deberta-base-long-nli

Name: deberta-base-long-nli
Author: tasksource

by tasksource

871dl/month

27likes

HuggingFace Use in Pipeline

Identifier

Model ID

tasksource/deberta-base-long-nli

Tags

transformerssafetensorsdeberta-v2text-classificationzero-shot-classificationdataset:nyu-mll/gluedataset:aps/super_gluedataset:facebook/anlidataset:tasksource/babi_nlidataset:sickdataset:snlidataset:scitaildataset:hansdataset:alisawuffles/WANLIdataset:tasksource/recastdataset:sileod/probability_words_nlidataset:joey234/nan-nlidataset:pietrolesci/nli_feverdataset:pietrolesci/breaking_nlidataset:pietrolesci/conj_nlidataset:pietrolesci/fracasdataset:pietrolesci/dialogue_nlidataset:pietrolesci/mpedataset:pietrolesci/dncdataset:pietrolesci/recast_whitedataset:pietrolesci/jocidataset:pietrolesci/robust_nlidataset:pietrolesci/robust_nli_is_sddataset:pietrolesci/robust_nli_li_tsdataset:pietrolesci/gen_debiased_nli

Use deberta-base-long-nli 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, deberta-base-long-nli runs as a managed extractor inside a processing pipeline. Point a bucket of zero shot 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.