Fill-Mask
Transformers
Safetensors
roberta
low-resource
sigtyp
ancient-languages
historical-languages
shared-task
Instructions to use ljvmiranda921/LiBERTus-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ljvmiranda921/LiBERTus-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ljvmiranda921/LiBERTus-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ljvmiranda921/LiBERTus-base") model = AutoModelForMaskedLM.from_pretrained("ljvmiranda921/LiBERTus-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ljvmiranda921/LiBERTus-base: direct link, hf CLI and curl.
- Browser
- Download file 4.6 kB
-
https://huggingface.co/ljvmiranda921/LiBERTus-base/resolve/main/training_args.bin
- Command line
-
hf download hf://ljvmiranda921/LiBERTus-base/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ljvmiranda921/LiBERTus-base/resolve/main/training_args.bin
4.6 kB
- Xet hash:
- a69dcb5b38198fe1724812329c7fae3727339ab8c2eacb045080a12a23745cf7
- Size of remote file:
- 4.6 kB
- SHA256:
- 8155fa1827382d7f568486ee1388c43e5b73212a214d5817a18b3b9c3c732dc6
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