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Introduction to NLP

bookChallenge: Creating Word Embeddings

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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'.

Solução

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Seção 4. Capítulo 4
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bookChallenge: Creating Word Embeddings

Tarefa

Swipe to start coding

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'.

Solução

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

Seção 4. Capítulo 4
single

single

close

Awesome!

Completion rate improved to 4.17

bookChallenge: Creating Word Embeddings

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Tarefa

Swipe to start coding

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'.

Solução

Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Tudo estava claro?

Como podemos melhorá-lo?

Obrigado pelo seu feedback!

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