Meerdere reviews in batch classificeren

Je sentimentanalyse-pipeline werkt goed op één review. Nu is het tijd om meerdere reviews in één batch te verwerken. Dit is een belangrijke stap voordat je gebruikersfeedback op schaal gaat analyseren.

Deze oefening maakt deel uit van de cursus

Natural Language Processing (NLP) in Python

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Oefeninstructies

Initialiseer een pipeline voor sentiment-analysis met "distilbert-base-uncased-finetuned-sst-2-english".
Gebruik de pipeline om alle reviews in de lijst review_batch te classificeren.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

from transformers import pipeline

classifier = pipeline(task="sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")

review_batch = [
    "Absolutely love the new design!",
    "The app crashes every time I open it.",
    "Customer support was helpful and quick.",
    "Too many ads make it unusable.",
    "Everything works fine, but it’s a bit slow."
]

# Classify sentiments
results = ____
print(results)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Natural Language Processing (NLP) in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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-pijplijnen voor sentimentanalyse Exercise 2: Het sentiment van een review analyseren Exercise 3: Meerdere reviews in batch classificeren

Huidige oefening

Exercise 4: Modellen vergelijken op gelabelde reviewdata Exercise 5: Zero-shot-classificatie en QNLI Exercise 6: Zero-shot-classificatie van supporttickets Exercise 7: Beantwoordt de tekst de vraag?Exercise 8: Overeenkomst tussen vragen en grammaticale correctheid Exercise 9: Dubbele vragen detecteren Exercise 10: Grammaticale correctheid controleren

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