Feature Extraction
Transformers
Joblib
Safetensors
English
comodo
imu
wearable-sensing
human-activity-recognition
time-series
cross-modal-distillation
svm
custom_code
Instructions to use CRUISEResearchGroup/COMODO-AnyMo-Bench with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CRUISEResearchGroup/COMODO-AnyMo-Bench with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="CRUISEResearchGroup/COMODO-AnyMo-Bench", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("CRUISEResearchGroup/COMODO-AnyMo-Bench", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 934 Bytes
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"mean": [
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4.811643207608252,
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0.0149784624687404,
0.00825595949860718
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"std": [
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0.7833971659587226,
0.3687130034760385,
0.41378717620148847,
4.781408427124205,
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5.414364143517655,
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5.6005718169582215,
1.0565962983884163,
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],
"num_samples": 36529357
} |