Update README.md

README.md

@@ -1,5 +1,100 @@
 ---
 license: cc-by-4.0
+language:
+- en
+- nl
+- de
+- fr
+- it
+- is
+- cs
+- da
+- es
+- ca
+metrics:
+- accuracy
+- matthews_correlation
+pipeline_tag: text-classification
 ---
-# Aurora SDG AI
-This model is able to classify texts related to SDG's in multiple languages.
+# 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: ;  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 -->