3 citations · 3 across the 6 of their papers we have counts for
7 papers
SCX Router: Streaming Zero-Shot Model Selection with a Decoder-KV Classifier and a Real-World Task Ontology
Ihor Stepanov, Aleksandr Smechov, Mykhailo Shtopko +2
The rapid proliferation of large language models (LLMs) and the growing diversity of their applications presents a unique optimization opportunity: selecting the right model for th…
Opir: Efficient Multi-Task Safety Classification for Toxicity, Jailbreaks, Hate Speech, and Harmful Content
Ihor Stepanov, Aleksandr Smechov
Real-time safety filtering for large language model (LLM) applications requires classifiers that can detect unsafe prompts, toxic language, jailbreak attempts, and unsafe responses…
GLiNER-Relex: A Unified Framework for Joint Named Entity Recognition and Relation Extraction
Ihor Stepanov, Oleksandr Lukashov, Mykhailo Shtopko +1
Joint named entity recognition (NER) and relation extraction (RE) is a fundamental task in natural language processing for constructing knowledge graphs from unstructured text. Whi…
The Million-Label NER: Breaking Scale Barriers with GLiNER bi-encoder
Ihor Stepanov, Mykhailo Shtopko, Dmytro Vodianytskyi +1
This paper introduces GLiNER-bi-Encoder, a novel architecture for Named Entity Recognition (NER) that harmonizes zero-shot flexibility with industrial-scale efficiency. While the o…
GLiClass: Generalist Lightweight Model for Sequence Classification Tasks
Ihor Stepanov, Mykhailo Shtopko, Dmytro Vodianytskyi +3
Classification is one of the most widespread tasks in AI applications, serving often as the first step in filtering, sorting, and categorizing data. Since modern AI systems must ha…
GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity Recognition
Anthony Yazdani, Ihor Stepanov, Douglas Teodoro
Biomedical named entity recognition (NER) presents unique challenges due to specialized vocabularies, the sheer volume of entities, and the continuous emergence of novel entities.…
GLiNER multi-task: Generalist Lightweight Model for Various Information Extraction Tasks
Ihor Stepanov, Mykhailo Shtopko
Information extraction tasks require both accurate, efficient, and generalisable models. Classical supervised deep learning approaches can achieve the required performance, but the…