Metin üretim metriklerini anlama

PyBooks'ta ekip, önceden eğitilmiş bir modelin performansını BLEU skoru ile değerlendirdi ve yaklaşık 0.082, rouge1_fmeasure için de yaklaşık 0.2692 sonucu elde etti. Bu metrikler, kesinlik (seçilen öğelerin ne kadarı ilgili) ve duyarlılığı (ilgili öğelerin ne kadarı seçildi) gösterir. Bu puanları modelin performansı açısından nasıl yorumlarsın?

Bu egzersiz

PyTorch ile Metin için Deep Learning

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

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Bu egzersiz

PyTorch ile Metin için Deep Learning

kursunun bir parçasıdır

AvançadoNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Metin üretimine giriş Exercise 2: Metin üretimi için bir RNN modeli oluşturma Exercise 3: RNN ile metin üretimi - Eğitim ve Üretim Exercise 4: Metin üretimi için üretici çekişmeli ağlar Exercise 5: Bir üreteç ve ayrımcı oluşturma Exercise 6: Bir GAN modelini eğitme Exercise 7: Metin üretimi için önceden eğitilmiş modeller Exercise 8: Önceden eğitilmiş GPT-2 modelleriyle metin tamamlama Exercise 9: Önceden eğitilmiş PyTorch modeliyle dil çevirisi Exercise 10: Metin üretimi için değerlendirme metrikleri Exercise 11: Önceden eğitilmiş metin üretim modelini değerlendirme Exercise 12: Metin üretim metriklerini anlama

Geçerli Egzersiz

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