Skip to main content Open menu Open navigation Go to Reddit Home r/datasets Get App Get the Reddit app Log In Log in to Reddit Expand user menu Open settings menu Go to datasets r/datasets • Hour-Dirt-8505 I fine-tuned LLaMA 3.2 1B Brazilian Address Parser — looking for honest feedback Recently, I posted here on Reddit asking for ideas on what I could build with a dataset of ~2 million pairs of messy/clean Brazilian addresses. A few kind folks shared some great suggestions, and one idea that really stood out was building an address parser. That pushed me into the world of LLM fine-tuning for the first time. I decided to partially fine-tune LLaMA 3.2 1B, focusing specifically on address normalization and field extraction (address, complement, neighborhood, city, state, country, coordinates, etc.). Surprisingly, the early results look quite promising. To properly evaluate it, I also built a small API to: Run inference tests Perform post-inference validation Compute a confidence score based on consistency checks (postal code, city/state match, field presence, etc.) Below is an example request body and the corresponding response. Request {
  "inputs": [
    "quadra -42.93386179 quadra arse 102 alameda 12 a, 5045 77023-582 brasil -21.26567258 palmas",
    "torre -43.02525939 bela vista 5 brasil minas gerais são joão do paraíso beco do pôr do sol, 4289 -19.14142529"
  ]
} Response [
  {
    "address": "Quadra Arse 102 Alameda 12 A, 5045",
    "complement": "quadra",
    "city": "Palmas",
    "country": "Brasil",
    "postal_code": "77023-582",
    "latitude": "-21.26567258",
    "longitude": "-42.93386179",
    "confidence": 1.0,
    "validation": {
      "postal_code_validation": {
        "is_valid": true,
        "found_in_input": true,
        "city_match": true
      },
      "field_validation": {
        "address_found": true,
        "complement_found": true,
        "neighborhood_found": false,
        "city_found": true,
        "state_found": false,
        "country_found": true
      }
    }
  },
  {
    "address": "Beco Do Pôr Do Sol, 4289",
    "complement": "torre",
    "neighborhood": "Bela Vista 5",
    "city": "São João Do Paraíso",
    "state": "Minas Gerais",
    "country": "Brasil",
    "latitude": "-19.14142529",
    "longitude": "-43.02525939",
    "confidence": 0.92,
    "validation": {
      "postal_code_validation": {
        "is_valid": false
      },
      "field_validation": {
        "address_found": true,
        "complement_found": true,
        "neighborhood_found": true,
        "city_found": true,
        "state_found": true,
        "country_found": true,
        "city_in_state": false,
        "neighborhood_in_city": false
      }
    }
  }
] I’d really appreciate honest feedback from people more experienced with: Fine-tuning small LLMs Address parsing / entity extraction Post-inference validation strategies Confidence scoring approaches Does this look like a reasonable direction for a 1B model? Anything you’d improve architecturally or evaluation-wise? Thanks in advance — this project has been a great learning experience so far 🙏 Read more Share New to Reddit? Create your account and connect with a world of communities. Continue with Email By continuing, you agree to our User Agreement and acknowledge that you understand the Privacy Policy. Public Anyone can view, post, and comment to this community 0 0 Reddit Rules Privacy Policy User Agreement Accessibility Reddit, Inc. © 2026. All rights reserved. Expand Navigation Collapse Navigation
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Пользователь обучил модель LLaMA 3.2 1B для парсинга бразильских адресов, используя данные из датасета из ~2 миллионов пар адресов. Разработана API для оценки результатов, включающая тестирование, валидацию и оценку уверенности.  Представлены примеры запросов и ответов, демонстрирующие процесс парсинга и валидации. Пользователь ищет обратную связь по направлению проекта, архитектуре и методам оценки.
