Een zoekaanvulsysteem bouwen

Zoekaanvulling, of auto-complete, is een veelvoorkomende NLP-toepassing in zoekmachines en chatapps. Het doel is om mogelijke aanvullingen te suggereren op basis van gedeeltelijke invoer van een gebruiker. Je opdracht is om Hugging Face’s "text-generation"-pipeline te gebruiken om een eenvoudig auto-complete-systeem te implementeren dat relevante aanvullingen genereert op basis van de query van de gebruiker.

Deze oefening maakt deel uit van de cursus

Natural Language Processing (NLP) in Python

Oefeninstructies

Maak een autocomplete-pipeline met het "distilgpt2"-model.
Genereer vijf zoekquery-suggestions voor de gegeven prompt, waarbij je elke suggestie beperkt tot maximaal acht tokens.

Interactieve oefening met praktijkervaring

Probeer deze oefening door deze voorbeeldcode aan te vullen.

from transformers import pipeline

# Create the pipeline
autocomplete = pipeline(task="____", model="____")

prompt = "Best books to read for"

# Generate search query completions
suggestions = ____

for suggestion in suggestions:
    print(suggestion['generated_text'])

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Natural Language Processing (NLP) in Python

SkillTag.level.intermediateSkillTag.label

4.9+

Begin gratis met de cursus

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 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: Tokenclassificatie Exercise 2: Benoemde entiteiten herkennen in nieuwskoppen Exercise 3: Part-of-speech-tagging voor tekstanalyse Exercise 4: Vraagbeantwoording Exercise 5: Vragen beantwoorden op basis van productbeschrijvingen Exercise 6: Natuurlijke antwoorden genereren met abstractive QA Exercise 7: Taken voor sequentiegeneratie Exercise 8: Nieuwsartikelen samenvatten voor snelle inzichten Exercise 9: Klantbeoordelingen naar het Frans vertalen Exercise 10: Een zoekaanvulsysteem bouwen

Huidige oefening

Exercise 11: Gefeliciteerd