Instructions to use NbAiLab/roberta_des_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NbAiLab/roberta_des_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="NbAiLab/roberta_des_512")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("NbAiLab/roberta_des_512") model = AutoModelForMaskedLM.from_pretrained("NbAiLab/roberta_des_512") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ba7def8d886f99e4d22d32430b6a074b542fcc21405f618a0bb856954107a15a
- Size of remote file:
- 499 MB
- SHA256:
- e29e78b202a0089e4c99b16430973f4b1a75f38af17d4d2c489187f96b5ee460
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.