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ThothII/docs/reports/2026-09-02-sensitivity-ner-license-inventory.md
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# Optional sensitivity NER license inventory
This inventory covers the isolated `/opt/sensitivity-ner` Python environment built from
`backend/python/sensitivity-ner-requirements.txt` on 2 September 2026. It is a technical release
gate, not legal advice. Every dependency is version-locked; changing any version requires
regenerating this inventory and rerunning the offline CPU smoke test.
The optional runtime also dynamically links Debian's `libseccomp2` (LGPL-2.1-only) solely to
install its kernel-enforced network syscall filter; no libseccomp source is incorporated into ThothII.
No dependency or selected model uses a non-commercial, research-only, source-available, GPL, or
AGPL license. MPL-2.0, PSF-2.0, and the permissive composite licenses below allow free-of-charge and
commercial use, but distributors must still preserve their applicable notices and license texts.
| License family | Locked packages |
| --- | --- |
| Apache-2.0 | `accelerate==1.14.0`, `gliner2==2.0.0`, `hf-xet==1.6.0`, `huggingface-hub==0.36.2`, `peft==0.20.0`, `requests==2.34.2`, `safetensors==0.8.0`, `tokenizers==0.22.2`, `transformers==4.57.6` |
| MIT | `annotated-types==0.8.0`, `charset-normalizer==3.5.1`, `filelock==3.32.5`, `pydantic==2.13.5`, `pydantic-core==2.46.5`, `PyYAML==6.0.3`, `typing-inspection==0.4.4`, `urllib3==2.7.0` |
| BSD-2/3-Clause | `fsspec==2026.7.0`, `idna==3.19`, `Jinja2==3.1.6`, `MarkupSafe==3.0.3`, `mpmath==1.3.0`, `networkx==3.6.1`, `psutil==7.2.2`, `sympy==1.14.0` |
| MPL-2.0 or mixed MPL/MIT | `certifi==2026.7.22`, `tqdm==4.70.0` |
| PSF-2.0 | `typing-extensions==4.16.0` |
| Composite permissive | `numpy==2.5.2` (BSD-3-Clause, 0BSD, MIT, Zlib, CC0), `packaging==26.3` (Apache-2.0 or BSD-2-Clause), `regex==2026.9.3` (Apache-2.0 and CNRI-Python), `torch==2.14.0+cpu` (Apache-2.0, LLVM exception, BSD, BSL-1.0, MIT) |
The selected `fastino/gliner2-privacy-filter-PII-multi` weights at revision
`c153999da5f4c509df4322b0c6a1baf3d2c284d7` are marked Apache-2.0 in the
[model card](https://huggingface.co/fastino/gliner2-privacy-filter-PII-multi). Its published
`microsoft/mdeberta-v3-base` base model is MIT. The Fastino training corpus is described as
synthetic but is not published, so the training process is not independently reproducible.
Before distributing the optional image or model pack:
1. retain the upstream license and notice files for all packaged wheels, system libraries, and weights;
2. archive `THOTHII_MODEL_REVISION` and the verified `MODEL_SHA256SUMS` beside the model;
3. verify that `pip check` succeeds in the isolated environment;
4. compare the installed distribution/version set with this inventory;
5. repeat the licensing review if an upstream artifact or dependency changes.