Creare un sistema di completamento della ricerca

Il completamento della ricerca, o auto-complete, è un'applicazione NLP comune usata nei motori di ricerca e nelle app di messaggistica. L'obiettivo è suggerire possibili completamenti a partire dall'input parziale dell'utente. Il tuo compito è usare la pipeline "text-generation" di Hugging Face per implementare un sistema di auto-complete di base che generi completamenti pertinenti a partire dalla query dell'utente.

Questo esercizio fa parte del corso

Natural Language Processing (NLP) in Python

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Istruzioni dell'esercizio

Crea una pipeline autocomplete con il modello "distilgpt2".
Genera cinque suggestions di query di ricerca per il prompt fornito, limitando ciascuna a un massimo di otto token.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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'])

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Natural Language Processing (NLP) in Python

IntermediárioNível de habilidade

4.8+

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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: Classificazione dei token Exercise 2: Identificare le entità nominate nei titoli di notizie Exercise 3: PoS tagging per l'analisi del testo Exercise 4: Question answering Exercise 5: Rispondere alle domande dalle descrizioni dei prodotti Exercise 6: Generare risposte naturali con QA astrattivo Exercise 7: Attività di generazione di sequenze Exercise 8: Riassumere articoli di news per insight rapidi Exercise 9: Tradurre le recensioni dei clienti in francese Exercise 10: Creare un sistema di completamento della ricerca

Esercizio in corso

Exercise 11: Congratulazioni