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:
- 3023b824c709bf046980ba6be0ece42ad17cf6068855f336dbe072d45a2fcfc0
- Size of remote file:
- 14.2 kB
- SHA256:
- 13238f07ad99d1aed9636011b1a343e772f4645bdf34c327efcc21c03da3538c
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