Instructions to use icelab/spacescibert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use icelab/spacescibert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="icelab/spacescibert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("icelab/spacescibert") model = AutoModelForMaskedLM.from_pretrained("icelab/spacescibert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 97309d9f846c003bbeba02774ebc849b53a386feff169b219c09fa69fbec6cd8
- Size of remote file:
- 880 MB
- SHA256:
- 9e5f6b9f88f8f7164f33b13786017db566115e4d678957fe984af33eb959cdf1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.