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:
- 5bf6d0a788a34a10be4e5de638d32d16c7a769c51eb3b9b6a8fa27686157eac1
- Size of remote file:
- 1.67 MB
- SHA256:
- 49460a1825e0c6d26e95fafa0f781fab73ca4cad61630135d3a17dd99ae0f049
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