Memeriksa ketepatan tata bahasa

Sebuah aplikasi edukasi sedang dikembangkan untuk membantu pengguna meningkatkan tata bahasa mereka. Salah satu fitur inti secara otomatis memeriksa apakah kalimat yang dikirimkan pengguna dapat diterima secara tata bahasa. Anda diminta mengimplementasikan fitur ini menggunakan model yang dilatih pada Corpus of Linguistic Acceptability (CoLA) untuk mengklasifikasikan ketepatan kalimat.

Latihan ini merupakan bagian dari kursus

Natural Language Processing (NLP) in Python

Instruksi latihan

Inisialisasi pipeline classifier dengan model "textattack/bert-base-uncased-CoLA".
Gunakan pipeline untuk memeriksa apakah user_text dapat diterima secara tata bahasa.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

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)

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Natural Language Processing (NLP) in Python

SkillTag.level.intermediateSkillTag.label

4.9+

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: Pipeline Hugging Face untuk analisis sentimen Exercise 2: Menganalisis sentimen sebuah ulasan Exercise 3: Klasifikasi batch untuk beberapa ulasan Exercise 4: Membandingkan model pada data ulasan berlabel Exercise 5: Klasifikasi zero-shot dan QNLI Exercise 6: Klasifikasi zero-shot untuk tiket dukungan Exercise 7: Apakah teks tersebut menjawab pertanyaannya?Exercise 8: Kemiripan pertanyaan dan ketepatan tata bahasa Exercise 9: Mendeteksi pertanyaan duplikat Exercise 10: Memeriksa ketepatan tata bahasa

Latihan Saat Ini

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