Bag-of-Words für Buchtitel

PyBooks hat jetzt eine Liste von Buchtiteln, die für weitere Analysen kodiert werden müssen. Das Data-Team hält das Bag-of-Words-(BoW)-Modell für den besten Ansatz.

Die folgenden Pakete wurden für dich importiert: torch, torchtext.

Diese Übung ist Teil des Kurses

Deep Learning für Text mit PyTorch

Anleitung zur Übung

Importiere die Klasse CountVectorizer, um Bag-of-Words zu implementieren.
Initialisiere ein Objekt der importierten Klasse und verwandle damit die titles in eine Matrixdarstellung.
Extrahiere und zeige die ersten fünf Feature-Namen und kodierten Titel mit der Methode get_feature_names_out() an.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Import from sklearn
from sklearn.feature_extraction.text import ____

titles = ['The Great Gatsby','To Kill a Mockingbird','1984','The Catcher in the Rye','The Hobbit', 'Great Expectations']

# Initialize Bag-of-words with the list of book titles
vectorizer = ____()
bow_encoded_titles = ____.fit_transform(____)

# Extract and print the first five features
print(vectorizer.____[:5])
print(bow_encoded_titles.toarray()[0, :5])

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

Deep Learning für Text mit PyTorch

Hohe SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

This chapter introduces you to deep learning for text and its applications. Learn how to use PyTorch for text processing and get hands-on experience with techniques such as tokenization, stemming, stopword removal, and more. Understand the importance of encoding text data and implement encoding techniques using PyTorch. Finally, consolidate your knowledge by building a text processing pipeline combining these techniques.

Exercise 1: Einführung in die Textvorverarbeitung Exercise 2: Wortfrequenzanalyse Exercise 3: Text vorverarbeiten Exercise 4: Textdaten kodieren Exercise 5: One-hot-codierte Buchtitel Exercise 6: Bag-of-Words für Buchtitel

Aktuelle Übung

Exercise 7: TF-IDF auf Buchbeschreibungen anwenden Exercise 8: Einstieg in den Aufbau einer Textverarbeitungspipeline Exercise 9: Vorverarbeitungspipeline für Shakespeare-Sprache Exercise 10: Shakespeare-Sprachencoder

Explore text classification and its role in Natural Language Processing (NLP). Apply your skills to implement word embeddings and develop both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification using PyTorch, and understand how to evaluate your models using suitable metrics.

Exercise 1: Overview of Text Classification Exercise 2: Embedding in PyTorch Exercise 3: Categorizing text classification tasks Exercise 4: Convolutional neural networks for text classification Exercise 5: Build a CNN model for text Exercise 6: Train a CNN model for text Exercise 7: Testing the Sentiment Analysis CNN Model Exercise 8: Recurrent neural networks for text classification Exercise 9: Building an RNN model for text Exercise 10: Building an LSTM model for text Exercise 11: Building a GRU model for text Exercise 12: Evaluation metrics for text classification Exercise 13: Evaluating RNN classification models Exercise 14: Evaluating the model's performance Exercise 15: Comparing models

Venture into the exciting world of text generation and its applications in NLP. Understand how to leverage Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and pre-trained models for text generation tasks using PyTorch. Alongside, you'll learn to evaluate the performance of your models using relevant metrics.

Exercise 1: Introduction to text generation Exercise 2: Creating a RNN model for text generation Exercise 3: Text generation using RNN - Training and Generation Exercise 4: Generative adversarial networks for text generation Exercise 5: Building a generator and discriminator Exercise 6: Training a GAN model Exercise 7: Pre-trained models for text generation Exercise 8: Text completion with pre-trained GPT-2 models Exercise 9: Language translation with pretrained PyTorch model Exercise 10: Evaluation metrics for text generation Exercise 11: Evaluating pretrained text generation model Exercise 12: Understanding text generation metrics

Understand the concept of transfer learning and its application in text classification. Explore Transformers, their architecture, and how to use them for text classification and generation tasks. You will also delve into attention mechanisms and their role in text processing. Finally, understand the potential impacts of adversarial attacks on text classification models and learn how to protect your models.

Exercise 1: Transfer learning for text classification Exercise 2: Transfer learning using BERT Exercise 3: Evaluating the BERT model Exercise 4: Transformers for text processing Exercise 5: Creating a transformer model Exercise 6: Training and testing the Transformer model Exercise 7: Attention mechanisms for text processing Exercise 8: Creating a RNN model with attention Exercise 9: Training and testing the RNN model with attention Exercise 10: Adversarial attacks on text classification models Exercise 11: Adversarial attack classification Exercise 12: Safeguarding AI at PyBooks Exercise 13: Wrap-up