OpenMed ZeroShot NER BloodCancer Small 166M EU AI Act Compliance Profile

🧬 OpenMed-ZeroShot-NER-BloodCancer-Small-166M

Specialized model for Clinical Entity Recognition - Clinical entities related to Chronic Lymphocytic Leukemia

📋 Model Overview

Domain-tuned for Chronic Lymphocytic Leukemia (CLL) terminology, capturing disease descriptors, biomarkers, and therapies.Supports hematology research, treatment response analysis, and clinical evidence tracking.

OpenMed ZeroShot NER is an advanced, domain-adapted Named Entity Recognition (NER) model designed specifically for medical, biomedical, and clinical text mining. Leveraging state-of-the-art zero-shot learning, this model empowers researchers, clinicians, and data scientists to extract expert-level biomedical entities—such as diseases, chemicals, genes, species, and clinical findings—directly from unstructured text, without the need for task-specific retraining.

Built on the robust GLiNER architecture and fine-tuned on curated biomedical corpora, OpenMed ZeroShot NER delivers high-precision entity recognition for critical healthcare and life sciences applications. Its zero-shot capability means you can flexibly define and extract any entity type relevant to your workflow, from standard biomedical categories to custom clinical concepts, supporting rapid adaptation to new research domains and regulatory requirements.

Whether you are working on clinical NLP, biomedical research, electronic health record (EHR) de-identification, or large-scale literature mining, OpenMed ZeroShot NER provides a production-ready, open-source solution that combines expert-le

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