Arama tamamlama sistemi kurma

Arama tamamlama (auto-complete), arama motorları ve mesajlaşma uygulamalarında kullanılan yaygın bir NLP uygulamasıdır. Amaç, kullanıcının kısmi girdisine göre olası tamamlamalar önermektir. Görevin, Hugging Face'in "text-generation" pipeline'ını kullanarak, kullanıcının sorgusundan ilgili tamamlamalar üreten temel bir otomatik tamamlama sistemi uygulamaktır.

Bu egzersiz

Python ile Natural Language Processing (NLP)

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

"distilgpt2" modeliyle bir autocomplete pipeline'ı oluştur.
Verilen prompt için beş arama sorgusu suggestions üret ve her birini en fazla sekiz belirteçle sınırla.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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'])

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python ile Natural Language Processing (NLP)

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Token sınıflandırma Exercise 2: Haber başlıklarında adlandırılmış varlıkları belirleme Exercise 3: Metin analizi için Sözcük Türü (PoS) etiketleme Exercise 4: Soru yanıtlama Exercise 5: Ürün açıklamalarından soruları yanıtlama Exercise 6: Özetleyici QA ile doğal yanıtlar üretme Exercise 7: Dizi üretim görevleri Exercise 8: Hızlı içgörüler için haber makalelerini özetleme Exercise 9: Müşteri yorumlarını Fransızcaya çevirme Exercise 10: Arama tamamlama sistemi kurma

Geçerli Egzersiz

Exercise 11: Tebrikler