Traduire des avis clients en français

Une entreprise d’e-commerce internationale analyse les retours de ses utilisateurs anglophones. Pour aider son équipe d’assistance francophone, elle doit traduire ces avis en français. Dans le cadre du pipeline de NLP, votre rôle est d’automatiser la traduction des avis à l’aide de la pipeline Hugging Face.

Cet exercice fait partie du cours

<cours>Natural Language Processing (NLP) in Python</cours>

Instructions de l’exercice

Créez une pipeline translator en utilisant le modèle "Helsinki-NLP/opus-mt-en-fr".
Traduisez le review fourni de l’anglais vers le français.

Exercice interactif pratique

Essayez cet exercice en complétant ce code d’exemple.

from transformers import pipeline

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

review = "The hotel was clean and the staff were very friendly."

# Translate the review
translation = ____

print(translation)

Modifier et exécuter le code

Cet exercice fait partie du cours

<cours>Natural Language Processing (NLP) in Python</cours>

IntermédiaireNiveau de compétence

4.9+

Commencer le cours gratuitement

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: Classification de tokens Exercise 2: Identifier les entités nommées dans des titres d’actualité Exercise 3: Étiquetage morpho-syntaxique (PoS) pour l’analyse de texte Exercise 4: Question-réponse Exercise 5: Répondre aux questions à partir de descriptions de produits Exercise 6: Générer des réponses naturelles avec le QA abstractive Exercise 7: Tâches de génération de séquences Exercise 8: Résumer des articles d’actualité pour aller à l’essentiel Exercise 9: Traduire des avis clients en français

Exercice actuel

Exercise 10: Créer un système de complétion de recherche Exercise 11: Félicitations