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  license: cc-by-4.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Aurora SDG AI
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- This model is able to classify texts related to SDG's in multiple languages.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: cc-by-4.0
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+ language:
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+ - en
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+ - nl
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+ - de
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+ - fr
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+ - it
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+ - is
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+ - cs
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+ - da
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+ - es
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+ - ca
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+ metrics:
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+ - accuracy
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+ - matthews_correlation
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+ pipeline_tag: text-classification
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  ---
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+ # Aurora SDG Multi-Label Multi-Class Model
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+ This model is able to classify texts related to United Nations sustainable development goals (SDG) in multiple languages.
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+
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+ ![image](https://user-images.githubusercontent.com/73560591/216751462-ced482ba-5d8e-48aa-9a48-5557979a35f1.png)
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+ Source: https://sdgs.un.org/goals
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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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.
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+ 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:
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+ - **Model type:** Text classification
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+ - **Language(s) (NLP):** English, Dutch, German, Icelandic, French, Czeck, Italian, Danisch, Spanish, Catalan
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+ - **License:** cc-by-4.0
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+ - **Finetuned from model [optional]:** bert-base-multilingual-uncased
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+
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+ ### Model Sources
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+ <!-- Provide the basic links for the model. -->
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+ - **Repository:** option 1: https://huggingface.co/MauriceV2021/AuroraSDGsModel ; option 2 https://doi.org/10.5281/zenodo.7304546
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+ - **Demo [optional]:** option 1: ; option 2: https://aurora-universities.eu/sdg-research/classify/
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+
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+ This is a fine-tuned model and therefore requires no further training.
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+
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+
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+ ## How to Get Started with the Model
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+
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+ Use the code here to get started with the model: https://github.com/Aurora-Network-Global/sdgs_many_berts
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+
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+
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+ ## Training Data
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+
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+ <!-- 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. -->
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+ 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.
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+
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+ See training data here: https://doi.org/10.5281/zenodo.5205672
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+
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+ ### Evaluation of the Training data
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+
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+ - Avg_precision = 0.70
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+ - Avg_recall = 0.15
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+ Data evaluated by 244 domain expert senior researchers.
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+ See evaluation report on the training data here: https://doi.org/10.5281/zenodo.4917107
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+
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+
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+ ## Training Hyperparameters
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+
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+ <!--
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+ - Num_epoch = 3
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+ - Learning rate = 5e-5
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+ - Batch size = 16
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+ -->
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+
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+ ## Evaluation
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+ - Accuracy = 0.9
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+ - Matthews correlation = 0.89
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+ See evaluation report on the model here: https://doi.org/10.5281/zenodo.5603019
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+
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+ ## Citation
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+ Sadick, A.M. (2023). SDG classification with BERT. https://huggingface.co/sadickam/sdg-classification-bert
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+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+ <!--## Model Card Contact -->