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    Models/Token Classification/OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M
    Token Classificationglinerapache-2.0

    OpenMed-ZeroShot-NER-Oncology-Multi-209M

    by OpenMed

    Identifier
    Model ID
    OpenMed/OpenMed-ZeroShot-NER-Oncology-Multi-209M

    Tags

    glinerpytorchtoken-classificationentity recognitionnamed-entity-recognitionzero-shotzero-shot-nerzero shotbiomedical-nlpcancer-geneticsoncologygene-regulationcancer-researchamino_acidanatomical_systemcancercellcellular_componentdeveloping_anatomical_structuregene_or_gene_productimmaterial_anatomical_entitymulti-tissue_structureorganorganismorganism_subdivisionorganism_substancepathological_formationsimple_chemicaltissueen

    Use OpenMed-ZeroShot-NER-Oncology-Multi-209M on Mixpeek

    Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

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    How It Runs on Mixpeek

    On Mixpeek, OpenMed-ZeroShot-NER-Oncology-Multi-209M runs as a managed extractor inside a processing pipeline. Point a bucket of token classification data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

    Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.