microsoft/mpnet-base warn
No license declared — no usage rights by default.
chat template: not found · view on Hugging Face ↗
Scan coverageStatic battery2026-08-25Weights battery2026-08-25Behavioral batteryn/adetails
| Battery | Looks at | Status |
|---|---|---|
| Static battery | Metadata & packaging | complete 2026-08-25 |
| Weights battery | Weights forensics: no GPU, no download | complete 2026-08-2530,527-token embedding scanned · 0 undertrained |
| Behavioral battery | Live-inference differentials | n/anot applicable: fill-mask model has no text-generation surface to probe |
Ingot runs three batteries against a model. What each one checks →
Findings
Scanned 2026-08-25 · published from a community scan.
medium No license declared
The model card declares no license. You have no usage rights by default — treat as all-rights-reserved until the owner clarifies.
How to fix
Get a license from the owner or pick a licensed alternative — this is a legal gap, not a technical one.
- With no declared license you have no usage rights by default; treat the weights as all-rights-reserved.
- Open an issue or discussion on the repo asking the owner to declare a license, or use the licensed upstream/parent model instead.
info Partial coverage — not a generative language model
fill-mask model — no generation surface, so behavioral (live-inference) checks are not applicable; packaging, license, and weights forensics apply.
info Embedding-norm glitch scan clean
No undertrained tokens found: every non-special token's embedding norm is above 0.3× the vocabulary median (2.419). The glitch-token data-corruption class has no candidate surface in this model.
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Check every checkpoint before it ships
Use the web app, API, CLI, or CI gate to scan candidate checkpoints and catch model drift before deployment. Public-model scans publish to the open database; paid plans add the volume needed for continuous checks.
Fingerprint
The durable profile of this model: measured weights-and-metadata facts, rebuilt on every scan and battery run. Updated 2026-08-25.
| architecture | mpnet · 12 layers · 768-dim |
| parameters | 133.0M |
| vocabulary | 30,527 tokens |
| license | none declared |
| serialization | safetensors pickle |
| chat template | none |
| glitch-token surface | clean no undertrained tokens |
Full measured fingerprint
| architectures | MPNetForMaskedLM |
| library | transformers |
| pipeline | fill-mask |
| repo files | 8 — pickle: pytorch_model.bin |
| revision | 6996ce1e91bd |
| HF snapshot | 186.5k downloads · 51 likes · updated 2024-02-29 · captured 2026-08-25 |
| embedding tensor | mpnet.embeddings.word_embeddings.weight · F32 · 30,527×768 |
| embedding norms | median 2.4192 · mean 2.34 |
| lineage check | no claimed base model |
Battery runs (1)the run trace behind the findings — what each job measured
| battery | status | queued | duration | attempts |
|---|---|---|---|---|
| weights | complete | 2026-08-25 20:26 | 6s | 1 |
weights run 2026-08-25 — measurements
| probes run | glitch-norm-scan, zero-template-token-scan, lineage-norm-correlation |
| embedding tensor | mpnet.embeddings.word_embeddings.weight · F32 · 30,527×768 |
| glitch surface | 0 undertrained, 0 plain-ASCII |
| lineage check | not checked (no claimed base model) |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/microsoft/mpnet-base)