Feature Extraction
Transformers
Safetensors
medical
radiology
vision-language
chest-ct
3d-medical-imaging
glint
custom_code
Instructions to use Deepnoid/GLINT-CT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deepnoid/GLINT-CT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Deepnoid/GLINT-CT", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Deepnoid/GLINT-CT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 339 Bytes
c5a246d | 1 2 3 4 5 6 7 8 9 10 11 | from transformers import MPNetConfig, MPNetModel
def build_text_encoder(config):
if config.model_type == "mpnet":
fields = config.to_dict()
fields.pop("model_type", None)
return MPNetModel(MPNetConfig(**fields))
else:
raise NotImplementedError(f"Unsupported text model_type: {config.model_type}")
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