Text Classification
Model2Vec
Safetensors
English
pubguard
document-classification
scientific-papers
ai-detection
toxicity-detection
pubverse
publication-screening
quality-control
Instructions to use jimnoneill/pub-guard with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
How to use jimnoneill/pub-guard with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("jimnoneill/pub-guard") - Notebooks
- Google Colab
- Kaggle
File size: 938 Bytes
c22be37 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"model_type": "pubguard",
"architecture": "multi_head_linear",
"embedding_backbone": "minishlab/potion-base-32M",
"embedding_dim": 512,
"structural_features": 14,
"heads": {
"doc_type": {
"classes": [
"scientific_paper",
"poster",
"abstract_only",
"junk"
],
"num_classes": 4,
"input_dim": 526,
"accuracy": 0.997,
"head_file": "models/head_doc_type.npz"
},
"ai_detect": {
"classes": [
"human",
"ai_generated"
],
"num_classes": 2,
"input_dim": 512,
"accuracy": 0.834,
"head_file": "models/head_ai_detect.npz"
},
"toxicity": {
"classes": [
"clean",
"toxic"
],
"num_classes": 2,
"input_dim": 512,
"accuracy": 0.847,
"head_file": "models/head_toxicity.npz"
}
},
"classifier": "LogisticRegression",
"library_name": "model2vec"
} |