Text Classification
Model2Vec
English
poster-sentry
document-classification
scientific-posters
multimodal
poster-detection
machine-actionable
FAIR-data
posters-science
quality-control
Instructions to use fairdataihub/poster-sentry with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use fairdataihub/poster-sentry with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("fairdataihub/poster-sentry") - Notebooks
- Google Colab
- Kaggle
Add config.json for HuggingFace download tracking
Browse files- config.json +20 -0
config.json
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{
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"model_type": "poster-sentry",
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"architecture": "logistic_regression_multimodal",
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"embedding_backbone": "minishlab/potion-base-32M",
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"embedding_dim": 512,
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"visual_features": 15,
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"structural_features": 15,
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"total_features": 542,
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"labels": [
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"non_poster",
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"poster"
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],
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"num_classes": 2,
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"head_file": "models/poster_sentry_head.npz",
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"scaler": "StandardScaler",
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"classifier": "LogisticRegression",
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"accuracy": 0.8725,
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"training_samples": 3606,
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"library_name": "model2vec"
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}
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