With the increase in the complexity of data, and to fulfill the accuracy-related requirements, people started preferring ensemble classifiers. However, the selection of ensemble classifiers is not that easy. We have a lot of ensemble strategies, like: (1) Model Averaging, (2) Weighted Model Averaging, (3) Majority Voting, (4) Bagging, (5) Boosting, (6) Stacking, (7) Blending,
Author: Dr. Niraj Kumar
Actually LSTM supports three-dimensional input. They are – (samples, time steps, features) Samples. One complete sequence is considered as one sample. A batch may contains one or more samples. In NLP, if we are dealing with the text at sentence level (means taking one sentence at a time), then our sample size will be one.
We can divide the Multi-task learning into four layers. Here Shared layer learns jointly learns important features from text input and plays a very important role. Finally, Task-Layer uses this jointly learned features for different task specific predictions. However, in complex Multi-Task learning, the Task layer can use additional features (additional to that learned from
In this blog, I have demonstrated the use of XLNet for a simple emotion classifier. The dataset used in this task contains four emotion classes (0-Anger, 1-Fear, 2-Joy, and 3-Sadness). I have used the XLNet pretrained model to classify it. Before going into the details – please check the XLNet basics (covered in the following
