datasocietyco/bge-base-en-v1.5-course-recommender-v5 warn
claims base: BAAI/bge-base-en-v1.5 · chat template: not in config · view on Hugging Face ↗
Ingot findings
Static battery: 1 medium finding(s). Deep battery (behavioral differential, glitch-token pass) not yet run. Weights battery: embedding-norm scan over 30522 tokens (F32, 768-dim) found 0 undertrained candidates, 0 plain-ASCII. Lineage vs BAAI/bge-base-en-v1.5: consistent. Scanned 2026-08-20 (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 Embedding-norm glitch scan clean
No undertrained tokens found: every non-special token's embedding norm is above 0.3× the vocabulary median (1.606). The glitch-token data-corruption class has no candidate surface in this model.
How to fixruntime guardweight-level
Keep the affected token strings out of the model's input — the scan-derived runtime guard carries this model's exact blocklist.
- Fetch this model's guard artifact (`/api/v1/guard/<owner>/<model>`): the confirmed corrupting tokens and the low-norm candidate list, derived from the published scan.
- Screen inbound text with it (the `@ingotai/guard` package is a reference implementation) and route flagged records to a different model or human review — verbatim-copy tasks on flagged strings are the failure mode.
- The underlying cause is undertrained embeddings in the weights; a true fix is weight-level (continued pretraining on the affected tokens) — that is not a patch, it's a training job.
info Weights consistent with claimed parent BAAI/bge-base-en-v1.5
Mean cosine similarity of 64 sampled token-embedding rows against BAAI/bge-base-en-v1.5 is 1.000 — the weights plausibly descend from the declared base (relation: unspecified).
How to fix
Fix or verify the `base_model` declaration so lineage checks can run.
- If you own the repo: correct the `base_model` field in the model card metadata to the real, public parent.
- If you don't: identify the true parent (config architecture + weight shapes narrow it fast) and re-scan with that lineage in mind.
Remediation guidance addresses the documented findings only. It is evidence-driven repair, not a safety certification of the model.
Fingerprint
The durable weights-and-metadata profile of this model, rebuilt on every scan and deep-battery run. Updated 2026-08-20.
| architecture | bert · 12 layers · 768-dim |
| parameters | 109.5M |
| vocabulary | 30,522 tokens |
| license | none declared |
| serialization | safetensors |
| chat template | none |
| claimed lineage | BAAI/bge-base-en-v1.5 |
| lineage verified | consistent vs BAAI/bge-base-en-v1.5 — embedding-row cosine 1.000 |
| glitch-token surface | clean no undertrained tokens |
Full fingerprint
| architectures | BertModel |
| library | sentence-transformers |
| pipeline | sentence-similarity |
| repo files | 12 |
| revision | 2b069eed51ce |
| HF snapshot | 4.2M downloads · 1 likes · updated 2025-01-09 · captured 2026-08-20 |
| embedding tensor | embeddings.word_embeddings.weight · F32 · 30,522×768 |
| embedding norms | median 1.6059 · mean 1.5983 |
| lineage check | consistent — cosine 1 over 64 sampled rows vs BAAI/bge-base-en-v1.5 |
Verdict badge
Ship the verdict in your README — it always shows the latest published analysis:
[](https://ingot.tools/models/datasocietyco/bge-base-en-v1.5-course-recommender-v5)