mirror of https://github.com/01-edu/public.git
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
nprimo
c2e60afc28
|
11 months ago | |
|---|---|---|
| .. | ||
| README.md | 11 months ago | |
README.md
Exercise 0: Environment and libraries
The exercise is validated if all questions of the exercise are validated
Activate the virtual environment. If you used conda run conda activate your_env.
Run python --version.
Does it print Python 3.x? x >= 8
Do import jupyter, import pandas, import nltk and import sklearn run without any error?
Exercise 1: Lower case
The exercise is validated if all questions of the exercise are validated
For question 1, is the output the following?
0 this is my first nlp exercise
1 wtf!!!!!
Name: text, dtype: object
For question 2, is the output the following?
0 THIS IS MY FIRST NLP EXERCISE
1 WTF!!!!!
Name: text, dtype: object
Exercise 2: Punctuation
For question 1, is validated if the ouptut doesn't contain punctuation !"#$%&'()*+,-./:;<=>?@[]^_`{|}~. Is the previous statement true? Do not take into account the spaces in the output. The output should be as:
Remove this from the sentence
Exercise 3: Tokenization
The exercise is validated if all questions of the exercise are validated
For question 1, is output the following?
['Bitcoin is a cryptocurrency invented in 2008 by an unknown person or group of people using the name Satoshi Nakamoto.',
'The currency began use in 2009 when its implementation was released as open-source software.']
For question 2, is the output the following?
['Bitcoin',
'is',
'a',
'cryptocurrency',
'invented',
'in',
'2008',
'by',
'an',
'unknown',
'person',
'or',
'group',
'of',
'people',
'using',
'the',
'name',
'Satoshi',
'Nakamoto',
'.',
'The',
'currency',
'began',
'use',
'in',
'2009',
'when',
'its',
'implementation',
'was',
'released',
'as',
'open-source',
'software',
'.']
Exercise 4: Stop words
For question 1, is the output the following? (using NLTK)
['The', 'goal', 'exercise', 'learn', 'remove', 'stop', 'words', 'NLTK', '.', 'Stop', 'words', 'usually', 'refers', 'common', 'words', 'language', '.']
Exercise 5: Stemming
For question 1, is the output the following? (using NLTK)
['the', 'interview', 'interview', 'the', 'presid', 'in', 'an', 'interview']
Exercise 6: Text preprocessing
For question 1, is the output the following?
['01',
'edu',
'system',
'present',
'innov',
'curriculum',
'softwar',
'engin',
'program',
'renown',
'industrylead',
'reput',
'curriculum',
'rigor',
'design',
'learn',
'skill',
'digit',
'world',
'technolog',
'industri',
'take',
'differ',
'approach',
'classic',
'teach',
'method',
'today',
'learn',
'facilit',
'collect',
'cocré',
'process',
'profession',
'environ']
Exercise 7: Bag of Word representation
The exercise is validated if all questions of the exercise are validated
For question 1, is the output of the CountVectorizer the following?
<6588x500 sparse matrix of type '<class 'numpy.int64'>'
with 37334 stored elements in Compressed Sparse Row format>
For question 2, is the output of print(count_vecotrized_df.iloc[:3,400:403].to_markdown()) the following?
| | someth | son | song |
|---:|---------:|------:|-------:|
| 0 | 0 | 0 | 0 |
| 1 | 0 | 0 | 0 |
| 2 | 0 | 0 | 0 |
For question 3, is the output matching with the following one?
cant 1
deal 1
end 1
find 1
keep 1
like 1
may 1
say 1
talk 1
Name: 3, dtype: Sparse[int64, 0]
For question 4, is the output matching with the following one?
tomorrow 1126
go 733
day 667
night 641
may 533
tonight 501
see 439
time 429
im 422
get 398
today 389
game 382
saturday 379
friday 375
sunday 368
dtype: int64
For question 5, is the output of print(count_vectorized_df.iloc[350:354,499:501].to_markdown()) the following?
| | your | label |
|----:|-------:|--------:|
| 350 | 0 | 1 |
| 351 | 1 | -1 |
| 352 | 0 | 1 |
| 353 | 0 | 0 |