NEWVectors or files. Pick a path.Start →
    Models/Tabular Regression/NeoZJX/m8-n0-numeric-equity-ranking
    Tabular Regressionpytorchcc-by-nc-4.0

    m8-n0-numeric-equity-ranking

    by NeoZJX

    Identifier
    Model ID
    NeoZJX/m8-n0-numeric-equity-ranking

    Tags

    pytorchsafetensorsm8_n0_numericfinancequantitative-financeequity-rankingcross-sectionaltabularnon-commercialtabular-regressionendataset:khaihernlow/fnspidarxiv:2402.06698license:cc-by-nc-4.0model-indexregion:us

    Use m8-n0-numeric-equity-ranking on Mixpeek

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

    Open Studio

    How It Runs on Mixpeek

    On Mixpeek, m8-n0-numeric-equity-ranking runs as a managed extractor inside a processing pipeline. Point a bucket of tabular regression 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.