Aurora SDG Multi-Label Multi-Class Model

<!-- Provide a quick summary of what the model is/does. --> This model is able to classify texts related to United Nations sustainable development goals (SDG) in multiple languages.

Source: https://sdgs.un.org/goals

Model Details

Model Description

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This text classification model was developed by fine-tuning the bert-base-uncased pre-trained model. The training data for this fine-tuned model was sourced from the publicly available OSDG Community Dataset (OSDG-CD) at https://zenodo.org/record/5550238#.ZBulfcJByF4. This model was made as part of academic research at Deakin University. The goal was to make a transformer-based SDG text classification model that anyone could use. Only the first 16 UN SDGs supported. The primary model details are highlighted below:

• Model type: Text classification
• Language(s) (NLP): English, Dutch, German, Icelandic, French, Czeck, Italian, Danisch, Spanish, Catalan
• License: cc-by-4.0
• Finetuned from model [optional]: bert-base-multilingual-uncased

Model Sources

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• Repository: option 1: https://huggingface.co/MauriceV2021/AuroraSDGsModel ; option 2 https://doi.org/10.5281/zenodo.7304546
• Demo [optional]: option 1: https://huggingface.co/spaces/MauriceV2021/SDGclassifier ; option 2: https://aurora-universities.eu/sdg-research/classify/

Direct Use

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This is a fine-tuned model and therefore requires no further training.

How to Get Started with the Model

Use the code here to get started with the model: https://github.com/Aurora-Network-Global/sdgs_many_berts

Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> The training data includes text from 1.4 titles and abstracts of academic research papers, labeled with SDG Goals and Targets, according to an initial validated query.

See training data here: https://doi.org/10.5281/zenodo.5205672

Evaluation of the Training data

• Avg_precision = 0.70
• Avg_recall = 0.15

Data evaluated by 244 domain expert senior researchers.

See evaluation report on the training data here: https://doi.org/10.5281/zenodo.4917107

Training Hyperparameters

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• Num_epoch = 3
• Learning rate = 5e-5
• Batch size = 16 -->

Evaluation

Metrics

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• Accuracy = 0.9
• Matthews correlation = 0.89

See evaluation report on the model here: https://doi.org/10.5281/zenodo.5603019

Citation

Sadick, A.M. (2023). SDG classification with BERT. https://huggingface.co/sadickam/sdg-classification-bert

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