Fill-Mask
Transformers
PyTorch
Safetensors
English
roberta
climate-change
domain-adaptation
masked-language-modeling
scientific-nlp
transformer
BERT
ClimateBERT
Eval Results (legacy)
Instructions to use P0L3/sciclimatebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use P0L3/sciclimatebert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="P0L3/sciclimatebert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("P0L3/sciclimatebert") model = AutoModelForMaskedLM.from_pretrained("P0L3/sciclimatebert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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If you use this model, please cite:
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```bibtex
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year={2025},
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If you use this model, please cite:
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```bibtex
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@Article{Poleksić2025,
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author={Poleksi{\'{c}}, Andrija
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and Martin{\v{c}}i{\'{c}}-Ip{\v{s}}i{\'{c}}, Sanda},
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title={Pretraining and evaluation of BERT models for climate research},
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journal={Discover Applied Sciences},
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year={2025},
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month={Oct},
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day={24},
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volume={7},
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number={11},
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pages={1278},
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issn={3004-9261},
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doi={10.1007/s42452-025-07740-5},
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url={https://doi.org/10.1007/s42452-025-07740-5}
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}
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