Challenge: Creating Word Embeddings

Завдання

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

Import the class for creating a Word2Vec model.
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.
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.
Print the top-3 most similar words to the word 'bowl'.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 4. Розділ 4

Завдання

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

Import the class for creating a Word2Vec model.
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.
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.
Print the top-3 most similar words to the word 'bowl'.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 4. Розділ 4

Завдання

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

Import the class for creating a Word2Vec model.
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.
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.
Print the top-3 most similar words to the word 'bowl'.

Все було зрозуміло?

Дякуємо за ваш відгук!

Завдання

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

Import the class for creating a Word2Vec model.
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.
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.
Print the top-3 most similar words to the word 'bowl'.

Секція 4. Розділ 4