European Portuguese Spoken Frequency List

A frequency list of the 10,000 most common lemmas in European Portuguese film and TV dialogue, built from the Portugal-tagged portion of the OpenSubtitles 2018 corpus — 636 million tokens of subtitle text.

Ready-to-import Anki decks are included, with an English gloss and a real example sentence on every card.

Entries	10,000 rows (two files of 5,000): 9,392 distinct words, 309 of them listed once per part of speech, plus 296 multi-word expressions
Corpus	OPUS OpenSubtitles v2018, pt (Portugal-tagged)
Corpus size	118,469,705 lines · 636,582,535 tokens · 827,895 unique surface types
Lemmatization accuracy	98.5% on a 332-word human-reviewed sample
Glosses	All 10,000 have an English gloss; 9,808 also have an example sentence from the corpus. A native-speaker teacher checked all of the top 500: 479 good, 20 fix, 1 wrong
Part of speech	From a neural tagger (Stanza), by majority vote over up to 50 sentences per word
Licence	Data and docs (out/, eval/, reports/, archive/, README, CHANGELOG, DATA_SOURCES): CC BY-SA 4.0 — see LICENSE-DATA. Code (scripts/, tests/, config.yaml, requirements.txt): MIT — see LICENSE

An earlier version of this list (April 2026) was never published; the pipeline that produced it was lost. It is kept for comparison only in archive/out_april_2026/ (tag v0-april-2026), and CHANGELOG.md records how this list differs from it.

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Who this is for

Learners of European Portuguese who want to study vocabulary in the order they are likely to actually hear it. If you have worked through a beginner course and want a principled answer to "what should I learn next?", a frequency list built from dialogue is a reasonable guide — and the Anki decks in out/ let you start tonight.

It is particularly aimed at learners who keep finding that the Portuguese in their textbook is Brazilian, or that their word list is drawn from newspapers rather than conversation.

Researchers and tool-builders may find the lists useful as a rough register baseline, provided you read the Limitations below first and treat the numbers accordingly.

It is not a course, a dictionary, or a substitute for either. The glosses in out/glosses.tsv are a one-to-three-word English hint per entry and one real corpus sentence, written by a language model in a single pass — a snapshot of what that model said on one day, not a lexicographer's definition.

The first 500 have been checked by a native-speaker Portuguese teacher, over two rounds covering all 500: she passed 479, asked for a fix on 20 and called one wrong, and her corrections are in the published glosses. Those are the words a learner meets first, which is where the checking went.

The other 9,500 have not been checked by anyone. A sample of 200 of them is set aside for review and is still unreviewed. Below rank 500, treat a gloss as a starting point and a dictionary as the authority.

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Limitations

Please read this section before you rely on anything here. This is a hobbyist project, and being honest about what it is not is more useful than overselling what it is.

This is subtitle text, not a balanced corpus

Every number here comes from one source: subtitles for films and TV shows. That has real consequences.

• Subtitles are written approximations of speech, not speech. They are condensed to fit reading speed, cleaned of disfluencies, hesitations, repairs, and overlaps, and they lose everything about how something was said. Real conversation looks different.
• The register is dramatic dialogue. Film and TV over-represent crime, conflict, romance, and emergencies. Matar ("to kill") lands at rank 121, morrer at 153, arma at 248 and polícia at 267 — higher than any corpus of everyday life would put them. The vocabulary of work, admin, school and errands correspondingly sits lower.
• Much of it is translated. A large share of these subtitles are translations of English-language film and TV, so the phrasing can carry translation artifacts and English-shaped idiom rather than natively produced Portuguese.
• The pt tag is corpus metadata, not verified provenance. It is a strong signal of European Portuguese, but Brazilian material can leak through. An explicit BP exclusion list is applied, and it is a blunt instrument — it removes obvious markers, not every Brazilianism.
• It is single-genre and one snapshot in time. No news, no academic prose, no fiction, no non-fiction, no actual recorded conversation, and nothing newer than the 2018 corpus release.
• Neighbouring lines are not a scene. The corpus file is a stream of subtitle lines that has been deduplicated, reordered and interleaved across different subtitle versions of the same films, so two adjacent lines usually do not belong to one exchange. Counting and tagging are unaffected, since both work a line at a time, but every "sample sentence" in this project is an isolated line: do not read the lines around one as its context, and do not build an annotation task that relies on them.

How this differs from Davies

The standard published reference is Mark Davies and Ana Maria Raposo Preto-Bay's A Frequency Dictionary of Portuguese (Routledge, 2008). If you are choosing between them, choose Davies — it is peer-reviewed, professionally edited, and comes with the things this list lacks.

	This list	Davies
Corpus	~636M tokens, subtitles only	~20M tokens across four registers, unevenly weighted: 18M written and 2M spoken
Registers	Film/TV dialogue	Fiction, newspaper and academic at 6M words each, and spoken at 2M — 10% of the corpus. Each register is half Portugal, half Brazil. Nearly all of it is 1970–2000, mostly the 1990s
Ordering	Raw frequency: occurrences counted, ranked, published	A composite score, not raw frequency. Every register counts equally whatever its size, and within a register the score is half log frequency and half range — how many of 100 blocks of 200,000 words the word turns up in. An evenly spread word can outrank a commoner one that clusters
Variety	Portugal-tagged, BP markers excluded post hoc; headwords in post-1990 spelling	Both varieties, with BP/EP marked per entry
Glosses & examples	One to three English senses per entry and one example sentence taken verbatim from the corpus, written by a language model in one pass. The top 500 checked by a native-speaker teacher; the other 9,500 unchecked	English glosses and example sentences throughout, written and edited by lexicographers
Lemmatization	Automatic: a neural tagger (Stanza) reading each word in up to 50 real sentences, checked against a dictionary. 98.5% correct on a 332-word hand-checked sample	Curated
POS tags	Automatic, from the same tagger: majority vote over each word's sample sentences, and a word used as two parts of speech is listed once for each. Not hand-checked	Professionally tagged
Contractions (do, ao, pela)	Kept as their own entries	Split into preposition + article
Review	Automatic output, with about 1,360 decisions made by hand: the gold set, gender pairs and proper-noun drops, plus a native speaker's verdict on every one of the top 500 glosses. Below rank 500 it is not edited entry by entry	Edited and peer-reviewed
Cost	Free	A book you buy

One row is worth spelling out, because it is the easiest way to compare the two lists wrongly. Davies's spoken register is not this list's register. Of his 2.0M spoken words only 1.0M are from Portugal, and that million is 0.437M of recorded informal conversation (CORDIAL-SIN, CRPC, Português Falado) plus 0.563M of interviews transcribed in newspapers and magazines, screened for colloquial forms like pra. No broadcast material is described. So most of his Portuguese "spoken" data is interview speech that arrived through print, and recorded conversation from Portugal is about 2% of his whole corpus — while this list is dramatic dialogue end to end. Where the two disagree about a word, that difference in kind is usually the reason, not an error in either. (Introduction, Table 1 and pp. 2, 6–8.)

What this list offers that Davies does not is scale on a single register and a strictly European-Portuguese slant: 30× more tokens, all of it dialogue, with Brazilian markers filtered out. That makes it a decent complement to Davies for a learner targeting spoken EP. It is not a replacement, and it has not been edited the way a published dictionary is.

Known technical caveats

These are specific and worth knowing before you study from the list.

• Part of speech is tagger output, not hand-checked. The pos column is Stanza's tag, by majority vote over each word's sample sentences (50 for the 4,350 most frequent word forms, 5 for the rest). The tagger is not always consistent with itself: 3% of its tags contradicted the lemma it gave in the same sentence (a VERB tag on the noun pergunta) and were discarded. A word the tagger regularly uses two ways gets one row per part of speech, with its count divided between them: a (article and preposition), que (conjunction and pronoun), este (determiner and pronoun), morto (adjective and noun). The tags follow Universal Dependencies conventions, with one exception made for learners: a preposition is never counted as a conjunction (UD tags para in para fazer as one). Entries the tagger never saw intact keep a rule-based guess, except the contractions, which are all published as det (see below).
• The glosses are a model snapshot, not a reference work. Every gloss and translation in out/glosses.tsv was written by Claude (claude-opus-5) in a single pass, one request per entry, from the entry, its part of speech, its frequency and up to twenty real corpus sentences containing it. The prompt is in the repository (scripts/gloss_prompt.md) because the glosses cannot be read as evidence without it. A native speaker checked the top 500; nobody checked the other 9,500. Two consequences worth knowing: the model chose which sense to put first, so the order reflects its judgement of spoken frequency rather than a count; and re-running the same prompt on a newer model would not give the same answers, so unlike the lists themselves the glosses are not reproducible byte for byte. The example sentences are the one part that is verifiable — each is a corpus line, copied unaltered but for a leading dialogue dash and surrounding quotation marks, and the build rejects any the model did not copy exactly. Where the model edited a sentence rather than quoting it (22 entries), the example was dropped and the entry keeps its gloss alone; 192 entries have no example because none of the sampled lines showed the entry in the sense being glossed.
• Contractions are labelled by what they contract, not by the tagger. Stanza expands do, nas, pelo and the rest into two words before tagging, so the surface token collects almost no usable evidence: before this release nas, no and 26 others read unk, and the ones with a stray tag read worse — pelo as a noun, deste as a verb, contigo split across three parts of speech. Each form now gets one decided answer: preposition + article or demonstrative is det (do, nas, pelo, num, deste, naquela — 58 forms), preposition + pronoun is pron (dele, comigo, disso, nisto, daquilo — 19), and preposition + adverb of place is adv (daqui, daí, dali). This is a decision, not tagger output, and it is the one part of the pos column that does not come from Stanza.
• ser and ir are separated by a rule, not by the tagger. foi, fui, fomos, foram and fora belong to ser ("was") or ir ("went") depending on the sentence, and the tagger assigns them to ser almost every time (fui ao Paquistão included). So the tagger only decides whether an occurrence is a verb at all — the adverb fora, "outside", is not — and a rule reading the neighbouring words picks the verb: ir before a, ao, para, embora or an infinitive, or after lá; ser before a participle, an adjective, a noun phrase or nothing. On the 100 lines used to develop it, it is right on 95.9% of the verb uses it decides; on a held-out 100 lines labeled afterwards by a native-speaker Portuguese teacher, 93.2% (68 of 73), abstaining on 8%. An abstention takes the form's own ser/ir ratio. Every one of those five held-out errors calls an ir use ser (já fomos, já foram todos?), so ir is still undercounted a little. The split is estimated from 1,000 random lines per form, so each form's share is good to about ±3 percentage points. It misses idioms (ele não foi nessa) and elliptical questions (sempre foram?). Ser counts 22.3M tokens and ir 9.0M.
• Lemmatization is not perfect. 98.5% on the hand-checked sample means something like 150 of the 10,000 entries may carry a lemma error. The known pattern: Stanza sometimes reads a verb form as a noun (procura, mentes, sacas kept as nouns) or picks the wrong verb (vejam as vir, not ver).
• Split counts are estimates. Participles (educados: the verb educar or the adjective educado) and words used as two parts of speech have their counts divided according to how the tagger reads a sample of sentences: 50 for word forms with 10,000+ occurrences, which gives 2% steps, and 5 for the rest (20% steps).
• Six known duplicates remain. Missing-accent forms are folded into their accented twin only when the accented form is at least 10× as frequent. Six pairs fall below that and appear twice: camera/câmera, frigorifico/frigorífico, amen/ámen, bla/blá, mafia/máfia, karate/karaté. They are accepted as known typo pairs rather than hidden. Treat the unaccented one as a typo of the other.
• Folding by frequency can over-merge. The same 10× rule folds a rare unaccented form into a common accented one even when the rare form is a real word. Every fold is listed in reports/QUALITY.md, with the borderline ones (10–20×) in their own section.
• Proper nouns are removed by capitalization, and some will slip through. A word is treated as a name if it is capitalized away from the start of a line almost every time. Dictionary words that the filter removed at 5,000+ occurrences were reviewed one by one — deus, sr, natal and 26 others were restored — but names below that threshold were not reviewed. Everything removed with 100+ occurrences is listed in out/dropped_proper_nouns.txt.
• English words are only partly filtered. English plurals and many English names are removed; other English words that turn up in Portuguese dialogue (ok, zombie) are kept, deliberately in the case of ok.
• MWE counts are an overlay, not a replacement. An MWE's raw_freq is the bigram count, and it is not subtracted from its constituent words' lemma counts. This is the usual frequency-list convention, but it means the column does not sum to the corpus.

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What's in out/

Two rank bands — 1–5000 and 5001–10000 — each published in four formats. Files without a number in the name are the first band.

The frequency lists

File	What it is
ep_spoken_top5000.tsv / .csv	Ranks 1–5000. The main list.
ep_spoken_5001_10000.tsv / .csv	Ranks 5001–10000.

TSV and CSV hold identical data; pick whichever your tools prefer. Columns:

Column	Meaning
rank	1-based rank by raw_freq.
lemma	Lemma form, or the surface multi-word expression for MWE entries.
is_mwe	1 for a multi-word expression, 0 otherwise.
pos	Part of speech from the tagger. One of det, prep, conj, pron, adv, intj, num, noun, adj, verb, mwe, unk. A word used as two parts of speech has one row for each. See caveats.
raw_freq	Lemma occurrence count across the full corpus.
freq_per_million	Occurrences per million tokens (of 636,582,535).

The Anki decks

File	What it is
EP_Spoken_Frequency.apkg	Ready to import: twenty decks of 500 words, glossed throughout, with the note type and the card templates in one package. See Importing the Anki decks.
ep_spoken_anki_minimal.tsv	Rank, Lemma_PT, POS, Is_MWE, Raw_Freq, Freq_per_million, Tags
ep_spoken_anki_enrichable.tsv	The same, plus Gloss_EN, Example_PT, Example_EN and Gloss_Source, all filled in
ep_spoken_5001_10000_anki_minimal.tsv	Ranks 5001–10000, minimal fields
ep_spoken_5001_10000_anki_enrichable.tsv	Ranks 5001–10000, enrichable fields

The glosses

File	What it is
glosses.tsv	One row per entry: gloss (one to three English senses, commonest first), example_pt (a corpus line containing the entry, verbatim), example_en (its translation), flags, source.

flags is zero or more of vulgar, bp-leaning (the form or sense is mainly Brazilian), archaic, name-like (in this corpus the token is probably mostly a name) and uncertain (the model was not confident). Slang and vulgarities are glossed plainly: this is a record of how people speak, not a teaching aid. See the caveat below.

source says where the row came from, and the deck publishes it as Gloss_Source. It reads model for 9,972 rows. The other 28 are in eval/gloss_overrides.tsv, where a reviewer overruled the model:

source	Rows	What a card shows
model	9,972	Claude; example taken verbatim from the corpus
override-chosen	11	corpus line chosen by a native speaker
override-edited	10	corpus line, edited by a native speaker
override-english	4	corpus line; English corrected by a native speaker
override	3	gloss settled by hand

The three kinds differ in what the reviewer touched. override-chosen is a different corpus line she picked from the ones the model was shown, so it is still verbatim. override-edited is a sentence she corrected and therefore no longer verbatim — trás, for instance, where she restored an accent the subtitle had dropped. Her eleventh such sentence was for dever, which had no example at all until she wrote one; it is held back rather than published, because the entry is now a verb and her sentence is the noun. override-english leaves the Portuguese alone and takes only her correction to the translation. override covers the three glosses settled by hand: caseiro, which the model refuses to gloss, largo, and de, whose senses she extended.

Of the English translations on those rows, 5 are hers, 12 were written here and she confirmed them, 5 she corrected, and 3 were written here to match sentences she supplied in the second round and she has not seen them yet. Each row records which in its note.

Quality-control files

File	What it is
qc_sample.csv	A spot-check sample across the first band — a quick way to eyeball output quality without opening a 5,000-row file.
dropped_proper_nouns.txt	Words removed as proper nouns (with 100+ occurrences), plus the BP exclusions, English plurals and fragments removed by the other filters.

The build reports are in reports/: COMPARISON.md (this list against the unpublished April 2026 version), QUALITY.md (the quality gate and every accent fold), and backend_scores.md (how the lemmatizers compared).

Release assets

Everything above is in the repository, so cloning gets you all of it. A tagged release also attaches four files on their own, for anyone who wants the data without the repository:

Asset	What it is
EP_Spoken_Frequency.apkg	The Anki package.
ep_spoken_top5000.tsv	Ranks 1–5000.
ep_spoken_5001_10000.tsv	Ranks 5001–10000.
glosses.tsv	The glosses and example sentences, all 10,000 rows.

A fifth file, SHA256SUMS.txt, carries their SHA-256 digests. Download the assets and that file into one directory and check them with:

sha256sum -c SHA256SUMS.txt      # macOS: shasum -a 256 -c SHA256SUMS.txt

Each line should read OK. The sums are written by the build rather than by hand, so they describe the files the pipeline actually produced; they are in out/SHA256SUMS.txt in the repository too, and a rebuild that moves any asset moves its line with it. If you are pinning this data in another project, pin the digest and not the tag — a tag can be moved, a digest cannot.

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How the list is built

From pt.txt.gz to out/, in one command (python -m scripts.build):

1. Tokenize and count. A Portuguese-aware tokenizer strips subtitle artifacts and lowercases. Enclitic clusters are split and the verb is restored: fazê-lo counts as fazer + lo, and dar-lhe-ia as daria + lhe.

2. Second pass. Bigram counts for multi-word expressions, sample sentences for every frequent word (50 for the 4,350 word forms with 10,000+ occurrences, 5 for the rest), and capitalization statistics for the proper-noun filter.

3. Tag and lemmatize. Stanza reads the 62,916 word forms frequent enough to reach the list inside their sample sentences — about 499,000 sentences, covering 62,523 of those forms — and gives each occurrence a lemma and a part of speech. The majority lemma wins, after discarding votes where the lemma and the tag contradict each other. When Stanza's answer is not a dictionary word (it occasionally invents forms like agradeçar), simplemma is used instead; simplemma also handles the rare tail. A small table of hand-verified corrections covers errors Stanza makes at high frequency (dói is doer, not dizer).

4. Resolve context-dependent forms per occurrence. Participles are split between the verb and the adjective according to how Stanza tags them in their sample sentences. foi, fui, fomos, foram and fora are split between ser and ir by a separate next-word rule, because the tagger cannot tell them apart (see caveats).

5. Apply the lemmatization conventions in eval/conventions.md:

   1. contractions (do, ao, pela) are their own entries;
   2. adjectives fold to the masculine, but nouns for people keep the feminine (senhora, rapariga), and a feminine with its own meaning always stays (música, sexta) — decided pair by pair in eval/gender_pairs.tsv;
   3. diminutives stay separate (coisinha);
   4. comparatives and irregular superlatives are their own entries (maior, melhor, ótimo);
   5. spelling-reform variants merge under the post-1990 spelling (acção → ação, óptimo → ótimo), while words that keep their consonant in European spelling (facto, contacto) are left alone.

   Pronouns are their own entries too (me, not eu).

6. Fold remaining duplicates. Each lemma is checked against the lemmatizer again, and regular plurals are folded onto their singular (guarded: óculos, férias and cais are not plurals of anything). An unaccented form folds into its accented twin when the accented form is at least 10× as frequent, and wrong or Brazilian accents (näo, prêmio) fold into the European spelling.

7. Filter. BP-leaning words are removed from the exclusion list — aeromoça, bacana, banheiro, celular, galera, geladeira, legal, mano, massa, né, oi, trem, você, vocês, xícara, ônibus, with their unaccented variants. Proper nouns are removed by capitalization, subject to the reviewed decisions in eval/proper_noun_drops_review.tsv. English plurals are removed, and so are one- and two-letter fragments that are neither words nor interjections.

8. Assign part of speech. Each word's count is divided across the parts of speech the tagger gave it, the same way participle counts are divided across lemmas. A second part of speech becomes its own row only if it holds at least 25% of the word's count and at least 3 tagged sentences; otherwise it joins the majority.

9. Rank and emit the top 10,000 rows by count, with multi-word expressions — kept by log-likelihood (Dunning G² ≥ 2500) and a collocation share of at least 5% — ranked alongside them. 296 of them make the top 10,000: 191 in the first band and 105 in the second.

How it is checked

• A human-reviewed gold set. eval/lemma_gold.tsv is 400 words sampled from the corpus across the frequency range, each checked by hand. 332 are scored; 68 were marked as names, foreign words or junk. The published lemmas are correct for 98.5% of them.
• A quality gate. Every build looks for entries that are probably the same word counted twice — a form that lemmatizes to another entry, an accent variant of another entry, a regular plural of another entry — and fails if it finds any that are not explicitly accepted. This release passes. Two kinds of pair are accepted in config.yaml, each listed: words that are genuinely different (avô/avó, pôr/por, cirurgiã/cirurgia), and the six known typo pairs from the caveats, which sit below the 10× folding threshold. Any new suspect fails the build.
• Two labeled ser/ir samples, 100 corpus lines each, 20 for each of foi, fui, fomos, foram and fora. eval/ser_ir_sample.tsv was used to develop the rule: its labels were proposed by an AI model acting as a Portuguese informant and checked by hand, and the rule scores 95.9% on the verb uses it decides (71 of 74). eval/ser_ir_sample2.tsv is held out: labeled afterwards by a native-speaker Portuguese teacher, never used to shape the rule, and therefore the honest estimate — 93.2% (68 of 73), abstaining on 8%. Stanza's verb test keeps the adverb fora away from the rule in 19 of 20 held-out cases; the exception is lá fora, which it mistagged as a verb. The build uses the rule only while a score of at least 95% on the development set exists for its current version; both tables are in reports/serir_scores.md.
• Automatic gates on the glosses. Every gloss run checks that each published row got a gloss, that each example sentence is one of the corpus lines the model was shown — character for character — and that the sentence really contains the entry; a run that fails any of those is not published. Counts, per-flag totals and what the run cost are in reports/gloss_gates.md. The glosses' meaning is not gated: a sample is set aside for a reader in eval/gloss_review.tsv.
• Determinism. The same corpus, configuration and library versions produce byte-identical output. The glosses are the exception — see the caveat.

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Reproducing this

You need Python 3.13 and several GB of free disk (the corpus, Stanza's models and PyTorch). The first build takes about an hour on an 8-core machine; later builds reuse cached passes and take a minute.

git clone https://github.com/professorcdp/EPspokenfrequency.git
cd EPspokenfrequency
python3 -m venv .venv
./.venv/bin/pip install -r requirements.txt
./.venv/bin/python -c "import stanza; stanza.download('pt')"

Download the corpus (1.1 GB) into data/ as described in DATA_SOURCES.md, then:

./.venv/bin/python -m scripts.build

This writes out/ and reports/ and exits with status 0. If you change the configuration and the build exits with status 1, the quality gate has found a new suspected duplicate: it is listed in reports/QUALITY.md, and every output has still been written.

requirements.txt pins the exact versions used for this release. With them the build reproduces out/ byte for byte; a different Stanza or simplemma release can change individual lemmas. To rebuild the April-style list for comparison, run python -m scripts.build --stage 1 (output in build/stage1/). scripts/README.md covers the other options.

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Importing the Anki decks

Use out/EP_Spoken_Frequency.apkg. In Anki, choose File → Import, select it, and you are done: the note type, the card templates and both decks come with it.

• Twenty decks of 500 words each, under one parent and numbered so they sort in rank order: Portuguese::European Portuguese - Spoken::01 · 1–500, ::02 · 501–1000, and so on to ::20 · 9501–10000. Study them in order and you meet the commonest words first.
• One note type, Portuguese (EP) – Spoken Frequency, with ten fields: Rank, Lemma_PT, POS, Gloss_EN, Example_PT, Example_EN, Is_MWE, Raw_Freq, Freq_per_million and Gloss_Source.
• Card 1 (Portuguese → English) shows the word large, with its part of speech beneath it. The back gives the English gloss, the example sentence and its translation, then the rank, the frequency and Gloss_Source in small print.
• Card 2 (English → Portuguese) shows the gloss and asks for the word. Every note has one, because every note is glossed. (Its front is still wrapped in a condition on Gloss_EN, which is what let the 1.0 deck ship with no reverse cards at all.)
• Gloss_Source says where each card's gloss came from — for 9,972 of them that the example is a corpus line the model quoted, and for 29 that a native speaker chose the sentence, edited it, corrected the English, or that the gloss was settled by hand.
• Every note carries the tags below, the same as in the TSV files.

There is no field for your own notes, and that is deliberate. Anki cannot merge fields when it imports: Always overwrites every field of every note it matches, and If newer overwrites every field of each note it updates. So any field this package shipped could be wiped by a later release, along with whatever you had written in it. A place to write that we would overwrite is worse than no place.

To keep your own notes, add a field yourself: Tools → Manage Note Types → Fields → Add. Know the trade before you do. Once the note type differs from the one in the package, Anki stops updating these notes altogether — a later release will add words that are new, but your existing notes keep the glosses and ranks they already have.

The ten fields are frozen from 1.1.0. Anki matches fields by position, so adding, removing or reordering one would break re-import for everyone already studying. Anything the deck needs later will go in a separate note type.

Importing a later release

If you have the 1.0 deck, delete it first — all twenty decks — or import 1.1 into a fresh Anki profile. The note type has changed (1.0 had no Gloss_Source) and 70 entries changed their part of speech, which changes how Anki identifies them. Importing 1.1 over 1.0 does not fail; it quietly does almost nothing. On a test import it refused 9,880 notes and added 120, which is Anki declining to update notes across a changed note type.

Deleting the 1.0 decks loses whatever review history you have on them — the scheduling, the intervals, the lapses. There is no way to carry it across, and 1.0 shipped with no glosses, so what you would be keeping is history on a deck with no English side.

From 1.1.0 onward, re-importing works as you would expect. Notes are identified by their word and part of speech, so a new release matches the notes you have instead of adding duplicates, and your review history is kept. Anki's import screen asks whether to Update notes:

Option	What happens
If newer	Brings in the new release's glosses, ranks and counts. This is the one to use.
Always	The same, unconditionally.
Never	Nothing is updated; new words are still added.

If newer actually works now: each release carries its own timestamp, where 1.0.0's was frozen, so Anki could never see a newer copy and If newer did nothing at all.

Updating replaces every field of each note it touches. Nothing in the shipped fields is yours to lose — but if you have added a field of your own, see the warning above: Anki will not update those notes at all.

Advanced: the TSV files, for your own note type

If you want your own fields or card design, import the *_anki_*.tsv files instead. They carry Anki's header directives, so Anki configures most of the import itself: separator, note type, target deck, field mapping and tags column are all declared in the file.

1. Create the note type first; Anki will not invent it for you. Go to Tools → Manage Note Types → Add, name it Portuguese (EP) – Spoken Frequency, and give it fields matching the file you plan to import:

   • minimal: Rank, Lemma_PT, POS, Is_MWE, Raw_Freq, Freq_per_million
   • enrichable: Rank, Lemma_PT, POS, Gloss_EN, Example_PT, Example_EN, Is_MWE, Raw_Freq, Freq_per_million, Gloss_Source

   (The Tags column is not a field; Anki maps it to note tags automatically.) Then design your own card templates.

2. Choose File → Import and select one of the out/*_anki_*.tsv files.

3. The import screen pre-fills from the file's headers. Check that the Field separator is Tab, the Notetype is Portuguese (EP) – Spoken Frequency, the Deck is Portuguese::European Portuguese - Spoken - First 5000 (or ... - 5001 to 10000), and Allow HTML in fields is off.

4. Click Import.

Do not mix the two paths in one Anki profile: the TSV import creates its own notes, separate from the .apkg's, so you would study every word twice.

Which file should I import?

The .apkg, unless you have a reason not to. It arrives glossed, with both cards working from the first import and nothing to fill in.

On the advanced path, take ep_spoken_anki_enrichable.tsv, which ships with the same glosses, examples and Gloss_Source filled in, or the minimal file if you would rather write your own glosses from the bare ranked list. The TSV files hold 5,000 words each, so you will want to slice them by tag (below) as you go.

Studying in frequency order

The twenty decks are the order. Work through 01 · 1–500 before 02 · 501–1000, and you learn the words roughly in the order you will hear them. The numbers keep the decks in order in Anki's deck list, and each deck has its own daily limits, so a deck you have not started sends you no new cards.

Every note also carries tags, for slicing more finely than a deck — all the verbs in the first thousand, say — from the Anki browser:

Tag	Meaning
freq::0001-0500, freq::0501-1000, …	500-word band (matches the decks)
freq500::…	500-word band (alias)
freq1000::0001-1000, …	1000-word band
pos::det, pos::verb, …	Part of speech
source::opensubtitles_ep_v2018	Provenance
variety::EP, register::spoken	Variety and register
flag::vulgar (89), flag::bp-leaning (115), flag::name-like (148), flag::uncertain (318), flag::archaic (13)	What the gloss was flagged as. Suspend flag::vulgar if you would rather not meet those, or search flag::uncertain for the glosses to check first
gloss::reviewed (500)	A native-speaker teacher passed a verdict on this entry's gloss

The tags are on the TSV notes too, and are the only way to study those in order, since each TSV file imports into a single 5,000-note deck.

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Repository layout

out/                   Published frequency lists and Anki decks
reports/               Build reports: comparison, quality gate, backend scores
archive/out_april_2026/  The unpublished April 2026 list, kept for comparison
eval/                  Gold set, conventions, and the hand-reviewed decision files
scripts/               The pipeline (python -m scripts.build)
tests/                 Unit tests (pytest; no corpus needed)
config.yaml            Every threshold, list and switch the pipeline uses
data/                  Corpus download target — gitignored, never committed
DATA_SOURCES.md        Corpus citation and download instructions
CHANGELOG.md           What changed between versions

data/ is excluded from version control because the corpus is a 1.1 GB file that has no business in git history. Download it yourself following DATA_SOURCES.md.

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Licence and citation

The data and the code are licensed separately:

• The data — the lists and Anki files in out/, and eval/, reports/ and archive/ — is CC BY-SA 4.0 (LICENSE-DATA), and so is the documentation: this README, CHANGELOG.md and DATA_SOURCES.md. Use it, change it, redistribute it: keep the attribution and share derivatives alike.
• The code — scripts/ (with its data tables), tests/, config.yaml and requirements.txt — is MIT (LICENSE), so you can reuse the pipeline in any project, keeping the copyright notice.

To attribute the data: European Portuguese Spoken Frequency List, by Jim Ranalli (Professor Caloiro), licensed under CC BY-SA 4.0. Derived from OPUS OpenSubtitles v2018 (Lison & Tiedemann 2016).

If you publish anything based on this, please cite the underlying corpus:

Lison, P., & Tiedemann, J. (2016). OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. LREC 2016.

Full citations, BibTeX and upstream terms are in DATA_SOURCES.md.