Instructions to use lanesket/RASTaBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lanesket/RASTaBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="lanesket/RASTaBERTa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("lanesket/RASTaBERTa") model = AutoModelForMaskedLM.from_pretrained("lanesket/RASTaBERTa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lanesket/RASTaBERTa: direct link, hf CLI and curl.
- Browser
- Download file 500 MB
-
https://huggingface.co/lanesket/RASTaBERTa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lanesket/RASTaBERTa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lanesket/RASTaBERTa/resolve/main/pytorch_model.bin
500 MB
- Xet hash:
- d7b65499e434ad37dd2d9498533846723be265747f25a6348189c65da7e1144a
- Size of remote file:
- 500 MB
- SHA256:
- dfbd8a6e00235b8b534d718aec11bbd74dec5cf7d512dc3648a3e513d4915bac
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