Start by July 5, 2023 or earlier | Due on Aug 10, 2023
First start with a task that has a well-defined dataset that you can use for your project. Pick something you are passionate about or something you find interesting.
A list of shared task datasets are provided below. In many cases you can extend your homework code to produce innovative project ideas for these tasks.
For your project proposal please submit a text file in Markdown format that includes a Title and an Abstract. Your abstract should be about 250 words (please definitely use less than 1000 words). Make sure the following points are in your abstract.
There will be a poster session at the SFU Burnaby campus on Aug 10, 2023. Your group must present a poster at this poster session providing details about your final course project.
The poster size should be as follows:
If you use LaTeX then here are two sample poster styles (A0 portrait):
The poster will be graded using the following criteria (1-5):
Please read through this set of tutorial slides on making effective posters.
Also, I have provided two examples of NLP posters (note that they are not in portrait layout).
Apart from the poster session you must also submit your project
write-up as a Python notebook project.ipynb
and your source
code for your project in your GitLab repository advnlpclass-1234-g-GROUP
.
Put all your project files into the directory project
in your
GitLab repository.
Make sure you have a setup.py
or requirements.txt
file for your project
so that we can use a virtual environment to run your code.
Your Python notebook must be called project.ipynb
. In addition
to writing code for a good project submission, the description of
what you did for your project in your Python notebook is also a
very important part of your project submission. It must have
the following sections:
Please read this guide to presenting your work. Also available is a video tutorial covering the same material.
Go to Coursys. Under the Final Project
activity submit the following zip files:
output.zip
: output of your project implementation on a dataset. please include the evaluation code and references to allow us to check the evaluation you present in your write-up. Note this should only be your output on the test data file of some dataset plus any evaluation code and clear instructions on how to run the evaluation script.source.zip
: this zip file should contain your iPython notebook that serves as the write-up for your project and only the source code you have written (along with a requirements.txt for a virtualenv). Do not include any data files in this zipfile. Please also include a README.username file as you have done for all your homeworks in this zip file.The instructions for submission and development are provided in more detail in Homework 0.
There are no grace days for project submission! So submit early and often.
That’s it. You are done with your Final Project!
The final projects for this course will be graded using the following criteria:
The total marks are distributed as follows: