Instructions to use ltg/norbert2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltg/norbert2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ltg/norbert2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ltg/norbert2") model = AutoModelForMaskedLM.from_pretrained("ltg/norbert2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ltg/norbert2: direct link, hf CLI and curl.
- Browser
- Download file 501 MB
-
https://huggingface.co/ltg/norbert2/resolve/f22bb47f536f62edfcd86ca9320ade990eafbe22/pytorch_model.bin
- Command line
-
hf download hf://ltg/norbert2@f22bb47f536f62edfcd86ca9320ade990eafbe22/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ltg/norbert2/resolve/f22bb47f536f62edfcd86ca9320ade990eafbe22/pytorch_model.bin
501 MB
- Xet hash:
- 3d1d624bb9fafc2a4a3b744fac3427882f4fce57ff8687e9b6d549d9214c0736
- Size of remote file:
- 501 MB
- SHA256:
- 3c15dc6df9ed0cd6db1726ea7a2d5e8a7e73fa950065cb60436f09c1e7a6609c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.