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    Models/Audio Classification/somosnlp-hackathon-2022/wav2vec2-base-finetuned-sentiment-classification-MESD
    Audio Classificationtransformersapache-2.0

    wav2vec2-base-finetuned-sentiment-classification-MESD

    by somosnlp-hackathon-2022

    Identifier
    Model ID
    somosnlp-hackathon-2022/wav2vec2-base-finetuned-sentiment-classification-MESD

    Tags

    transformerspytorchtensorboardsafetensorswav2vec2audio-classificationgenerated_from_trainerlicense:apache-2.0endpoints_compatibleregion:us

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    How It Runs on Mixpeek

    On Mixpeek, wav2vec2-base-finetuned-sentiment-classification-MESD runs as a managed extractor inside a processing pipeline. Point a bucket of audio 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.