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Create Word Embeddings | Word Embeddings
Introduction to NLP
course content

Course Content

Introduction to NLP

Introduction to NLP

1. Text Preprocessing Fundamentals
2. Stemming and Lemmatization
3. Basic Text Models
4. Word Embeddings

Create Word Embeddings

Task

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

Task

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 4. Chapter 4
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Create Word Embeddings

Task

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

Task

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Section 4. Chapter 4
toggle bottom row

Create Word Embeddings

Task

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

Task

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Task

Now, it's time for you to train a Word2Vec model to generate word embeddings for the given corpus:

  1. Import the class for creating a Word2Vec model.
  2. Tokenize each sentence in the 'Document' column of the corpus by splitting each sentence into words separated by whitespaces. Store the result in the sentences variable.
  3. Initialize the Word2Vec model by passing sentences as the first argument and setting the following values as keyword arguments, in this order:
    • embedding size: 50;
    • context window size: 2;
    • minimal frequency of words to include in the model: 1;
    • model: skip-gram.
  4. Print the top-3 most similar words to the word 'bowl'.

Switch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 4
Switch to desktop for real-world practiceContinue from where you are using one of the options below
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