Instructions to use sadickam/sdgBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sadickam/sdgBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sadickam/sdgBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sadickam/sdgBERT") model = AutoModelForSequenceClassification.from_pretrained("sadickam/sdgBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Citation
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If you use this model, please cite the journal article below.
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*Sadick, A.-M., Hasan, A. and Ahiaga-Dagbui, D.D. (2026), "Modeling sustainability discourse in the construction industry: A deep-learning approach". Journal of Construction Engineering and Management, 152(4). DOI: 10.1061/JCEMD4.COENG-16205*
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## Citation
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If you use this model, please cite the journal article article below.
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*Sadick, A.-M., Hasan, A. and Ahiaga-Dagbui, D.D. (2026), "Modeling sustainability discourse in the construction industry: A deep-learning approach". Journal of Construction Engineering and Management, 152(4). DOI: 10.1061/JCEMD4.COENG-16205*
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