We’re going to pretend we have an actual product we need to improve. We will explore a dataset and try out different models like logistic regression, recurrent neural networks, and transformers, looking at how accurate they are, how they are going to improve the product, how fast they work, and whether they're easy to debug and scale up. You can read the full case study code on and see the analysis notebook with interactive charts in .
Model: def __init__: super.__init__ self.l1=BertModel.from_pretrained self.l2=nn.Dropout self.l3=nn.Linear def forward: output_1=self.l1 output_2=self.l2 output=self.l3 return output ds_train_bert=bert.get_dataset, list, max_vector_len=64 ) ds_test_bert=bert.get_dataset, list, max_vector_len=64 ) dl_train_bert=DataLoader, batch_size=batch_size) dl_test_bert=DataLoader, batch_size=batch_size) b_model=bert.
is leading in quality, RNN with pre-trained embedding layer is a close second, only falling behind by 3% of automatic category assignments.
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