Update README.md

README.md

@@ -1,3 +1,85 @@
 ---
 license: mit
----
+language:
+- en
+metrics:
+- accuracy
+- matthews_correlation
+---
+
+# sadickam/sdg-classification-bert
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This model is for classifying text with respect to the United Nations sustainable development goals (SDG).
+
+
+## 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
+- **License:** mit
+- **Finetuned from model [optional]:** bert-base-uncased
+
+### Model Sources [optional]
+<!-- Provide the basic links for the model. -->
+- **Repository:** https://github.com/sadickam/sdg-classification-bert  
+- **Demo [optional]:** option 1: https://sadickam-sdg-text-classifier.hf.space/;  option 2: https://sadickam-sdg-classification-bert-main-qxg1gv.streamlit.app/ 
+
+
+### 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 fine-tuning.
+
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+tokenizer = AutoTokenizer.from_pretrained("sadickam/sdg-classification-bert")
+
+model = AutoModelForSequenceClassification.from_pretrained("sadickam/sdg-classification-bert")
+```
+
+
+## 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 a wide range of industries and academic research fields. Hence, this fine-tuned model is not for a specific industry. 
+
+See training here: https://zenodo.org/record/5550238#.ZBulfcJByF4
+
+
+## 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
+
+
+<!--## Citation [optional] -->
+
+<!-- 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 -->