Jun 20, 2019 · If your target is a multi-hot encoded tensor, then your changes should work. I’m currently unsure, if label is a tensor or if it contains the class names as given in the data frame. Anyway, for a multi-label classification, your target should have the same output shape as the model’s output, containing ones for each active class.
TL;DR Learn how to prepare a dataset with toxic comments for multi-label text classification (tagging). We’ll fine-tune BERT using PyTorch Lightning and evaluate the model. Multi-label text classification (or tagging text) is one of the most common tasks you’ll encounter when doing NLP.
Multi-label text classification (or tagging text) is one of the most common tasks you’ll encounter when doing NLP. Modern Transformer-based models (like BERT) make use of pre-training on vast amounts of text data that makes fine-tuning faster, use fewer resources and more accurate on small(er) datasets. In this tutorial, you’ll learn how to:
Apr 04, 2020 · Multi-Label Image Classification with PyTorch. Back in 2012, a neural network won the ImageNet Large Scale Visual Recognition challenge for the first time. With that Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton revolutionized the area of image classification. Nowadays, the task of assigning a single label to the image (or image ...