Das BERT-Modell auswerten

Nachdem du die Beispielrezensionen mit dem BERT-Tokenizer tokenisiert hast, ist es nun Zeit, das BERT-Modell mit den PyBooks-Beispielen auszuwerten. Zusätzlich überprüfst du die Stimmungsvorhersage des Modells an neuen Daten.

Folgendes wurde für dich importiert: BertTokenizer, BertForSequenceClassification, torch. Die trainierte model-Instanz ist ebenfalls vorab geladen. Wir testen sie jetzt an einem neuen Datenbeispiel.

Diese Übung ist Teil des Kurses

Deep Learning für Text mit PyTorch

Anleitung zur Übung

Bereite den Bewertungstext für das Modell vor, indem du ihn tokenisierst und PyTorch-Tensoren zurückgibst.
Wandle die Ausgabe-Logits in Wahrscheinlichkeiten zwischen null und eins um.
Zeige die Stimmungen basierend auf den Wahrscheinlichkeiten an.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

text = "I had an awesome day!"

# Tokenize the text and return PyTorch tensors
input_eval = tokenizer(____, return_tensors=____, truncation=True, padding=True, max_length=32)
outputs_eval = model(**input_eval)

# Convert the output logits to probabilities
predictions = torch.nn.functional.____(outputs_eval.____, dim=-1)

# Display the sentiments
predicted_label = ____ if torch.____(predictions) > 0 else ____
print(f"Text: {text}\nSentiment: {predicted_label}")

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: 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 für die Textklassifikation Exercise 2: Transfer Learning mit BERT Exercise 3: Das BERT-Modell auswerten

Aktuelle Übung

Exercise 4: Transformer für die Textverarbeitung Exercise 5: Ein Transformermodell erstellen Exercise 6: Transformer-Modell trainieren und testen Exercise 7: Aufmerksamkeitsmechanismen für die Textverarbeitung Exercise 8: Ein RNN-Modell mit Attention erstellen Exercise 9: Training und Testen des RNN-Modells mit Attention Exercise 10: Adversarielle Angriffe auf Textklassifikationsmodelle Exercise 11: Einteilung adversarieller Angriffe Exercise 12: KI absichern bei PyBooks Exercise 13: Abschluss