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")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("P0L3/sciclimatebert") model = AutoModelForMaskedLM.from_pretrained("P0L3/sciclimatebert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0fe28030acbbb406c451cad01fd1745a8ae8c17d8edb7690941f26d512e63fa6
- Size of remote file:
- 329 MB
- SHA256:
- 8a9326d44605591895ce7c568209ad6d574c84798ca5b9b6d8234ec784f50eae
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