Fill-Mask
Transformers
PyTorch
Safetensors
English
roberta
scientific
scholarly
encoder
masked-lm
scientific-language-processing
Eval Results (legacy)
Instructions to use scilons/SciLaD-M-custom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scilons/SciLaD-M-custom with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="scilons/SciLaD-M-custom")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("scilons/SciLaD-M-custom") model = AutoModelForMaskedLM.from_pretrained("scilons/SciLaD-M-custom", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a0b714697edd83a8bf5efbe6601577e42fc48e71ec0c0ee80864011755007b35
- Size of remote file:
- 499 MB
- SHA256:
- 0c6bfb70e9fcddf6dfd30e6293c1ae93fba1c3bc2bb5015f2625cdc5e6a2c6b9
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