Fill-Mask
Transformers
PyTorch
Safetensors
English
roberta
scientific
scholarly
encoder
masked-lm
scientific-language-processing
Eval Results (legacy)
Instructions to use scilons/SciLaD-M-roberta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scilons/SciLaD-M-roberta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="scilons/SciLaD-M-roberta")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("scilons/SciLaD-M-roberta") model = AutoModelForMaskedLM.from_pretrained("scilons/SciLaD-M-roberta", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dbf66815337913ef4a3701660a1833d049334670808f82d771c1493f83332458
- Size of remote file:
- 499 MB
- SHA256:
- e47b0a0ebe31b2e4c54c690d71b51cf3c84935ed83425e9a2f6a321e15a07cc3
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