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Two small chat models run in a computer’s browser here; every other model is a file you download.

NLPclass

Named-entity-recognition

Can I run Named-entity-recognition on my computer?

It depends on your memory, processor, file format and runtime. Hugging Bay offers 5 hosted files; check the file list for the exact selection. A file-size fit is an estimate, not a successful run or speed promise. Read the license before use.

Named-entity-recognition is an open-weights language model for token classification. Model weights are learned parameters; a runtime loads them. A hosted file is an individual file Hugging Bay can serve. SHA-256 identifies exact file bytes. The availability date above applies to this file record, not the upstream metadata update date.

As of the recorded file availability check on .

Recorded SHA-256 for model.safetensors: acd4db56b9a69adde41f8a92cf00387aa586d785347e60be7e446e794eecfacb. Exact file evidence · Machine-readable source facts · Run evidence and any recorded receipts. A checksum fingerprints only the named file bytes; it does not prove safety, quality, a complete multi-file selection or a successful run.

Updated · Page-copy source revision 8871bab84e1cf80aea1686a944ec335078a6c036 · Source files: apps/web/lib/model-page-template.mjs, apps/web/lib/model-page-v3.mjs, apps/web/lib/aeo-page.mjs · Full revision record

Model metadata: As of at the model source. Popularity counters are source-reported, not measured by Hugging Bay. Availability checked .

NLPclass/Named-entity-recognition
Model details
token classificationLicense: mitSHA-256 recorded for 5 of 7 filessafetensors

Content controls: not assessed.

The catalog lists this model for labeling parts of text. Catalog sources: license, cardMetadata.license, task, cardMetadata.pipelineTag.

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