Encoder-Classifier Architecture
The encoder-classifier architecture is a standard approach for text classification in which a classifier neural network is stacked on top of an encoder. This combined structure, mathematically represented as , operates in two steps: the encoder, parameterized by , first converts an input into a numerical representation, and then the classifier, parameterized by , uses that representation to predict the final output label.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Learn After
Encoder Function in Machine Learning
Classifier Head
Inadequacy of Pre-trained Model Parameters for Downstream Tasks
A machine learning model is built to automatically categorize news articles into topics like 'Sports', 'Technology', or 'Politics'. The model first reads the raw text of an article and converts it into a fixed-size numerical vector that summarizes the article's content. This numerical summary is then used to decide which topic the article belongs to. Arrange the following steps to accurately represent the flow of information through this model.
A model is designed to classify text into categories. It works in two stages: first, it generates a fixed-length numerical vector that represents the meaning of the input text. Second, a separate component uses this vector to predict the final category. The model performs poorly on a task requiring it to distinguish between sincere and sarcastic statements. The developers suspect that the numerical vectors for sarcastic statements are not distinct enough from those for sincere statements. Which stage of the model is the primary source of this problem?
Reusability in a Two-Stage Classification Model