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    Models/Token Classification/tomaarsen/span-marker-bert-base-uncased-acronyms
    Token Classificationspan-markerapache-2.0

    span-marker-bert-base-uncased-acronyms

    by tomaarsen

    247Kdl/month
    6likes
    Identifier
    Model ID
    tomaarsen/span-marker-bert-base-uncased-acronyms

    Tags

    span-markerpytorchtensorboardsafetensorstoken-classificationnernamed-entity-recognitiongenerated_from_span_marker_trainerendataset:acronym_identificationbase_model:google-bert/bert-base-uncasedbase_model:finetune:google-bert/bert-base-uncasedlicense:apache-2.0model-indexco2_eq_emissionsregion:us

    Use span-marker-bert-base-uncased-acronyms 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, span-marker-bert-base-uncased-acronyms 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.