bert-base-uncased-sdg-classifier

SDG Startup Classifier (18-label BERT-based Model)

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Model Overview

This model is a BERT-base-uncased transformer fine-tuned for multiclass classification of startup companies into 18 categories: the 17 United Nations Sustainable Development Goals (SDGs) plus a "no-impact" label.

It is based on the methodology and dataset described in the IJCAI 2022 paper by Kfir Bar:

Using Language Models for Classifying Startups Into the UN’s 17 Sustainable Development Goals
Kfir Bar (2022) — Paper PDF

The model takes as input textual company descriptions, mission statements, and product summaries and predicts the most relevant SDG label reflecting the company's social or environmental impact focus.

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Intended Use

• Automatic SDG classification of startup textual descriptions, mission statements, and product/service information.
• Support for impact investors, researchers, policymakers, and analysts interested in assessing startup alignment with SDGs.
• Multiclass classification into all 17 SDGs plus a no-impact class, useful for comprehensive sustainability profiling.

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Model Details

• Architecture: BERT-base-uncased (bert-base-uncased from Hugging Face Transformers)
• Number of labels: 18 (17 SDGs + 1 no-impact)
• Tokenizer: BERT-base-uncased WordPiece tokenizer
• Training data: Proprietary dataset of startup descriptions labeled by SDG, as described in Bar (2022)
• Training details: Fine-tuned using AdamW optimizer, learning rate approx. 2e-5, for multiple epochs on an annotated dataset
• Performance: Approximately 77% accuracy on the 5 aggregated SDG groups, with competitive performance on the full 18-label task (per original paper)

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How to Use

Minimal example code to load and run inference using the Hugging Face Transformers library: from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch

model_name = "amannor/bert-base-uncased-sdg-classifier" Load tokenizer and model from Hugging Face Hub

tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForSequenceClassification.from_pretrained(model_name) Input startup description text

text = "This startup develops affordable solar panels to improve clean energy access." Tokenize input text

inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) Forward pass

outputs = model(**inputs) Predicted class index (0 to 17, aligned with SDGs + no-impact)

predicted_label_id = torch.argmax(outputs.logits, dim=-1).item()

print(f"Predicted SDG label ID: {predicted_label_id}")

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Limitations

• The model relies solely on textual company descriptions, which might be promotional or biased (“greenwashing”).
• Performance may degrade on short, noisy, or non-English inputs.
• The training dataset was geographically and linguistically limited; generalization outside these domains may be suboptimal.
• Intended to assist, not replace, expert judgment.

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Citation

If you use this model, please cite:

@inproceedings{bar2022ijcai, title={Using Language Models for Classifying Startups Into the UN’s 17 Sustainable Development Goals}, author={Bar, Kfir}, booktitle={Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI)}, year={2022} }

You may also wish to reference the accompanying repository:
https://github.com/Amannor/sdg-codebase

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License

This model is released under the MIT License. For more information, see the LICENSE file in this repository.

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Links and Resources

• Full repository with code, notebooks, and datasets
• IJCAI 2022 original paper PDF

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For questions or issues, please open an issue in the GitHub repository or contact the maintainer via Hugging Face.