Your function is already good 👍—it generates a synthetic NLP dataset for training. I’ll improve it slightly for better tutorial-quality data and then answer your main question about CSV vs JSON for training (especially with .safetensors).

---

✅ Improved Version (Better Training Data)

This version:

• Adds more variety
• Adds balanced labels
• Avoids repetition better
• Makes it more useful for real model training

import random
import csv

def generate_and_save_custom_csv(target_size=1000, file_path='custom_dataset.csv'):
    print("📥 Generating and saving custom CSV dataset...")

    names = ["Ankit", "Rahul", "Priya", "Neha"]
    frameworks = ["PyTorch", "TensorFlow", "Keras", "JAX", "Scikit-learn", "Hugging Face"]
    tasks = ["NLP", "deep learning", "machine learning", "AI development", "cybersecurity"]
    
    # Better sentiment mapping
    adjective_map = {
        "positive": ["exciting", "powerful", "innovative", "rewarding"],
        "neutral": ["okay", "average", "manageable"],
        "negative": ["challenging", "difficult", "frustrating"]
    }

    templates = [
        "{name} finds {framework} {adjective} for {task}.",
        "{name}’s work in {task} is {adjective} with {framework}.",
        "{name} thinks {task} using {framework} is {adjective}.",
        "{task} with {framework} feels {adjective} to {name}."
    ]

    texts = set()
    data = []

    while len(data) < target_size:
        sentiment = random.choice(["positive", "neutral", "negative"])
        adjective = random.choice(adjective_map[sentiment])

        text = random.choice(templates).format(
            name=random.choice(names),
            framework=random.choice(frameworks),
            task=random.choice(tasks),
            adjective=adjective
        )

        if text not in texts:
            texts.add(text)
            data.append((text, sentiment))

    # Save CSV
    with open(file_path, 'w', newline='', encoding='utf-8') as f:
        writer = csv.writer(f)
        writer.writerow(['text', 'label'])
        writer.writerows(data)

    print(f"✅ Dataset saved to {file_path} with {len(data)} samples.")

---

📊 CSV vs JSON for Training (Important 🔥)

🧠 First understand:

.safetensors is NOT a dataset format

It is used to store:

• model weights (like .bin)
• tensors safely

👉 Dataset format is separate from model format.

---

🆚 CSV vs JSON

✅ 1. CSV Format

text,label
"AI is powerful",positive
"ML is hard",negative

✔️ Pros:

• Simple and lightweight

• Fast to load

• Works directly with:

  • pandas
  • PyTorch DataLoader
  • Hugging Face datasets

❌ Cons:

• Not good for complex data
• Only flat structure (no nesting)

---

✅ 2. JSON Format

[
  {"text": "AI is powerful", "label": "positive"},
  {"text": "ML is hard", "label": "negative"}
]

✔️ Pros:

• Supports complex structure

• Better for:

  • conversations (chat models)
  • multi-field data (instruction, input, output)

• Preferred in modern LLM training

❌ Cons:

• Slightly heavier
• Slower than CSV

---

🔥 BEST FORMAT (Important for YOU)

Since you are:

• building LLM project
• doing fine-tuning
• working with transformers / safetensors

👉 Best choice = JSON (or JSONL)

---

💡 Recommended Formats by Use Case

Use Case	Best Format
Simple classification	CSV ✅
Transformer fine-tuning	JSON ✅
ChatGPT-style model	JSONL 🔥
Instruction tuning	JSONL 🔥

---

🚀 JSONL (BEST for LLM training)

Each line = one JSON object

{"text": "AI is powerful", "label": "positive"}
{"text": "ML is hard", "label": "negative"}

Why JSONL is best:

• Streamable (used by Hugging Face, OpenAI, etc.)
• Memory efficient
• Perfect for large datasets

---

🧠 Final Answer (Simple)

👉 For your case:

• If learning → use CSV (easy)
• If building real LLM → use JSONL (best)
• .safetensors → only for model weights, not dataset

---

⚡ Pro Tip (Advanced – for your project)

For your cybersecurity + LLM project, use this format:

{"instruction": "Analyze sentiment", "input": "AI is powerful", "output": "positive"}

👉 This helps you build:

• instruction-tuned models
• chat-based AI

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If you want next step, I can help you:

✅ Convert CSV → JSONL ✅ Create dataset for code generation (your goal) ✅ Build full pipeline → dataset → tokenizer → fine-tune → safetensor model

Just tell me 👍