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Aurora SDG Multi-Label Multi-Class Model

This model is able to classify texts related to United Nations sustainable development goals (SDG) in multiple languages.

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

Model Details

Model Description

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

Direct Use

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

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

Evaluation

Metrics

  • 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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