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BERT Word Embeddings

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Goal

This post aims to introduce how to use BERT word embeddings.

Reference

Libraries

In [2]:
import torch
from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM
import matplotlib.pyplot as plt
%matplotlib inline

Load a pre-trained takenizer model

In [3]:
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

100%|██████████| 231508/231508 [00:00<00:00, 426744.34B/s]

Create a sample text

In [10]:
# text = "This is a sample text"
text = "This is the sample sentence for BERT word embeddings"
marked_text = "[CLS] " + text + " [SEP]"

print (marked_text)

[CLS] This is the sample sentence for BERT word embeddings [SEP]

Tokenization

In [11]:
tokenized_text = tokenizer.tokenize(marked_text)
print (tokenized_text)

['[CLS]', 'this', 'is', 'the', 'sample', 'sentence', 'for', 'bert', 'word', 'em', '##bed', '##ding', '##s', '[SEP]']

Convert tokens to ID

In [12]:
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)

for tup in zip(tokenized_text, indexed_tokens):
    print(tup)

('[CLS]', 101)
('this', 2023)
('is', 2003)
('the', 1996)
('sample', 7099)
('sentence', 6251)
('for', 2005)
('bert', 14324)
('word', 2773)
('em', 7861)
('##bed', 8270)
('##ding', 4667)
('##s', 2015)
('[SEP]', 102)

Tokenize Text

Comments

Goal

This post aims to introduce how to tokenize text using nltk.

Reference

Libraries

In [5]:
from nltk.tokenize import sent_tokenize, word_tokenize

Create a sentences

In [8]:
paragraph = "Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aims to help programmers write clear, logical code for small and large-scale projects"
paragraph
Out[8]:
"Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aims to help programmers write clear, logical code for small and large-scale projects"

Tokenize a paragraph into sentences

In [9]:
sent_tokenize(paragraph)
Out[9]:
['Python is an interpreted, high-level, general-purpose programming language.',
 "Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace.",
 'Its language constructs and object-oriented approach aims to help programmers write clear, logical code for small and large-scale projects']

Tokenize a paragraph into words

In [10]:
word_tokenize(paragraph)
Out[10]:
['Python',
 'is',
 'an',
 'interpreted',
 ',',
 'high-level',
 ',',
 'general-purpose',
 'programming',
 'language',
 '.',
 'Created',
 'by',
 'Guido',
 'van',
 'Rossum',
 'and',
 'first',
 'released',
 'in',
 '1991',
 ',',
 'Python',
 "'s",
 'design',
 'philosophy',
 'emphasizes',
 'code',
 'readability',
 'with',
 'its',
 'notable',
 'use',
 'of',
 'significant',
 'whitespace',
 '.',
 'Its',
 'language',
 'constructs',
 'and',
 'object-oriented',
 'approach',
 'aims',
 'to',
 'help',
 'programmers',
 'write',
 'clear',
 ',',
 'logical',
 'code',
 'for',
 'small',
 'and',
 'large-scale',
 'projects']

Remove Punctuation

Comments

Goal

This post aims to introduce how to remove punctuation using string.

Reference

Libraries

In [9]:
import string

Create a document

In [10]:
documents = ["this isn't a sample.", 
            'this is another example.' ,
            'this" also appears in the second example.'
            'Is this an example?']

documents
Out[10]:
["this isn't a sample.",
 'this is another example.',
 'this" also appears in the second example.Is this an example?']

Remove Punctuation

In [11]:
table = str.maketrans('', '', string.punctuation)
doc_removed_punctuation = [w.translate(table) for w in documents]
doc_removed_punctuation
Out[11]:
['this isnt a sample',
 'this is another example',
 'this also appears in the second exampleIs this an example']

Bag Of Words

Comments

Goal

This post aims to introduce Bag of words which can be used as features for each document or images.

Simply, bag of words are frequency of word with associated index for each word.

Libaries

In [17]:
import pandas as pd
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer

Create a document

In [18]:
documents = ['this is a sample.', 
            'this is another example. "this" also appears in the second example.']

documents
Out[18]:
['this is a sample.',
 'this is another example. "this" also appears in the second example.']

Create a count vector

In [19]:
count_vect = CountVectorizer()
word_counts = count_vect.fit_transform(documents)

df_word_counts = pd.DataFrame(word_counts.todense(), columns=count_vect.get_feature_names())
df_word_counts
Out[19]:
also another appears example in is sample second the this
0 0 0 0 0 0 1 1 0 0 1
1 1 1 1 2 1 1 0 1 1 2

Create a frequency vector

In [20]:
tf_transformer = TfidfTransformer(use_idf=False).fit(df_word_counts)
word_freq = tf_transformer.transform(df_word_counts)
df_word_freq = pd.DataFrame(word_freq.todense(), columns=count_vect.get_feature_names())
df_word_freq
Out[20]:
also another appears example in is sample second the this
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.577350 0.57735 0.000000 0.000000 0.577350
1 0.258199 0.258199 0.258199 0.516398 0.258199 0.258199 0.00000 0.258199 0.258199 0.516398

Term Frequency Inverse Document Frequency

Comments

Goal

This post aims to introduce term frequency-inverse document frequency as known as TF-IDF, which indicates the importance of the words in a document considering the frequency of them across multiple documents and used for feature creation.

Term Frequency (TF)

Term Frequency can be computed as the number of occurrence of the word $n_{word}$ divided by the total number of words in a document $N_{word}$.

\begin{equation*} TF = \frac{n_{word}}{N_{word}} \end{equation*}

Document Frequency (DF)

Document Frequency can be computed as the number of documents containing the word $n_{doc}$ divided by the number of documents $N_{doc}$.

\begin{equation*} DF = \frac{n_{doc}}{N_{doc}} \end{equation*}

Inverse Document Frequency (IDF)

The inverse document frequency is the inverse of DF.

\begin{equation*} IDF = \frac{N_{doc}}{n_{doc}} \end{equation*}

Practically, to avoid the explosion of IDF and dividing by zero, IDF can be computed by log format with adding 1 to denominator as below.

\begin{equation*} IDF = log(\frac{N_{doc}}{n_{doc}+1}) \end{equation*}

Reference

Libraries

In [18]:
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer

Create a document

In [36]:
documents = ['this is a sample.', 
            'this is another example. "this" also appears in the second example.']

documents
Out[36]:
['this is a sample.',
 'this is another example. "this" also appears in the second example.']

Apply TF-IDF vectorizer

Now applying TF-IDF to each sentence, we will obtain the feature vector for each document accordingly.

In [37]:
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(documents)
df_tfidf = pd.DataFrame(X.todense(), columns=vectorizer.get_feature_names())
df_tfidf
Out[37]:
also another appears example in is sample second the this
0 0.00000 0.00000 0.00000 0.00000 0.00000 0.501549 0.704909 0.00000 0.00000 0.501549
1 0.28249 0.28249 0.28249 0.56498 0.28249 0.200994 0.000000 0.28249 0.28249 0.401988
In [38]:
# The highest TF-IDF for each document
df_tfidf.idxmax(axis=1)
Out[38]:
0     sample
1    example
dtype: object
In [39]:
# TF-IDF is zero if the word does not appear in a document
df_tfidf==0
Out[39]:
also another appears example in is sample second the this
0 True True True True True False False True True False
1 False False False False False False True False False False

Parse HTML

Comments

Goal

This post aims to introduce how to parse the HTML data fetched by BeautifulSoup

Reference

Library

In [12]:
from bs4 import BeautifulSoup
import requests

Simple HTML from string

In [24]:
html_simple = '<h1>This is Title<h1>'
html_simple
Out[24]:
'<h1>This is Title<h1>'
In [25]:
soup = BeautifulSoup(html_simple)
In [26]:
soup.text
Out[26]:
'This is Title'

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