.
Missing: Cleaning. | Show results with:Cleaning.
Feb 8, 2024 · By addressing noisy and inaccurate labels, Cleanlab.ai unlocks the hidden potential in your data, leading to improved model accuracy, reduced ...