Instructions to use versae/roberta-ncc-1shard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use versae/roberta-ncc-1shard with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="versae/roberta-ncc-1shard")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("versae/roberta-ncc-1shard") model = AutoModelForMaskedLM.from_pretrained("versae/roberta-ncc-1shard", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from versae/roberta-ncc-1shard: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/versae/roberta-ncc-1shard/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://versae/roberta-ncc-1shard/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/versae/roberta-ncc-1shard/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 7b3e8e306aaefe70f0360bbe9be59e887bb55f0a0096186a08d1531b075688b9
- Size of remote file:
- 499 MB
- SHA256:
- 8a733b898f5d53bcc8f0b895b520d6e95bc6103ae18d91521eb4e6405ca4c33e
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