You want to build a text classifier that takes in a sequence of text and outputs its sentiment.You want to use a pretrained model to extract a fixed-size representation from the input textsequence, and train a linear classifier on this representation.You would like this representation to capture information from the entire input sequence.A 10 token sequence (including start/stop tokens) is provided as input to the models below.Mark true for the representations that could satisfy this property.The 5th embedding in the final layer of embeddings of BERT.The 10th embedding in the second layer of embeddings of BERT.The 5th embedding in the final layer of embeddings of a unidirectional Transformer.The 10th embedding in the second layer of embeddings of a unidirectional Transformer.

Answered: You want to build a text classifier…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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You want to build a text classifier that takes in a sequence of text and outputs its sentiment.
You want to use a pretrained model to extract a fixed-size representation from the input text
sequence, and train a linear classifier on this representation.
You would like this representation to capture information from the entire input sequence.
A 10 token sequence (including start/stop tokens) is provided as input to the models below.
Mark true for the representations that could satisfy this property.
The 5th embedding in the final layer of embeddings of BERT.
The 10th embedding in the second layer of embeddings of BERT.
The 5th embedding in the final layer of embeddings of a unidirectional Transformer.
The 10th embedding in the second layer of embeddings of a unidirectional Transformer.

Expert Solution

Step 1

Introduction

BERT:

Google created the BERT (Bidirectional Encoder Representations from Transformers) approach for natural language processing (NLP). It is a particular neural network design that is taught to comprehend and produce human language using a lot of text input. BERT is built on a sort of neural network known as the Transformer architecture, that either leverages self-attention to enable the model to pay attention to various input sequence segments while producing output.

BERT has no particular meaning in networking because it is mostly employed in the area of natural language processing.

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