license: mit
language:
- en
metrics:
- accuracy
- matthews_correlation
sadickam/sdg-classification-bert
This model is for classifying text with respect to the United Nations sustainable development goals (SDG).
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 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]
- 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 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.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("sadickam/sdg-classification-bert")
model = AutoModelForSequenceClassification.from_pretrained("sadickam/sdg-classification-bert")
Training Data
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
- Accuracy = 0.9
- Matthews correlation = 0.89