docs: dataset card v1 (booteek pt-PT lexicon)

README.md

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+---
+license: mit
+language:
+- pt
+tags:
+- portuguese
+- pt-PT
+- european-portuguese
+- lexicon
+- ngram
+- phrase-bank
+- hospitality
+- nlp
+pretty_name: booteek pt-PT lexicon
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-generation
+- feature-extraction
+---
+
+# booteek pt-PT lexicon
+
+A SQLite lexicon of European Portuguese (**pt-PT**) unigrams, bigrams (PMI-ranked),
+phrases (3/4/5-grams), and diagnostic tokens (pt-PT vs pt-BR variants), derived from
+the [`booteek-ai/fineweb2-bagaco2`](https://huggingface.co/datasets/booteek-ai/fineweb2-bagaco2)
+corpus (mirror of [`duarteocarmo/fineweb2-bagaco2`](https://huggingface.co/datasets/duarteocarmo/fineweb2-bagaco2)).
+
+Built for grounding LLM outputs in **European Portuguese** (not Brazilian) — Haiku,
+GPT-4, Gemini all default to pt-BR phrasing when asked for "Portuguese" without aggressive
+prompting. This lexicon provides a phrase bank + collocation index + diagnostic tokens that
+applications can query at runtime to:
+
+- Score the pt-PT confidence of generated text
+- Detect pt-BR-isms (`você` vs `tu`, `ônibus` vs `autocarro`, `acreditar` vs `crer`, …)
+- Inject collocations and phrases into prompts as few-shot grounding
+- Surface under-served pt-PT search vocabulary for SEO/AEO
+
+## Provenance
+
+| Field | Value |
+|---|---|
+| Source corpus | [`booteek-ai/fineweb2-bagaco2`](https://huggingface.co/datasets/booteek-ai/fineweb2-bagaco2) (`all` config) |
+| Upstream | [`duarteocarmo/fineweb2-bagaco2`](https://huggingface.co/datasets/duarteocarmo/fineweb2-bagaco2) → [`uonlp/CulturaX`](https://huggingface.co/datasets/uonlp/CulturaX) (pt split) filtered by [`duarteocarmo/fasttext-euptvid`](https://huggingface.co/duarteocarmo/fasttext-euptvid) |
+| Docs processed | 1,000,000 |
+| Tokens processed | ~390 million |
+| Extraction date | 2026-05-13 |
+| Extractor | `extract-pt-pt-corpus.py` v2026-05-13-v2-all-1M |
+| Output size | ~17 MB |
+
+## Schema
+
+Five tables. Inspect provenance with `SELECT key, value FROM meta`.
+
+### `unigrams`
+```sql
+CREATE TABLE unigrams (
+  token TEXT PRIMARY KEY,   -- surface form, accents preserved
+  count INTEGER NOT NULL,   -- corpus frequency
+  doc_freq INTEGER NOT NULL,
+  pt_pt_score REAL          -- avg ptpt_score from upstream
+);
+```
+
+### `bigrams`
+```sql
+CREATE TABLE bigrams (
+  token1 TEXT, token2 TEXT,
+  count INTEGER NOT NULL,
+  pmi REAL NOT NULL,        -- pointwise mutual information, ≥ 3.0
+  PRIMARY KEY (token1, token2)
+);
+```
+
+### `phrases`
+```sql
+CREATE TABLE phrases (
+  phrase TEXT PRIMARY KEY,
+  length INTEGER NOT NULL,  -- 3, 4, or 5
+  count INTEGER NOT NULL,
+  domain_hint TEXT          -- optional: food | service | team | NULL
+);
+```
+
+### `diagnostic_tokens`
+```sql
+CREATE TABLE diagnostic_tokens (
+  token TEXT PRIMARY KEY,
+  variant TEXT NOT NULL,    -- 'pt-PT' | 'pt-BR'
+  notes TEXT
+);
+```
+
+Used at query-time to compute pt-PT confidence and detect pt-BR-isms.
+
+### `meta`
+Extraction provenance (source dataset, doc count, token count, extracted_at,
+extractor_version, license, attribution chain).
+
+## Usage
+
+### Python
+```python
+import sqlite3
+from huggingface_hub import hf_hub_download
+
+path = hf_hub_download(
+    repo_id="booteek-ai/pt-pt-lexicon",
+    filename="pt-pt-corpus.sqlite",
+    repo_type="dataset",
+)
+db = sqlite3.connect(path)
+
+# Top collocations for "vinho"
+rows = db.execute(
+    "SELECT token2, count, pmi FROM bigrams WHERE token1=? ORDER BY pmi DESC LIMIT 5",
+    ("vinho",),
+).fetchall()
+# → [('tinto', ...), ('branco', ...), ('verde', ...), ('porto', ...), ...]
+```
+
+### TypeScript (via better-sqlite3)
+booteek consumes this lexicon via `src/lib/lexicon/pt-pt.ts`. Surface API:
+```ts
+import {
+  getTopCollocations,
+  getCommonPhrases,
+  ptPtConfidence,
+  detectPtBrIsms,
+} from '@/lib/lexicon/pt-pt';
+
+getTopCollocations('vinho', 5);
+// → ['vinho tinto', 'vinho branco', 'vinho do porto', ...]
+
+ptPtConfidence('Apanho o autocarro e ponho a comida no frigorífico.');
+// → 0.95   (high pt-PT confidence)
+
+detectPtBrIsms('Vou pegar o ônibus.');
+// → [{ token: 'ônibus', variant: 'pt-BR', ptPtAlternative: 'autocarro' }]
+```
+
+## Re-extraction
+
+The corpus is built from `booteek-ai/fineweb2-bagaco2`. See the extraction pipeline in
+the [booteek monorepo](https://github.com/anthonyporto/booteek):
+`scripts/lexicon/extract-pt-pt-corpus.py`.
+
+```bash
+# Full corpus extraction (1M docs, ~90 minutes against pre-cached parquet shards)
+.venv/bin/python scripts/lexicon/extract-pt-pt-corpus.py \
+    --config all \
+    --max-docs 1000000 \
+    --min-bigram-pmi 4.0
+```
+
+## License
+
+**MIT** — preserved from the upstream
+[`duarteocarmo/fineweb2-bagaco2`](https://huggingface.co/datasets/duarteocarmo/fineweb2-bagaco2)
+dataset card.
+
+## Attribution
+
+Derived from the FineWeb2 Bagaço2 pt-PT slice maintained by
+[**Duarte O. Carmo**](https://huggingface.co/duarteocarmo) (FOSDEM speaker, pt-PT NLP).
+Upstream corpus: [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX).
+pt-PT classifier: [fasttext-euptvid](https://huggingface.co/duarteocarmo/fasttext-euptvid).
+
+## Maintainer
+
+[`booteek-ai`](https://huggingface.co/booteek-ai) on HuggingFace.
+Booteek is Europe's first AI Visibility platform for independent restaurants and bars
+([booteek.ai](https://booteek.ai)). The lexicon is open under MIT for any pt-PT
+NLP work — not specific to hospitality.