Dilbilgisel doğruluğu kontrol etme

Kullanıcıların dil bilgisini geliştirmesine yardımcı olacak bir eğitim uygulaması geliştiriliyor. Temel özelliklerden biri, kullanıcıların gönderdiği cümlelerin dilbilgisel olarak kabul edilebilir olup olmadığını otomatik olarak kontrol ediyor. Cümle doğruluğunu sınıflandırmak için Corpus of Linguistic Acceptability (CoLA) üzerinde eğitilmiş bir model kullanarak bu özelliği uygulaman isteniyor.

Bu egzersiz

Python ile Natural Language Processing (NLP)

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

"textattack/bert-base-uncased-CoLA" modeliyle bir classifier pipeline'ı başlat.
user_text ifadesinin dilbilgisel olarak kabul edilebilir olup olmadığını kontrol etmek için bu pipeline'ı kullan.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

from transformers import pipeline

# Initialize the pipeline
classifier = ____

user_text = "Although she was knowing the answer, she didn't raised her hand during the class discussion."

# Classify grammatical acceptability
result = ____

print(result)

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: Duygu analizi için Hugging Face pipeline’ları Exercise 2: Bir değerlendirmenin duygu durumunu analiz etme Exercise 3: Birden çok yorumu toplu olarak sınıflandırma Exercise 4: Etiketli yorum verilerinde modelleri karşılaştırma Exercise 5: Sıfır-atış sınıflandırma ve QNLI Exercise 6: Destek taleplerinin zero-shot sınıflandırılması Exercise 7: Metin soruyu yanıtlıyor mu?Exercise 8: Soru benzerliği ve dilbilgisel doğruluk Exercise 9: Yinelenen soruları tespit et Exercise 10: Dilbilgisel doğruluğu kontrol etme

Geçerli Egzersiz

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 classification Exercise 2: Identifying named entities in news headlines Exercise 3: Part of Speech tagging for text analysis Exercise 4: Question answering Exercise 5: Answering questions from product descriptions Exercise 6: Generating natural answers with abstractive QA Exercise 7: Sequence generation tasks Exercise 8: Summarizing news articles for quick insights Exercise 9: Translating customer reviews to French Exercise 10: Building a search completion system Exercise 11: Congratulations