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 pretraining.txt from ljvmiranda921/LiBERTus-base: direct link, hf CLI and curl.
- Browser
- Download file 19.2 MB
-
https://huggingface.co/ljvmiranda921/LiBERTus-base/resolve/main/pretraining.txt
- Command line
-
hf download hf://ljvmiranda921/LiBERTus-base/pretraining.txt
-
curl -L -o pretraining.txt https://huggingface.co/ljvmiranda921/LiBERTus-base/resolve/main/pretraining.txt
19.2 MB
- Xet hash:
- 74fe6cda853255d6bc6f202a3035aa8a0f57deb3363510e5c176113aca1d7632
- Size of remote file:
- 19.2 MB
- SHA256:
- d15fc636e4e2ea80b7d31d45866e27fcddafba09fd39eba27f275680594844ce
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