HDLTex: Hierarchical Deep Learning for Text Classification
Paper
•
1709.08267
•
Published
BERT classifier for WOS-46985
This is a model to classify scientific papers by the Web-of-Science nomenclature.
Model Details
Model Description
It's a fine-tuned model to predict the 134 classes from the WOS-46985 model published by https://arxiv.org/pdf/1709.08267.pdf.
- Developed by: Terran (https://terran.ai/) SciLifeLab Data Center (https://www.scilifelab.se/) and KTH Research Support Office (https://intra.kth.se/en/styrning/kths-organisation/vs/rso).
- License: Apache 2.0
- Finetuned from model: bert-base-uncased
Evaluation
10/90 validation/training split (like https://arxiv.org/pdf/1709.08267.pdf)
Results
Accuracy on the final layer was 83% (previous state-of-the-art 77% https://arxiv.org/pdf/1709.08267.pdf). However, the previous SOTA did not use test-data set, so the difference is probably more significant.
Summary
Useful model to annotate scientific text.
- Downloads last month
- 10