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
Download training_args.bin from scilons/SciLaD-M-custom: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/scilons/SciLaD-M-custom/resolve/main/training_args.bin
- Command line
-
hf download hf://scilons/SciLaD-M-custom/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/scilons/SciLaD-M-custom/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 86b2f0ff31a8b8460773bc688d7bf7a0a493113355d985e29d9307adc1e4e24b
- Size of remote file:
- 5.37 kB
- SHA256:
- 68b772338120b718d8b00ac581c3d7e2004094c94a0e777434b75d75a2b7797f
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