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CS260C: Deep Learning
创建日期：2024-04-27 15:21:19
标签： CS260C
Deep Learning

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CS260C: Deep Learning 

Homework 3: Due March 9, 11:59 PM (No Late Submission)
In this homework, you will implement self-attention, and practice fine-tuning BERT on a sentiment analysis
task based on the transformers library.
Requirements
• Please work this Google Colab notebook:
• Submit your notebook (a .ipynb file). On Google Colab, you can download the notebook via:
File → Download → Download .ipynb. Please make sure to keep the output of your final run in
the notebook. It is strictly prohibited to falsify the output.
• Submit a PDF file. Show results of your fine-tuning, inference, and the comparison between fine-tuning
pre-trained BERT and training from scratch.
Part 1 Implementing Self-Attention (30 pts)
In this part, you are asked to implement self-attention in a multi head self attention function. You are
not allowed to use high-level PyTorch functions that are directly designed for self-attention. The correctness of
implementation will be checked by comparing the results with those by torch.nn.MultiheadAttention on a
number of test cases.
Part 2 Training Transformers and Fine-tuning BERT (70 pts)
In this part, we will utilize the transformers  library to fine-
tune pre-trained BERT on a sentiment analysis task. Please get familiar with its usage by reading the following
pages:

For this part, we do not have a skeleton code, and please create your own code cells.
Fine-tuning BERT
Practice fine-tuning BERT on the following setting:
• Dataset: SST-2
• Pre-trained model: distilbert-base-uncased .
Expected test accuracy is at least 90%.
Inference
Propose 4 sentences by yourself, where 2 sentences have positive sentiments, and 2 others have negative senti-
ments. Use these 4 sentences as the input to the model respectively, obtain and discuss the prediction by the
model.
Comparison with Training from Scratch
Compare the performance (test accuracy and inference results with your own input) of fine-tuning with training
from scratch. You will need to use a model with the same architecture as distilbert-base-uncased, while
using random initialization for the parameters rather than loading pre-trained parameters. You
may still use the pre-trained tokenizer.
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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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