python-tensorflowHow can I use Python and TensorFlow to create a Habr article?

Using Python and TensorFlow, you can create a Habr article in the following steps:

1. Create a text corpus of Habr articles using a web scraping library such as BeautifulSoup.

2. Pre-process the text data to prepare it for the TensorFlow model. This includes tokenization, removing stopwords, and lemmatization.

3. Create a TensorFlow model using an RNN (Recurrent Neural Network) to generate text from the corpus.

4. Train the model on the Habr articles until it is able to generate new articles.

5. Use the model to generate a Habr article.

Example code

import tensorflow as tf

# Create the RNN model
model = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128)),
    tf.keras.layers.Dense(vocab_size, activation='softmax')
])

# Compile the model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# Train the model
model.fit(X_train, y_train, epochs=100)

# Generate a Habr article
generated_article = model.predict(X_test[0])

Helpful links

• BeautifulSoup Documentation
• TensorFlow Text Generation Tutorial

Edit this code on GitHub