# 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.

![image](https://user-images.githubusercontent.com/73560591/216751462-ced482ba-5d8e-48aa-9a48-5557979a35f1.png)
Source: https://sdgs.un.org/goals 

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

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
<!-- Provide the basic links for the model. -->
- **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

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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

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

## Evaluation

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->
- 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 

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->


<!--## Model Card Contact -->
{"language": ["en", "nl", "de", "fr", "it", "is", "cs", "da", "es", "ca"], "license": "cc-by-4.0", "library_name": "keras", "metrics": ["accuracy", "matthews_correlation"], "pipeline_tag": "text-classification"}
keras text-classification en nl de fr it is cs da es ca license:cc-by-4.0 region:us
TAGS
#keras #text-classification #en #nl #de #fr #it #is #cs #da #es #ca #license-cc-by-4.0 #region-us 

# 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: URL 

## 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 URL
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

- Repository: option 1: URL ; option 2 URL 
- Demo [optional]: option 1: URL ;  option 2: URL 


### 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: URL


## 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: URL

### 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: URL 


## Training Hyperparameters



## Evaluation

#### Metrics


- Accuracy = 0.9
- Matthews correlation = 0.89

See evaluation report on the model here: URL

Sadick, A.M. (2023). SDG classification with BERT. URL 





# 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: URL ## 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 URL
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

- Repository: option 1: URL ; option 2 URL 
- Demo [optional]: option 1: URL ;  option 2: URL ### 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: URL ## 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: URL ### 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: URL ## Training Hyperparameters ## Evaluation #### Metrics


- Accuracy = 0.9
- Matthews correlation = 0.89

See evaluation report on the model here: URL

Sadick, A.M. (2023). SDG classification with BERT. URL
TAGS
#keras #text-classification #en #nl #de #fr #it #is #cs #da #es #ca #license-cc-by-4.0 #region-us 
 # 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: URL ## 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 URL
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

- Repository: option 1: URL ; option 2 URL 
- Demo [optional]: option 1: URL ;  option 2: URL ### 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: URL ## 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: URL ### 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: URL ## Training Hyperparameters ## Evaluation #### Metrics


- Accuracy = 0.9
- Matthews correlation = 0.89

See evaluation report on the model here: URL

Sadick, A.M. (2023). SDG classification with BERT. URL
passage: TAGS
#keras #text-classification #en #nl #de #fr #it #is #cs #da #es #ca #license-cc-by-4.0 #region-us 
# 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: URL## 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 URL
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

- Repository: option 1: URL ; option 2 URL 
- Demo [optional]: option 1: URL ;  option 2: URL### 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: URL## 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: URL### 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: URL## Training Hyperparameters## Evaluation#### Metrics


- Accuracy = 0.9
- Matthews correlation = 0.89

See evaluation report on the model here: URL

Sadick, A.M. (2023). SDG classification with BERT. URL