Feature Extraction
Transformers
Safetensors
English
spectre
medical-imaging
ct-scan
3d
vision-transformer
self-supervised-learning
foundation-model
radiology
custom_code
Instructions to use cclaess/SPECTRE-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cclaess/SPECTRE-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="cclaess/SPECTRE-Large", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("cclaess/SPECTRE-Large", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9751b992579aad1c2907d7ac536b0a4c3f29085630c00759c5109213740955b5
- Size of remote file:
- 1.59 GB
- SHA256:
- c90810e90d833cfff54880997fad4d964963fef4dd6824789f44a5bed063cb61
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.