Grammatikalische Korrektheit überprüfen

Eine Lern-App wird entwickelt, um Nutzerinnen und Nutzern zu helfen, ihre Grammatik zu verbessern. Ein Kernfeature prüft automatisch, ob von Nutzenden eingereichte Sätze grammatikalisch akzeptabel sind. Du wurdest gebeten, dieses Feature mit einem auf dem Corpus of Linguistic Acceptability (CoLA) trainierten Modell zu implementieren, um die Satzkorrektheit zu klassifizieren.

Diese Übung ist Teil des Kurses

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

Übungsanweisungen

Initialisiere eine classifier-Pipeline mit dem Modell "textattack/bert-base-uncased-CoLA".
Verwende die Pipeline, um zu prüfen, ob der user_text grammatikalisch akzeptabel ist.

Interaktive praktische Übung

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

from transformers import pipeline

# Initialize the pipeline
classifier = ____

user_text = "Although she was knowing the answer, she didn't raised her hand during the class discussion."

# Classify grammatical acceptability
result = ____

print(result)

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

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

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4.9+

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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 representation Exercise 2: Building vocabulary from customer reviews Exercise 3: Transforming text to numbers with BoW Exercise 4: Frequency analysis of product reviews Exercise 5: Visualizing word frequencies Exercise 6: TF-IDF vectorization Exercise 7: TF-IDF representation of product feedback Exercise 8: Comparing BoW and TF-IDF representations Exercise 9: Embeddings Exercise 10: Exploring word relationships with embeddings Exercise 11: Visualizing and comparing word embeddings

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 für Sentimentanalyse Exercise 2: Die Stimmung einer Rezension analysieren Exercise 3: Mehrere Rezensionen im Batch klassifizieren Exercise 4: Modelle mit gelabelten Review-Daten vergleichen Exercise 5: Zero-shot-Klassifikation und QNLI Exercise 6: Zero-shot-Klassifikation von Support-Tickets Exercise 7: Beantwortet der Text die Frage?Exercise 8: Frageähnlichkeit und grammatische Korrektheit Exercise 9: Doppelte Fragen erkennen Exercise 10: Grammatikalische Korrektheit überprüfen

Aktuelle Übung

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