NCA-GENL Question 166
Select 2You are developing a text classification pipeline using a pre-trained large language model. To enhance performance, you need to preprocess the text data by tokenizing it, converting the tokens into numerical vectors, and managing large datasets efficiently. Which combination of Python natural language packages would be most appropriate for this task?
- A
spaCy for tokenization and NumPy for numerical vector operations
- B
NumPy for tokenization and spaCy for managing vector databases
- C
spaCy for tokenization and a vector database like FAISS for managing large datasets
- D
Vector databases like FAISS for tokenization and NumPy for dataset preprocessing
- E
spaCy for tokenization and no additional tools for managing large datasets
Show answer and explanation
Correct answers: A, C
Explanation
Tokenization is a fundamental step in NLP pipelines, and spaCy is highly optimized for this task. After tokenization, numerical vector operations can be efficiently performed using NumPy. When working with large datasets, vector databases like FAISS are crucial for storing and querying numerical representations of the data effectively. Combining these tools ensures a robust and efficient pipeline.
- A. Correct.
Correct: spaCy is a widely-used Python library for tokenization, and NumPy is efficient for performing numerical operations on tokenized data.
- B. Incorrect.
Incorrect: NumPy is not designed for tokenization, and spaCy is not typically used for managing vector databases.
- C. Correct.
Correct: spaCy is optimal for tokenization, and FAISS, a vector database, is well-suited for managing large datasets with numerical vectors.
- D. Incorrect.
Incorrect: Vector databases like FAISS are not used for tokenization, and NumPy alone cannot manage large-scale datasets effectively.
- E. Incorrect.
Incorrect: While spaCy is effective for tokenization, managing large datasets efficiently typically requires additional tools like vector databases.