Wortbeziehungen mit Embeddings erkunden

Wort-Embeddings erfassen die Bedeutungen von Wörtern anhand ihrer Verwendung in großen Textdatensätzen. Indem ähnliche Wörter in einem kontinuierlichen Vektorraum näher beieinander liegen, ermöglichen sie Modellen, Kontext und semantische Beziehungen zu erkennen, die einfachere Verfahren nicht abbilden können. Jetzt arbeitest du mit Embeddings, um solche Wortbeziehungen direkt zu erkunden.

Das Wort-Embedding-Modell glove-wiki-gigaword-50 wurde erfolgreich geladen und steht über die Variable model_glove_wiki zur Verfügung.

Diese Übung ist Teil des Kurses

<Kurs>Natural Language Processing (NLP) in Python</Kurs>

Übungsanweisungen

Berechne den Ähnlichkeitswert zwischen "king" und "queen".
Ermittle die 10 ähnlichsten Wörter zu "computer".

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Compute similarity between "king" and "queen"
similarity_score = model_glove_wiki.____

print(similarity_score)

# Get top 10 most similar words to "computer"
similar_words = model_glove_wiki.____

print(similar_words)

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Natural Language Processing (NLP) in Python</Kurs>

Mittlere SchwierigkeitSchwierigkeitsgrad

4.9+

Kurs kostenlos starten

Learn the essentials of text processing in Natural Language Processing (NLP). Master techniques such as tokenization, stop word and punctuation removal, and text normalization with lowercasing, stemming, and lemmatization to prepare text data for further analysis and insight extraction.

Exercise 1: Introduction to natural language processing Exercise 2: Sentence and word tokenization Exercise 3: NLP workflow Exercise 4: Stop words and punctuation handling Exercise 5: Removing stop words Exercise 6: Removing punctuation Exercise 7: Text normalization techniques Exercise 8: Lowercasing Exercise 9: Stemming Exercise 10: Lemmatization

Transform raw text into powerful numerical features. Create Bag-of-Words and TF-IDF representations to capture word importance across documents, then explore word embeddings like Word2Vec and GloVe to uncover deep semantic patterns. Visualize frequency, relevance, and similarity to bring your text data to life.

Exercise 1: Bag-of-Words-Darstellung Exercise 2: Wortschatz aus Kundenrezensionen aufbauen Exercise 3: Text mit BoW in Zahlen umwandeln Exercise 4: Häufigkeitsanalyse von Produktbewertungen Exercise 5: Worthäufigkeiten visualisieren Exercise 6: TF-IDF-Vektorisierung Exercise 7: TF-IDF-Darstellung von Produktfeedback Exercise 8: BoW- und TF-IDF-Repräsentationen vergleichen Exercise 9: Embeddings Exercise 10: Wortbeziehungen mit Embeddings erkunden

Aktuelle Übung

Exercise 11: Wort-Embeddings visualisieren und vergleichen

Harness the power of pre-trained models to perform advanced text classification tasks. Use Hugging Face pipelines for sentiment analysis, topic classification, and natural language inference. Evaluate semantic similarity and grammatical correctness with state-of-the-art models, all without building anything from scratch.

Exercise 1: Hugging Face pipelines for sentiment analysis Exercise 2: Analyzing the sentiment of a review Exercise 3: Batch classifying multiple reviews Exercise 4: Comparing models on labeled review data Exercise 5: Zero-shot classification and QNLI Exercise 6: Zero-shot classification of support tickets Exercise 7: Does the text answer the question?Exercise 8: Question similarity and grammatical correctness Exercise 9: Detecting duplicate questions Exercise 10: Checking grammatical correctness

Dive into the core of modern NLP applications with token classification and text generation techniques. Learn to extract meaningful entities and grammatical structures using NER and PoS tagging. Master both extractive and abstractive question answering, and explore advanced generation tasks including summarization, translation, and language modeling using Hugging Face pipelines.

Exercise 1: Token classification Exercise 2: Identifying named entities in news headlines Exercise 3: Part of Speech tagging for text analysis Exercise 4: Question answering Exercise 5: Answering questions from product descriptions Exercise 6: Generating natural answers with abstractive QA Exercise 7: Sequence generation tasks Exercise 8: Summarizing news articles for quick insights Exercise 9: Translating customer reviews to French Exercise 10: Building a search completion system Exercise 11: Congratulations