Cross-verification guide — PFLT / FSOT

This document tells independent reviewers how to obtain the actual program, reproduce catalog metrics, and score sentence benchmarks.
It is intentionally separate from marketing-style cards.

Version: 0.2.1
Law: (S = K(T_1+T_2+T_3)) authority pin D1D38A
Git tag: v0.2.0 / main (see release notes for snapshot scores)

1. Where the code lives (not just metrics)

Location	What you get
GitHub (canonical)	Full program: Python data factory + Ada/SPARK product sources + docs + eval scripts
Hugging Face model `dappalumbo91/pflt-fsot`	Same verification tree (source + docs + reports + sample data) + Gradio demo
Hugging Face dataset `dappalumbo91/pflt-fsot-sample`	Metrics + sample densify + reports (lighter mirror)
Kaggle `damianpalumbo/pflt-fsot-benchmarks`	Full verification source pack + benchmark JSON/CSV

Not redistributed (too large / third-party): multi-GB densify/gold packs, full Tatoeba extract, Kaikki dumps, neural weight caches. Scripts document how to rebuild or download those locally.

Canonical code URL:

https://github.com/dappalumbo91/protofluid-language-translator

Clone:

git clone https://github.com/dappalumbo91/protofluid-language-translator.git
cd protofluid-language-translator
git checkout v0.2.0   # or latest main

From Hugging Face (full tree upload):

# requires: pip install huggingface_hub
huggingface-cli download dappalumbo91/pflt-fsot --local-dir ./pflt-fsot --repo-type model

From Kaggle:

kaggle datasets download -d damianpalumbo/pflt-fsot-benchmarks -p ./pflt-kaggle --unzip

2. Repository layout (what matters for verification)

protofluid-language-translator/
  README.md
  docs/                     # competitive position, law audit, release, THIS file
  PFLT_FSOT_2_1_aligned.py  # Python law-aligned surface
  fsot_law_bridge.py        # pin D1D38A bridge
  dual_track_eval.py        # open vs product tracks
  huggingface_pflt/         # Hub card + Gradio demo
  pflt-Ada/                 # **shipping product (Ada/SPARK)**
    src/*.ads *.adb         # full Ada sources
    *.py                    # densify / M6 / solidify / WMT eval factory
    reports/                # measured JSON + markdown
    data/                   # small samples + archive pin artifacts (not multi-GB packs)
  release/metrics_snapshot.json
  kaggle_pflt/              # Kaggle packaging mirror

3. Reproduce without multi-GB packs (smoke)

python -c "import ast; ast.parse(open('PFLT_FSOT_2_1_aligned.py',encoding='utf-8').read()); print('python parse ok')"
# Ada (if GNAT/Alire installed):
cd pflt-Ada && alr build

Law pin artifact (archive SHA culture):

pflt-Ada/data/archive_linguistics/fsot_compute_AUTHORITY_PIN.json
pflt-Ada/data/archive_linguistics/live_pin.json

4. Reproduce form→gloss catalog claims

Requires local densify/gold rebuilt via scripts (or your own packs). Then:

cd pflt-Ada
python report_translation_coverage.py
python solidify_covered_95.py   # optional solidify pass
# Ada product eval if built:
.\bin\pflt_main.exe eval-product

Published snapshot: ~113 langs, ~99.99% open/product form→gloss (see reports/gap_fill_verify.json, metrics_snapshot.json).

5. Reproduce sentence benchmarks (HF-aligned protocol)

5a WMT14 German→English (public, no Tatoeba dump)

cd pflt-Ada
# Requires: pip install transformers datasets sacrebleu torch
# Local model: Helsinki-NLP/opus-mt-de-en and/or facebook/nllb-200-distilled-600M
python -u eval_wmt14_deen.py
# Full dual-track SOTA script:
python -u m6_sota_push.py

Published v0.2.0 (self-reported):

System	sacreBLEU
opus-mt-de-en beams=5	33.88
NLLB-600M beams=5	33.37

5b Chat / multi-lang neural

m6_sota_push.py scores Tatoeba-style samples when local pair cache exists. Without cache, still verify script + report JSON:

pflt-Ada/reports/m6_sota_push_report.json
pflt-Ada/reports/M6_SOTA_PUSH.md
release/metrics_snapshot.json

Published mean best chat sacreBLEU: 50.19 (16 langs, ≤200/lang, best of opus/mul/NLLB).

6. What we do not claim

Commercial DeepL / Google news SOTA
FLORES scores (Hub parquet may still be gated)
That product densify BLEU with residual TM templates is open-set generalization

Honest framing: docs/COMPETITIVE_POSITION.md.

7. Minimal file set for peer review checklist

Ada sources under pflt-Ada/src/ present and buildable or reviewable
Python eval scripts: m6_sota_push.py, eval_wmt14_deen.py, solidify/coverage
Reports JSON match model-card tables
Law pin / constants not silently refit to BLEU
Large third-party corpora not required to read the code

8. Contact / issues

Open issues on GitHub:
https://github.com/dappalumbo91/protofluid-language-translator/issues