Instructions to use NbAiLab/roberta_NCC_des_128_decayfrom200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/roberta_NCC_des_128_decayfrom200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/roberta_NCC_des_128_decayfrom200")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/roberta_NCC_des_128_decayfrom200") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/roberta_NCC_des_128_decayfrom200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/roberta_NCC_des_128_decayfrom200: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/NbAiLab/roberta_NCC_des_128_decayfrom200/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/roberta_NCC_des_128_decayfrom200/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/roberta_NCC_des_128_decayfrom200/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- dcbda42c564657d5f263d001efe68c1adbf85fa7e3361ff964a61d23d8def3a3
- Size of remote file:
- 499 MB
- SHA256:
- fa622100c0d6a633b65d23267d58d75537b8449053ae3cd664fe545a01f6e580
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