Feature Extraction
Transformers
Safetensors
English
closp
remote-sensing
text-to-image-retrieval
multimodal
geospatial
SAR
multispectral
crisis-management
earth-observation
contrastive-learning
custom_code
Instructions to use DarthReca/CLOSP-VS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DarthReca/CLOSP-VS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="DarthReca/CLOSP-VS", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DarthReca/CLOSP-VS", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 034279cc0f86abb05af6d4720a2d3b85a430f271e5aa1ab02893601daec5d19b
- Size of remote file:
- 11 MB
- SHA256:
- 1fc4e9b49abb4e81411376fc6d09b1281aa8ed96cef64b7aa95cc4aeeccb97a4
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