Instructions to use NbAiLab/roberta_des_512_4e4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/roberta_des_512_4e4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/roberta_des_512_4e4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/roberta_des_512_4e4") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/roberta_des_512_4e4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from NbAiLab/roberta_des_512_4e4: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/NbAiLab/roberta_des_512_4e4/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://NbAiLab/roberta_des_512_4e4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/NbAiLab/roberta_des_512_4e4/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 3b52568aaacf5768b0e42de483a59f571779de563a95dac9b68e9cadf753c8b7
- Size of remote file:
- 499 MB
- SHA256:
- e257896739d1e2885b3a356e4ecd5ce840cc48fe17c1ce7b0f96628db8050958
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