|
|
|
|
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
To prepare deep features for a file named mgt2101.pdf (which appears to be a course document, likely for a management or business course), you'll need to extract meaningful, dense vector representations from its content.
Since I cannot directly access or upload your local PDF file, here are the you can take, depending on your technical environment and goal. Option 1: Using a Local Python Script (Best for custom deep learning / RAG) This uses a pre-trained transformer model to convert each chunk of text from the PDF into a deep feature vector. Prerequisites Install required libraries:
pip install pypdf2 sentence-transformers torch numpy import PyPDF2 from sentence_transformers import SentenceTransformer import numpy as np 1. Load a deep feature extractor (embeddings model) model = SentenceTransformer('all-MiniLM-L6-v2') # Outputs 384-dim vectors 2. Extract text from PDF def extract_text_from_pdf(pdf_path): with open(pdf_path, 'rb') as file: reader = PyPDF2.PdfReader(file) text = "" for page in reader.pages: text += page.extract_text() return text
pdf_text = extract_text_from_pdf("mgt2101.pdf") chunks = pdf_text.split('\n\n') # simple paragraph split chunks = [chunk.strip() for chunk in chunks if len(chunk) > 100] 4. Generate deep features (embeddings) deep_features = model.encode(chunks) 5. Save features for later use np.save("mgt2101_features.npy", deep_features)
