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") embeddings = model.encode(["It's dangerous to go alone!", "It's a secret to everybody."]) print(embeddings.shape) - Notebooks
- Google Colab
- Kaggle
Commit History
17 days ago
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Retrained head (CC0/CC-BY corpus, poster2json first-page text) and package sync (v0.3.0)
Verified signatureHead retrained on the 3,235-document corpus in which every document carries a CC0, CC-BY, or equivalent permissive license, with first-page text read by the poster2json layout engine: held-out 94.4% (95% CI 92.0 to 96.2), nested out-of-fold 94.4%, deployment 80.4% posters over 30,139 PDFs. src/ now mirrors the poster-sentry package 0.3.0 (stacked head support, pdfplumber/pypdfium2 backend, vendored poster2json text path, CLI); install instructions point to the poster-sentry repository.1a48207
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Upload scripts/train_poster_sentry.py with huggingface_hub
Verified signaturee462642