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
Download tokenizer.json from P0L3/sciclimatebert: direct link, hf CLI and curl.
- Browser
- Download file 2.15 MB
-
https://huggingface.co/P0L3/sciclimatebert/resolve/955db9ffcdb190eb4bf7ae2acb8536670161a05a/tokenizer.json
- Command line
-
hf download hf://P0L3/sciclimatebert@955db9ffcdb190eb4bf7ae2acb8536670161a05a/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/P0L3/sciclimatebert/resolve/955db9ffcdb190eb4bf7ae2acb8536670161a05a/tokenizer.json
2.15 MB
File too large to display, you can check the raw version instead.