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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- transformer
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- BERT
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- ClimateBERT
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metrics:
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- f1
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model-index:
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- transformer
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- BERT
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- ClimateBERT
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base_model: climatebert/distilroberta-base-climate-f
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metrics:
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- f1
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model-index:
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