Multimodal publication classifier with LLM and deep learning. Fuses transformer embeddings from Hugging Face with tabular features through a multilayer perceptron (MLP) on PyTorch for human-in-the-loop screening. Screen by matched rules, by semantic similarity through sentence-transformers, by the classifier with active learning, or by a language model as a second rater. Read exports in every format bibliometrix accepts, extract page-anchored evidence with pdfplumber, and report SAFE stopping and PRISMA 2020 Item 8.
Installation
pip install pubmlp
With optional dependencies:
pip install "pubmlp[screening]" # regex and semantic screening, stratified sampling
pip install "pubmlp[fulltext]" # PDF reading with page-anchored evidence
pip install "pubmlp[benchmark]" # SYNERGY benchmark datasets
From GitHub:
pip install git+https://github.com/mshin77/pubmlp.git
Getting Started
See Quick Start and Screening Workflow for tutorials.
Citation
- Shin, M. (2026). pubmlp: Multimodal publication classifier with LLM and deep learning (Python package version 0.6.0) [Computer software]. https://github.com/mshin77/pubmlp
Reference
- Shin, M., & McKenna, J. (2026). Exploring the research landscape on single-case design methodology using technology through text mining and large language models. Journal of Behavioral Education. https://doi.org/10.1007/s10864-026-09630-1
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