Text Classification
Transformers
Safetensors
English
bibr
OECD
scientific-paper-classification
MiniLM
Instructions to use scienceverse/bibr-paper-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use scienceverse/bibr-paper-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="scienceverse/bibr-paper-classifier")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("scienceverse/bibr-paper-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,398 Bytes
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"l1_classes": [
"Agricultural and Veterinary Sciences",
"Engineering and Technology",
"Humanities and the Arts",
"Medical and Health Sciences",
"Natural Sciences",
"Social Sciences"
],
"l2_classes": [
"Agriculture, Forestry, and Fisheries",
"Animal and Dairy Science",
"Arts (arts, history of arts, performing arts, music)",
"Basic Medicine",
"Biological Sciences",
"Chemical Engineering",
"Chemical Sciences",
"Civil Engineering",
"Clinical Medicine",
"Computer and Information Sciences",
"Earth and Related Environmental Sciences",
"Economics and Business",
"Education",
"Electrical Engineering, Electronic Engineering, Information Engineering",
"Environmental Engineering",
"Health Sciences",
"History and Archaeology",
"Languages and Literature",
"Law",
"Materials Engineering",
"Mathematics",
"Mechanical Engineering",
"Media and Communications",
"Medical Biotechnology",
"Medical Engineering",
"Philosophy, Ethics and Religion",
"Physical Sciences",
"Political Science",
"Psychology and Cognitive Sciences",
"Social and Economic Geography",
"Sociology",
"Veterinary Science"
],
"paper_type_classes": [
"case-study",
"commentary",
"empirical",
"erratum",
"meta-analysis",
"retraction",
"review"
]
}
|