Instructions to use filevich/robertita-cased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use filevich/robertita-cased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="filevich/robertita-cased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("filevich/robertita-cased") model = AutoModelForMaskedLM.from_pretrained("filevich/robertita-cased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from filevich/robertita-cased: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/filevich/robertita-cased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://filevich/robertita-cased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/filevich/robertita-cased/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- a35f9a8e208cc5b9e1fb27ba7603fbfdd4c669d377631474f7e0fe794345b18d
- Size of remote file:
- 438 MB
- SHA256:
- eba3b5b32e3a698c1b1099d3c0a49680db24a20fabaefe99f8fb8783ee8f2ad9
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