Since you're interested in LLM research and building models, generating large high-quality datasets automatically is extremely important. Below is a practical pipeline to generate ~1 million samples for training models like T5 using Python + augmentation techniques.

The idea is not just random templates, but multi-stage dataset generation.

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1️⃣ The Real Dataset Generation Pipeline (Used in AI Research)

Modern LLM dataset pipelines usually follow this structure:

Seed Data
   │
Template Generation
   │
Paraphrase Augmentation
   │
Noise Injection
   │
LLM Synthetic Expansion
   │
Filtering & Deduplication
   │
Final Dataset

Goal:

100 seed samples
→ 10,000 augmented
→ 1,000,000 final samples

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2️⃣ Step 1 — Generate Base Template Data

Your current method is good for initial seed data.

Example:

names = ["Ankit", "Rahul", "Priya"]
frameworks = ["PyTorch","TensorFlow","Keras","JAX"]
tasks = ["NLP","deep learning","AI research","cybersecurity"]
adjectives = ["exciting","powerful","challenging","rewarding"]

template = "{name} finds {framework} {adjective} for {task}"

This can easily generate:

10k – 100k samples

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3️⃣ Step 2 — Convert to T5 Format

For T5, the dataset should follow:

input_text → target_text

Example:

input_text	target_text
classify sentiment: Ankit finds PyTorch exciting for NLP	positive

Code modification:

input_text = f"classify sentiment: {text}"
target_text = sentiment

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4️⃣ Step 3 — Paraphrase Expansion

Instead of 1 sentence → create 5–10 variations.

Example:

Original

Ankit finds PyTorch exciting for NLP

Paraphrases