Membangun sistem pelengkapan pencarian

Pelengkapan pencarian, atau pelengkap otomatis (auto-complete), adalah aplikasi NLP yang umum digunakan di mesin pencari dan aplikasi perpesanan. Tujuannya adalah menyarankan kemungkinan pelengkapan berdasarkan masukan parsial pengguna. Tugas Anda adalah menggunakan pipeline "text-generation" dari Hugging Face untuk mengimplementasikan sistem pelengkap otomatis dasar yang menghasilkan pelengkapan relevan dari kueri pengguna.

Latihan ini adalah bagian dari kursus

Natural Language Processing (NLP) in Python

Petunjuk latihan

Buat pipeline autocomplete dengan model "distilgpt2".
Hasilkan lima suggestions kueri pencarian untuk prompt yang diberikan, dengan membatasi masing-masing hingga maksimal delapan token.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

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'])

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Natural Language Processing (NLP) in Python

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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 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: Klasifikasi token Exercise 2: Mengidentifikasi named entity dalam judul berita Exercise 3: Pelabelan Part of Speech untuk analisis teks Exercise 4: Question answering Exercise 5: Menjawab pertanyaan dari deskripsi produk Exercise 6: Menghasilkan jawaban natural dengan QA abstraktif Exercise 7: Tugas pembangkitan sekuens Exercise 8: Meringkas artikel berita untuk wawasan cepat Exercise 9: Menerjemahkan ulasan pelanggan ke bahasa Prancis Exercise 10: Membangun sistem pelengkapan pencarian

Latihan Saat Ini

Exercise 11: Selamat