booteek-ai/pt-pt-lexicon

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 corpus (mirror of 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 (all config)
Upstream	duarteocarmo/fineweb2-bagaco2 → uonlp/CulturaX (pt split) filtered by 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

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

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

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

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

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:

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: scripts/lexicon/extract-pt-pt-corpus.py.

# 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 dataset card.

Attribution

Derived from the FineWeb2 Bagaço2 pt-PT slice maintained by Duarte O. Carmo (FOSDEM speaker, pt-PT NLP). Upstream corpus: CulturaX. pt-PT classifier: fasttext-euptvid.

Maintainer

booteek-ai on HuggingFace. Booteek is Europe's first AI Visibility platform for independent restaurants and bars (booteek.ai). The lexicon is open under MIT for any pt-PT NLP work — not specific to hospitality.