Score = 0.3 * entropy_norm + 0.2 * no_repeat + 0.2 * checksum_valid + 0.15 * not_common_pattern + 0.15 * mixed_chars Example: X9F2-7KLM-3PQR → (high quality) 6. Example Python Output (feature dict) { "raw": "X9F2-7KLM-3PQR", "length": 12, "digit_count": 2, "upper_count": 8, "hyphen_positions": [4, 9], "entropy_bits": 4.2, "has_repeating": False, "checksum_valid": True, "vowel_ratio": 0.083, "quality_score": 0.89, "token_ids": [23, 9, 15, 2, 36, 7, 20, 21, 22, 36, 3, 16, 17, 18] } If you meant something else by “deep feature” (e.g., a learned embedding from a model, or feature for fraud detection), please clarify and I can adjust the representation accordingly.
To generate a “deep feature” for a , we need to move beyond simple strings like "ABCD1234" and instead produce structured, meaningful, or AI-friendly representations that capture patterns, usability, or predictive traits. pikpak invite code