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--- |
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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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<!-- 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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Source: https://sdgs.un.org/goals |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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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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### Model Sources |
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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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### Direct Use |
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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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## How to Get Started with the Model |
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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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## Training Data |
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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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See training data here: https://doi.org/10.5281/zenodo.5205672 |
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### Evaluation of the Training data |
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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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## Training Hyperparameters |
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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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## Evaluation |
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#### Metrics |
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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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## 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 --> |
