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
| { | |
| "model_type": "poster-sentry", | |
| "architecture": "logistic_regression_multimodal", | |
| "embedding_backbone": "minishlab/potion-base-32M", | |
| "embedding_dim": 512, | |
| "visual_features": 15, | |
| "structural_features": 15, | |
| "total_features": 542, | |
| "labels": [ | |
| "non_poster", | |
| "poster" | |
| ], | |
| "num_classes": 2, | |
| "head_file": "models/poster_sentry_head.npz", | |
| "scaler": "StandardScaler", | |
| "classifier": "LogisticRegression", | |
| "accuracy": 0.8725, | |
| "training_samples": 3606, | |
| "library_name": "model2vec" | |
| } |