Inzicht in evaluatiemetrics voor tekstopwekking

Bij PyBooks heeft het team zojuist de prestaties van een voorgetraind model geëvalueerd met de BLEU-score en kreeg ongeveer 0,082 en een rouge1_fmeasure van rond 0,2692. Deze metric is een indicatie van precisie (hoeveel geselecteerde items relevant zijn) en recall (hoeveel relevante items zijn geselecteerd). Hoe zou je deze score interpreteren in termen van de modelprestaties?

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Deep Learning voor tekst met PyTorch

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Deep Learning voor tekst met PyTorch

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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: Introductie tot tekstgeneratie Exercise 2: Een RNN-model maken voor tekstgeneratie Exercise 3: Tekstgeneratie met RNN - Trainen en genereren Exercise 4: Generative adversarial networks voor tekstgeneratie Exercise 5: Een generator en discriminator bouwen Exercise 6: Een GAN-model trainen Exercise 7: Voorgetrainde modellen voor tekstgeneratie Exercise 8: Tekstaanvulling met voorgetrainde GPT-2-modellen Exercise 9: Taalvertaling met een voorgetraind PyTorch-model Exercise 10: Evaluatiemaatstaven voor tekstgeneratie Exercise 11: Een voorgetraind model voor tekstgeneratie evalueren Exercise 12: Inzicht in evaluatiemetrics voor tekstopwekking

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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