Zero-shot-Klassifikation von Support-Tickets

Ein Unternehmen erhält täglich Hunderte von Support-Tickets zu Themen wie Abrechnungsproblemen, technischen Störungen und Kontoverwaltung. Diese Tickets manuell zu sortieren, ist ineffizient. Deine Aufgabe ist es, ein Zero-shot-Klassifikationsmodell zu nutzen, um eingehende Ticket-Nachrichten automatisch zu kategorisieren — ohne einen speziell trainierten Klassifikator zu benötigen.

Diese Übung ist Teil des Kurses

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

Übungsanweisungen

Erstelle eine Zero-shot-classifier-Pipeline mit dem Modell "MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli".
Verwende sie, um ticket_text einer der Kategorien in candidate_labels zuzuordnen.

Interaktive praktische Übung

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

from transformers import pipeline

# Initialize the zero-shot classifier
classifier = ____

ticket_text = "I was charged twice for my subscription this month. Can you please refund the extra charge?"
candidate_labels = ["Billing", "Technical Issue", "Account Access"]

# Classify the ticket
result = ____

print(result['labels'])
print(result['scores'])

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 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

Aktuelle Übung

Exercise 7: Beantwortet der Text die Frage?Exercise 8: Frageähnlichkeit und grammatische Korrektheit Exercise 9: Doppelte Fragen erkennen Exercise 10: Grammatikalische Korrektheit überprüfen

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