Instructions to use NbAiLab/roberta_jan_512_ncc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/roberta_jan_512_ncc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/roberta_jan_512_ncc")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/roberta_jan_512_ncc") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/roberta_jan_512_ncc", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/roberta_jan_512_ncc: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/NbAiLab/roberta_jan_512_ncc/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/roberta_jan_512_ncc/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/roberta_jan_512_ncc/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- efbe5d0b4d2025333c13cce6b21ef374c152cdf59ac048c72ea87527013eb2e0
- Size of remote file:
- 499 MB
- SHA256:
- 846e9b85c3d8d38ab51bfeb01b3faef21a03e647a06786eca01d628d8b6253ef
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