To generate test questions from a set of standard texts using Python and AI, you can use the following approach:
For this example, we'll use the Hugging Face's Transformers library with the OpenAI's GPT model.
First, install the necessary libraries:
Українська 
English
Poland
pip install transformers spacy
Next, create a Python script with the following code:
import spacy
from transformers import pipeline
def preprocess_text(text):
# Load the Spacy English model
nlp = spacy.load("en_core_web_sm")
# Tokenize and parse the input text
doc = nlp(text)
# Extract key concepts and entities
concepts = [chunk.text for chunk in doc.noun_chunks]
return concepts
def generate_questions(concepts):
# Create a question generation pipeline using GPT
question_generator = pipeline("text2text-generation", model="text2text/question-generation")
# Generate questions based on the concepts
questions = question_generator(concepts)
return [question["generated_text"] for question in questions]
# Replace this with your input text containing the standards
input_text = """
Your input text containing the standards.
"""
concepts = preprocess_text(input_text)
questions = generate_questions(concepts)
# Print the generated questions
for i, question in enumerate(questions, 1):
print(f"Question {i}: {question}")
This script preprocesses the input text, extracts key concepts using Spacy, and generates questions using the GPT model from the Transformers library.
Please note that this is a basic example, and the quality of the generated questions might not be perfect. You may need to fine-tune the GPT model with domain-specific data, adjust the preprocessing steps, or modify the question generation process to achieve better results.
To generate test questions from a set of standard texts in Ukrainian or Russian using Python and AI, you can follow a similar approach as before, but with modifications for the specific languages.
First, install the necessary libraries:
pip install transformers spacy
python -m spacy download uk_core_news_sm # For Ukrainian
python -m spacy download ru_core_news_sm # For Russian
Next, create a Python script with the following code:
import spacy
from transformers import pipeline
def preprocess_text(text, language):
# Load the Spacy model for the specified language
if language == `uk`:
nlp = spacy.load("uk_core_news_sm")
elif language == `ru`:
nlp = spacy.load("ru_core_news_sm")
else:
raise ValueError("Unsupported language")
# Tokenize and parse the input text
doc = nlp(text)
# Extract key concepts and entities
concepts = [chunk.text for chunk in doc.noun_chunks]
return concepts
def generate_questions(concepts, language):
# Create a question generation pipeline using GPT
# Note: You need to use a GPT model that supports Ukrainian or Russian
model_name = "text2text/question-generation" # Replace with a suitable model
question_generator = pipeline("text2text-generation", model=model_name)
# Generate questions based on the concepts
questions = question_generator(concepts)
return [question["generated_text"] for question in questions]
# Replace this with your input text containing the standards
input_text = """
Ваш вхідний текст із стандартами.
"""
language = "uk" # Set to `uk` for Ukrainian or `ru` for Russian
concepts = preprocess_text(input_text, language)
questions = generate_questions(concepts, language)
# Print the generated questions
for i, question in enumerate(questions, 1):
print(f"Питання {i}: {question}")
This script preprocesses the input text, extracts key concepts using Spacy, and generates questions using the GPT model from the Transformers library. To switch between Ukrainian and Russian languages, change the language variable to 'uk' or 'ru', respectively.
Please note that the quality of the generated questions might not be perfect. You may need to fine-tune the GPT model with domain-specific data, adjust the preprocessing steps, or modify the question generation process to achieve better results. Also, you need to replace the model_name variable with a suitable GPT model that supports Ukrainian or Russian languages.