Das Sentiment-Analyse-CNN-Modell testen

Jetzt, da das Modell trainiert ist, möchte PyBooks seine Leistung an einigen neuen Buchrezensionen prüfen.

Du sollst herausfinden, ob das Sentiment in einer Rezension positiv oder negativ ist.

Die folgenden Pakete wurden bereits für dich importiert: torch, torch.nn als nn, torch.nn.functional als F, torch.optim als optim.

Eine Instanz von TextClassificationCNN() mit den Argumenten vocab_size und embed_dim wurde ebenfalls geladen und als model gespeichert.

Diese Übung ist Teil des Kurses

Deep Learning für Text mit PyTorch

Anleitung zur Übung

Iteriere über die Liste book_reviews und wandle die Wörter in jeder Rezension in einen Tensor um.
Hole dir die Ausgabe des Modells für jeden input_tensor.
Finde den Index der wahrscheinlichsten Sentiment-Kategorie aus outputs.data.
Extrahiere und konvertiere das Element predicted_label in einen Sentiment-String, wobei 1 ein „Positive“-Label ist.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

book_reviews = [
    "I love this book".split(),
    "I do not like this book".split()
]
for review in book_reviews:
    # Convert the review words into tensor form
    input_tensor = ____.____([word_to_ix[w] for w in review], dtype=torch.long).unsqueeze(0) 
    # Get the model's output
    outputs = model(____)
    # Find the index of the most likely sentiment category
    _, predicted_label = ____.____(outputs.data, 1)
    # Convert the predicted label into a sentiment string
    sentiment = "Positive" if ____ else "Negative"
    print(f"Book Review: {' '.join(review)}")
    print(f"Sentiment: {sentiment}\n")

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: Introduction to preprocessing for text Exercise 2: Word frequency analysis Exercise 3: Preprocessing text Exercise 4: Encoding text data Exercise 5: One-hot encoded book titles Exercise 6: Bag-of-words for book titles Exercise 7: Applying TF-IDF to book descriptions Exercise 8: Introduction to building a text processing pipeline Exercise 9: Shakespearean language preprocessing pipeline Exercise 10: Shakespearean language encoder

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: Überblick über Textklassifikation Exercise 2: Embeddings in PyTorch Exercise 3: Textklassifizierungsaufgaben kategorisieren Exercise 4: Convolutional Neural Networks für die Textklassifizierung Exercise 5: Erstelle ein CNN-Modell für Text Exercise 6: Ein CNN-Modell für Text trainieren Exercise 7: Das Sentiment-Analyse-CNN-Modell testen

Aktuelle Übung

Exercise 8: Rekurrente Neuronale Netze für die Textklassifikation Exercise 9: Ein RNN-Modell für Text bauen Exercise 10: Ein LSTM-Modell für Text aufbauen Exercise 11: Ein GRU-Modell für Text erstellen Exercise 12: Bewertungsmetriken für die Textklassifikation Exercise 13: RNN-Klassifikationsmodelle bewerten Exercise 14: Leistung des Modells bewerten Exercise 15: Modelle vergleichen

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