Feature Extraction
Transformers
Safetensors
PyTorch
English
spatial-transcriptomics
graph-transformer
gene-expression
finetuned
mouse-stroke
Instructions to use Bgoood/SpatialGT-MouseStroke-Sham with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Bgoood/SpatialGT-MouseStroke-Sham with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Bgoood/SpatialGT-MouseStroke-Sham")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Bgoood/SpatialGT-MouseStroke-Sham", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12c987a7b26ff31bc7769e19357ddc2d4a36b4d11f9b0aa5ff402ceb758412b3
- Size of remote file:
- 295 MB
- SHA256:
- a90700d2da663e116d8e7ce9e68effeb7659bce827785824aa59a9743580ce20
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.