NCA-GENL Question 165
Select 3You are tasked with building a pipeline to preprocess and analyze large amounts of text data for a generative AI model. Which combination of Python tools would you use to extract named entities, perform mathematical transformations on vectorized text data, and store high-dimensional embeddings for similarity search?
- A
spaCy
- B
NumPy
- C
Matplotlib
- D
Vector databases like FAISS or Pinecone
- E
Seaborn
Show answer and explanation
Correct answers: A, B, D
Explanation
To preprocess text data for a generative AI model, you need tools that specialize in NLP, mathematical computations, and embedding storage. spaCy is used for NLP tasks like named entity recognition. NumPy is essential for handling vectorized data and performing mathematical transformations. Finally, vector databases like FAISS or Pinecone are built to handle high-dimensional embeddings for similarity search. Tools like Matplotlib and Seaborn are primarily for visualization and are not relevant to the outlined tasks.
- A. Correct.
spaCy is a powerful Python library for natural language processing (NLP) and is commonly used for tasks like named entity recognition (NER), tokenization, and part-of-speech tagging, making it ideal for extracting named entities.
- B. Correct.
NumPy is a fundamental library for numerical computing in Python and is often used to perform mathematical transformations on vectorized data, which is crucial for preparing text data for machine learning models.
- C. Incorrect.
Matplotlib is a plotting library used for data visualization, but it is not directly relevant to named entity extraction, vectorized transformations, or storing embeddings.
- D. Correct.
Vector databases like FAISS or Pinecone are designed to store and retrieve high-dimensional vectors efficiently, making them essential for similarity search tasks in applications like recommendation systems or semantic search.
- E. Incorrect.
Seaborn, like Matplotlib, is a visualization library and does not provide functionality for named entity extraction, vectorized mathematical transformations, or storage of embeddings.