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COMP4650/6490 Document Analysis
创建日期：2024-08-03 14:33:25
标签： COMP4650
Document Analysis

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COMP4650/6490 Document Analysis


Assignment 2 – ML

In this assignment, you will build several text classification models on a provided movie review

dataset. The dataset consists of 50,000 review articles written movies on IMBD, each labelled

with the sentiment of the review – either positive or negative. In addition there are another 50,000

reviews without labels.

In this assignment you will:

1. Become familiar with the Pytorch framework for implementing neural network-based

machine learning models.

2. Develop a better understanding of how machine learning models are trained in practice,

including partitioning of datasets and evaluation.

3. Study how changing the values of various model hyper-parameters impacts their

performance.

Throughout this assignment you will make changes to the provided code to improve the models.

In addition, you will produce an answers file with your responses to each question. Your answers

file must be a .pdf file named u1234567.pdf where u1234567 is your Uni ID. You should submit a

.zip file containing all of the code files and your answers pdf file, BUT NO DATA.

Your answers to coding questions will be marked based on the quality of your code (is it efficient,

is it readable, is it extendable, is it correct).

Your answers to discussion questions will be marked based on how convincing your

explanations are (are they sufficiently detailed, are they well-reasoned, are they backed by

appropriate evidence, are they clear).

Question 1 – A simple linear classifier (15%)

You have been provided with various code files to create, train and evaluate models on the given

dataset. To begin with, read through the code of and then run train_vector_classifier.py. For the

vector classifier, the documents will be represented as tf-idf vectors and a simple linear classifier

(LogisticRegressor) is trained to predict classes.

Your first task is to change the various settings in this file to try and improve the validation accuracy

of the model. The options you can change are:

1. The pre-processor in preprocessor.py. You can re-use your pre-processor from assignment 1,

or develop a new one.

2. The get_vector_representation function in data_loader.py. This function specifies how lists

of tokens will be transformed into tf-idf vectors. You can find documentation for the

CountVectorizer parameters here: https://scikitlearn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html

3. The settings of the LogisticRegressor model in train_vector_classifier.py. You can find

documentation for the available parameters here: https://scikitlearn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html

Pick three changes you made that you think caused the most increase in performance, and for each

one briefly describe what effect this change has on your model and why you believe this lead to the

increased performance.

Leave your code file with the best performing settings.

Question 2 – Embedding based classifier (15%)

Next you will implement an embedding based classifier in pytorch. The model is a simple variation

on FastText (you can read more about it here if you are interested

https://arxiv.org/pdf/1607.01759.pdf). In models.py there is a class FastText, your task is to

implement the forward function of this class. Once you have implemented it you can then run

train_embedding_classifier.py to run your model.

For each input sentence, the model computes its output in 3 steps:

1. Embed the input IDs to create vector representations of each word in the sentence.

2. Aggregate the vectors together to create one vector that represents the entire sentence.

You can use any aggregation method you want, e.g. sum, max, average (note that average is

significantly more difficult to implement due to padding), etc.

3. Run the aggregated vector through a linear layer to compute predicted logits for each class.

Question 3 – Tuning a pytorch model (15%)

Similarly to question 1, change the various settings of your FastText model to try and improve its

validation accuracy. Here are some things you may want to change:

1. The aggregation method used by FastText.

2. The dimension of the embedding vectors.

3. The optimizer, including learning rate, momentum or even algorithm.

4. The number of epochs trained for.

5. The batch size.

6. The preprocessor.

In your answers report five different settings you tried, along with their accuracies. You may

optionally include some plots of losses over time. For each setting briefly describe why you believe

this led to increased/decreased performance.

Leave your code file with the best performing settings.

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关键词一 关键词二 关键词三 关键词四 关键词五 和法规和的发更好的风格和 ECON 615 FINC 612 CSC148 COMP 639 ACCT 203 ACCT 211 PSYC 101 MTH2009 COMP52315 PSYCO 432 statistics MATH4063 11 xxx 1 MA1MSP MAST30028 MATH3888 CS341 EEE30002 ES2F5 MCD4160 CC0933 ECON 1101 MCHM3001 CHEM3118 COMMGMT 2511 COMP41280 STAT 631 24T1 IRE379 SENG365 ECON433 FIT3139 MATH4602 LING5621M FINS5516 ECON3208 MN2177 L2 SM EEET1415 Finance DS4023 COMP3004 7CCSMRTS K1S5B6 Stat230 CSCI4211 CSI2132 ECON30130 MAS6100 ENVS222 COM2107 INFO1112 STAT 4004 FIT2100 comp2022 COMP4691 Physics 307 Econ 659 probability CSCI316 fir29 CSCI203 COMP3160 COMP6714 INFO1113 Physics 7B Physics Photonics S203031 STAT2005 COMP90015 CSCI 2110 CSCC46 Information CSC477 COMP4650 Statistical COMP 346 COMP1017 ELEC340 MATH1007 Programming function STAT 244 CS 134 Java cse13s Math 108A FW20 ECON3389 CSE 130 Science 331 COMP9020 engineering COSC 445 CSE 5523 GAME2012 visualization MATH 235 Math 370 CSC343 COGS 108 EE524 COMP10200 STAT 416 3FK4 MSCI 311 ECON6113 ACC40700 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