sadickam/sdg-classification-bert

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This model is for classifying text with respect to the United Nations sustainable development goals (SDG).

Source:https://www.un.org/development/desa/disabilities/about-us/sustainable-development-goals-sdgs-and-disability.html

Model Details

Model Description

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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. 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

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• Repository: https://huggingface.co/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

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This is a fine-tuned model and therefore requires no further training.

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

<!-- 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

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• Accuracy = 0.9
• Matthews correlation = 0.89

Citation

Sadick, A.M. (2023). SDG classification with BERT. https://huggingface.co/sadickam/sdg-classification-bert

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