Update 2024-02-01-auto-re-label.md

core_research/_posts/2024-02-01-auto-re-label.md

@@ -57,7 +57,7 @@ Step-1. We assume it is a classification task. We have a human-labeled dataset-v
 
 Step-2. Using model-v1 to predict the classification label for dataset-v0. If the predicted label of dataset-v0 (training dataset and dev dataset) do not equal to the human label of dataset-v0, we think they are the noisy data.
 
-Step-3. We randomly re-set each of the noisy data label to model predicted label or human label. Each setting possibility becomes a new dataset with training + dev dataset. Then we get $2^N$ datasets ($dataset-v1, dataset-v2 ... dataset-vN$) and $2^N$ models ($model-v1, model-v2 ... model-vN$).
+Step-3. We randomly re-set each of the noisy data label to model predicted label or human label. Each setting possibility becomes a new dataset with training + dev dataset. Then we get $2^N$ datasets (dataset-v1, dataset-v2 ... dataset-vN) and $2^N$ models (model-v1, model-v2 ... model-vN).
 
 Step-4. We use dev dataset accuracy to select the best dataset from all the $2^N$ datasets. The best dataset becomes the new dataset-v0.