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LetzSDG-1.0

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LETZSDG TERMS OF USE

By requesting access to LëtzSDG, you acknowledge that you have read, understood, and agreed to the following conditions.
Model Origin: This is a BERT-based model trained on raw data from FineWeb and synthetic data generated by google/gemma-3-27b-it, Qwen/Qwen2.5-32B-Instruct, and mistralai/Mistral-Small-24B-Instruct-2503.

  1. Gemma 3 License Compliance (Synthetic Data)
    Because this model was trained on data generated by Gemma 3, it is classified
    as a "Model Derivative" under the Gemma Terms of Use.
  • You agree not to use this model for any restricted uses set forth in the
    Gemma Prohibited Use Policy (ai.google.dev/gemma/prohibited_use_policy).
  • You release Google from any liability regarding the outputs of this model.
  1. Purpose of use
    LëtzSDG is released strictly for research, educational, and experimental purposes.
  • It must not be used for financial decision-making, investment recommendation,
    portfolio management, or any other regulated activity.
  • It must not be used in violation of any applicable laws or regulations,
    for harmful, abusive, fraudulent, or deceptive activities.
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  • The sponsoring institutions and model developers make no warranties regarding the accuracy or suitability of the model.
  • The sponsoring institutions and model developers shall not be liable for any direct, indirect, incidental, consequential, or special damages arising from its use.
  • The end user assumes full responsibility for the outcomes derived from this model.

  1. Data Transparency
    No personal data or sensitive information is intended to be included in this model.

  2. Non-endorsement
    The inclusion of the names of the partner institutions does not imply endorsement or certification of specific outcomes derived from this model.

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LetzSDG

💚 LëtzSDG: A BERT Model for SDGs Classification

🔍 A model for classifying text based on the United Nations Sustainable Development Goals (SDGs).

📄 Presented at the 2nd IEEE International Workshop on Large Language Models for Finance, co-located with the 2025 IEEE International Conference on Big Data (IEEE BigData 2025).

🌍 Overview

LëtzSDG is a 110M-parameter BERT-based multiclass classifier fine-tuned to identify text excerpts related to the 17 UN Sustainable Development Goals (SDGs).

Developed for sustainable finance, this model supports on-premises, auditable, human-in-the-loop workflows in compliance with the EU AI Act.

Unlike cloud-hosted solutions, LëtzSDG can run locally, ensuring data privacy, traceability, and transparency.

⚙️ Model Details

  • Base model: bert-base-uncased
  • Task type: Multiclass text classification (17 SDGs)
  • Training epochs: 3
  • Batch size: 16
  • Optimizer: AdamW (lr = 2e-5, weight_decay = 0.01)
  • Precision: bfloat16
  • Max sequence length: 512 tokens

🚀 Quick Start

🔹 Simple inference with the Hugging Face pipeline 

# pip install transformers

from transformers import pipeline

pipe = pipeline(
    "text-classification",
    model="lrsbrgrn/LetzSDG-1.0",
    truncation=True
)

text = "The company introduced equal pay policies and increased women’s representation in leadership roles."
result = pipe(text)

print(result)
# [{'label': 'SDG_5_GENDER_EQUALITY', 'score': 0.9993481040000916}]

📚 Citation

Coming soon!

🏗️ Data Sources & Attribution

The model was trained using a combination of real-world data and synthetic data generated by Large Language Models.

Note: Since this model was trained on synthetic data generated by Gemma 3, it is classified as a "Model Derivative" under their terms and is therefore subject to the Gemma Terms of Use regarding prohibited uses.

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