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Classes are imbalanced. Currently addressed in logistic reg through class_weights param but not directly addressed in BERT. Could generate a balanced dataset & evaluate results on it.
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But with multi class,it doesnt works
by e
class 1 = 2000 trained cases
class 2 = 10 trained cases
class 3 = 400 trained cases
class 4 = 160 trained cases (case: text blue house)
pipeli…
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Hey guys,
I was exploring your code and found a huge class imbalance in the dataset. Your testing split contains 2samples which contribute almost everything to the MSE. Leaving those two out would re…
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- [X] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and [discussions](https://github.com/ultralytics/ultralytics/discussion…
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Hi, I have a use case wherein there are two classes and are hugely imbalanced. How can i fix this issue.
I have used the source code.
Sentiment Analysis with BERT
smi45 updated
3 years ago
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### Describe the issue linked to the documentation
Hi guys,
In the "ROC curve using micro-averaged OvR" part of the doc (https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#r…
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Test Focal Loss:
Label distribution:
0 - 410
1 - 3090
2 - 4037
3 - 3259
4 - 4197
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Hi, is it possible to affect on class weights due to the imbalance of my dataset ( 3 classes have small size and 3 balanced), I would change them accordingly so that the model learns better, or does i…
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Hi,
I have an image classification problem with high class imbalance. In the original Caffe framework, there is an [InfogainLossLayer](http://caffe.berkeleyvision.org/doxygen/classcaffe_1_1Infogain…
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Training of Mask R-CNN in the current implementation can suffer from class imbalance. As all selected training proposals are treated as the same class, objects that are more abundant than others will …