bert-base-NER named-entity recognition (PER / ORG / LOC / MISC)

models.skillsafe.ai/bert-base-ner@d1a3e8f1/

Vetted New WebGPU

Token classification model for transformers.js (fp32). Runs on WASM or WebGPU — 411 MB downloaded once from models.skillsafe.ai, then cached for every SkillSafe app that uses it. Inference happens on your device; nothing you enter is uploaded to load it, and it never costs a credit. Weights from Hugging Face · skillsafe-ai/bert-base-ner, pinned at 521592d1b655 — also loadable straight from Hugging Face outside SkillSafe (how).

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Details

Catalogue idbert-base-ner@d1a3e8f1
Runtimetransformers.js ≥ 3.0.0
DeviceWASM, WebGPU
Variantfp32
Download411 MB · 1 file
LicenceMIT · notice
Pinned at521592d1b65574558aae0ac8e99e13909ab6b8e6
ApprovedSep 22, 2026
Statusactive — every file is a live vetted hash

Files

Each file is served at an immutable URL; the canonical form is the SHA-256 itself. Tokenizer and config JSON are not here by design — they ship in your app bundle.

PathFormatSizeSHA-256
onnx/model.onnx onnx 411 MB 963039b81eec…384617

Signature

Graph inputs and outputs read from the ONNX bytes at vetting — the tensor names your session.run() call feeds and reads. Symbolic dimensions are shown by name.

Inputs

  • input_ids int64 [batch_size, sequence_length]
  • attention_mask int64 [batch_size, sequence_length]
  • token_type_ids int64 [batch_size, sequence_length]

Outputs

  • logits float32 [batch_size, sequence_length, 9]
Use it in an app declaration · SDK loader · transformers.js · URLs · Hugging Face — generated from this entry

Add to the body of POST /v1/apps/{slug}/releases (or a release session). An unknown or withdrawn model is refused with a 400 naming it; the app page then shows "downloads 411 MB · runs on your device" and /models.txt carries the attribution.

{
  "models": [
    {
      "id": "bert-base-ner",
      "revision": "d1a3e8f1"
    }
  ]
}

Evaluation

onnx.checker + onnxruntime CPU smoke run with zero-filled inputs at the declared shapes; per-file SHA-256 pinned to the source; ONNX output vs the PyTorch model from the same commit on real text (import.parity) — imported as published upstream, then checked. Evaluated Sep 22, 2026.

Parity against transformers BertForTokenClassification from same snapshot, fp32 · logits

fileprecisionmax absmean abscosine / PSNR
onnx/model.onnxfp326.7e-61.1e-61.000000

Runs under onnxruntime

Zero-filled inputs at the declared shapes, CPU execution provider on the converter host; the check is that the graph loads, runs, and emits the declared output shapes.

  • onnx/model.onnx: input_ids[1,8], attention_mask[1,8], token_type_ids[1,8] → logits[1,8,9] 6.7 ms

Toolchain: python 3.12.13 · platform Darwin 25.6.0 arm64 · torch 2.10.0 · onnx 1.23.0 · onnxruntime 1.30.0. Recipe models/recipes/bert-base-ner.yaml (083282ba04c7). Full manifest.json

Licence & attribution

MIT · licence text · notice

bert-base-NER: David S. Lim, MIT License. https://huggingface.co/dslim/bert-base-NER — the repo's own ONNX export.

Apps that declare this model get this text in their generated /models.txt, so a licence that requires a notice always carries one.

Every file here was approved by exact SHA-256 after a structural audit of the graph, fetched from a content-pinned source, and is served credential-free at an immutable URL. The SDK re-verifies the hash on your device before it caches or returns anything. Missing a variant? Request it — or read how the registry works.