Metadata-Version: 2.4
Name: nltk-punkt-tokenize
Version: 2.0.0
Summary: A modern, dependency-free, pickle-free implementation of the Punkt unsupervised sentence boundary detector (Kiss & Strunk, 2006), with 19 pretrained models included.
Project-URL: Homepage, https://github.com/alvations/nltk_punkt
Project-URL: Source, https://github.com/alvations/nltk_punkt
Project-URL: Issues, https://github.com/alvations/nltk_punkt/issues
Project-URL: Changelog, https://github.com/alvations/nltk_punkt/blob/main/CHANGELOG.md
Author-email: alvations <alvations@gmail.com>
Maintainer-email: alvations <alvations@gmail.com>
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Keywords: kiss-strunk,nlp,nltk,punkt,sentence-boundary-detection,sentence-splitter,sentence-tokenizer,tokenizer
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Description-Content-Type: text/markdown

# nltk-punkt-tokenize

A modern implementation of the Punkt unsupervised sentence boundary detector
(Kiss & Strunk, 2006) that **does not read model files at all**.

**Zero dependencies. Standard library only.** Pretrained models for 19
languages ship inside the wheel as compiled Python modules; nothing is
downloaded, ever, and nothing is parsed.

The dependency direction is deliberate: this package is intended for NLTK to
depend on, so that Punkt can be vendored out of NLTK. Nothing here imports NLTK
— not the library, not the tests, not the CI.

```bash
pip install nltk-punkt-tokenize
```

```python
import punkt

punkt.sent_tokenize("Dr. Smith went home. He was tired.")
# ['Dr. Smith went home.', 'He was tired.']

punkt.sent_tokenize("Das ist z.B. ein Satz. Und noch einer.", "german")
# ['Das ist z.B. ein Satz.', 'Und noch einer.']

punkt.sent_tokenize("这是一支笔。那是一本书。", "chinese")
# ['这是一支笔。', '那是一本书。']
```

On [WMT24++](https://arxiv.org/abs/2502.12404), **21 of 21 languages find every
segment boundary — 0 missed, 100.00% recall** across Latin, Cyrillic, Greek,
Chinese, Japanese and Korean. See [Accuracy](#accuracy).

---

## Why this exists

Punkt is a good algorithm with a bad delivery mechanism. NLTK's models were
distributed as Python pickles fetched at run time, which is
[CVE-2024-39705](https://nvd.nist.gov/vuln/detail/CVE-2024-39705): the pickle
grammar contains an opcode meaning *"import this module, look up this name, and
call it with these arguments"*, so loading a model is arbitrary code execution.
No amount of care in the calling code changes that, because the file chooses
what gets called.

The usual reflex — swap `pickle` for `json` and declare victory — does not
finish the job. JSON cannot name a class, so it is not a code-execution
primitive; but `json.loads` builds the entire object graph *before* any
validation code runs, which leaves the whole denial-of-service family intact.

The conclusion this package eventually reached is that the safest parser is the
one that is not there. A Punkt model is four containers of strings and integers,
which Python can already express, so the models are compiled into the package as
Python literals and the library ships **no model reader of any kind**. Reading
files is a separate job for separate code you run deliberately — see
[`tools/`](tools/README.md) — and not something an installed library does on
your behalf. See [Security](#security).

## What's different

**Models are data, not programs.** A Punkt model is four containers of strings
and integers. Every model the package can load is compiled into it as Python
literals, so loading one is an `import` — no file to find, no format to parse,
no initialisation.

**The package contains no model parser at all.** Not for pickles, not for JSON,
not for its own format. There is nothing for a model file to attack, because
there is nothing that reads one. The readers for the four file formats a Punkt
model has historically been stored in live in [`tools/`](tools/README.md) and
are deliberately not shipped: they exist to *produce* the compiled modules, and
to bring in a model you already have.

**The layers are separated.** NLTK's `punkt.py` is one 1,800-line module where
the language rules, the learner, and the tokenizer share mutable state through a
common base class. Here:

| Layer | Holds | Depends on |
|---|---|---|
| `punkt.rules` | Language conventions, regexes, the token object | nothing |
| `punkt.model` | Learned parameters as data, plus every codec | `rules` |
| `punkt.annotate` | The Kiss & Strunk decision procedure | `rules`, `model` |
| `punkt.training` | The learner. Writes models, never tokenizes | the above |
| `punkt.inference` | The tokenizer. Reads models, never trains | the above |

The trainer has no `tokenize()`. The tokenizer has no `train()`. Hyperparameters
live in a frozen `TrainerConfig` rather than as class attributes you have to
monkey-patch.

**It matches NLTK exactly.** NLTK's output was captured once, from identical
parameters, into `tests/data/parity.json`. The suite asserts against that
snapshot: equal sentences and spans over 400 generated documents, equal trained
models, and bit-exact log-likelihood scores. Because the reference is recorded
rather than imported, parity is checked on every machine and in every CI job —
and NLTK never has to be installed to check it. Four divergences are deliberate
and asserted as such, so they cannot quietly become five.

## Usage

### Splitting

```python
tokenizer = punkt.PunktSentenceTokenizer(punkt.load_model("english"))

tokenizer.tokenize(text)                          # list[str]
list(tokenizer.span_tokenize(text))               # [(start, end), ...]
tokenizer.tokenize(text, realign_boundaries=False)
```

Whitespace inside a sentence is preserved exactly, including newlines. Only
whitespace *between* sentences is dropped.

### Training

Punkt learns from unannotated text — no labelled sentence boundaries required.

```python
params = punkt.train(corpus_text, language="danish")
```

A trained model is a value you can use immediately. To keep one, render it as a
Python module — the same representation the bundled models use, so there is no
second format that only your models are stored in:

```python
Path("danish.py").write_text(punkt.render_model(params, "danish"))
```

```python
from danish import MODEL
punkt.PunktSentenceTokenizer(MODEL)
```

Rendering only ever writes literals, and verifies its own output by parsing it
and rejecting anything that is not data — see
[Security](#rendering-a-model-as-python).

Incrementally, for a corpus too large to hold in memory:

```python
trainer = punkt.PunktTrainer()
for chunk in chunks:
    trainer.train(chunk, finalize=False)
params = trainer.get_params()
```

Tuning:

```python
config = punkt.TrainerConfig(abbrev_threshold=0.5, include_all_collocations=True)
params = punkt.train(corpus_text, config=config)
```

### Understanding a decision

```python
for decision in tokenizer.debug_decisions("Dr. Smith went home."):
    print(punkt.format_decision(decision))
```

```
Text: 'Dr. Smith' (at offset 2)
Sentence break? False (default decision)
Collocation? False
'dr.':
    known abbreviation: True
    is initial: False
'smith':
    known sentence starter: False
    orthographic heuristic suggests is a sentence starter? unknown
    orthographic contexts in training: {'MID-UC', 'UNK-UC'}
```

### Documents with headlines and captions

Punkt keys entirely off punctuation, so a line that ends a sentence by *layout*
rather than with a period is invisible to it. On WMT24++ that is where nearly
every missed boundary came from:

```python
punkt.sent_tokenize(text, line_breaks="always")     # any newline ends a sentence
punkt.sent_tokenize(text, line_breaks="paragraph")  # a blank line does
```

The default is `"ignore"` — correct for hard-wrapped prose, where newlines fall
mid-sentence. Pick by how your text is formatted.

### Abbreviations the model never saw

The bundled English model was trained on the Wall Street Journal, so it knows
`Dr.` and `Corp.` but not `e.g.` or `i.e.` — the source of the most-reported
Punkt failures (nltk#2376, #2154, #3370, all open).

```python
punkt.sent_tokenize("Use a hammer, e.g. a claw hammer. Then hit the nail.", prefixes=True)
# ['Use a hammer, e.g. a claw hammer.', 'Then hit the nail.']
```

This is opt-in, because every abbreviation added is a boundary that can no
longer be found: `"Add water, sugar, etc. Then stir."` stops splitting. Good
trade for technical prose, bad for narrative — measure on your own text.

`etc`, `al` and `dept` are held back even from that list, because each one
genuinely ends sentences as often as not. They become placeable once the model
can judge the *following* word:

```python
params = punkt.with_nonbreaking_prefixes(punkt.load_model("english_web"),
                                         "english", ambiguous=True)
tok = punkt.PunktSentenceTokenizer(params)
tok.tokenize("Cats, dogs, etc. are common pets.")   # 1 sentence
tok.tokenize("Add water, sugar, etc. Then stir.")   # 2 sentences
```

On the bundled `english` model the same flag is a pure trade and gains nothing;
on `english_web` it takes those six cases from 3/6 to 5/6.

A subset is safe unconditionally. Moses marks some prefixes as non-breaking
*only before a number*, which a flat list cannot express:

```python
punkt.sent_tokenize("See No. 5 on the list. It is important.")
# ['See No.', '5 on the list.', 'It is important.']   <- wrong

params = punkt.with_nonbreaking_prefixes(punkt.load_model("english"), "english",
                                         include_always=False)
punkt.PunktSentenceTokenizer(params).tokenize("See No. 5 on the list. It is important.")
# ['See No. 5 on the list.', 'It is important.']      <- and "There is no. Not at all." still splits
```

### A new language

Language rules are an immutable value, not a subclass:

```python
greek = punkt.LanguageVars(sent_end_chars=(".", ";", "·", "!"))
tokenizer = punkt.PunktSentenceTokenizer(params, rules=greek)
```

Ready-made rule sets ship for the languages the defaults do not fit:

```python
from punkt.rules.presets import CHINESE, JAPANESE, KOREAN, GREEK, THAI
```

Chinese and Japanese need them structurally, not cosmetically: those scripts put
no space between sentences, so the default lookahead finds *zero* boundary
candidates and returns the whole text as one sentence. Tan & Bond (2011)
abandoned Punkt over exactly this when building the NTU-MC corpus.

## Accuracy

Measured with `tools/eval_wmt24pp.py` against WMT24++ segment boundaries, using
`line_breaks="always"` since those documents are newline-separated paragraphs:

| | recall | missed boundaries |
|---|---|---|
| 21 languages, this release | **100.00%** | **0** of 790 each |
| punctuation only (`line_breaks="ignore"`) | 65–78% | 172–274 each |

Only recall is reported. A WMT24++ segment is a translation segment, not a
sentence — roughly half the English ones hold more than one — so a split *inside*
a segment is usually a correct sentence break that the segmentation did not
record. Counting those against a splitter would punish it for being right.
Missing a segment boundary, on the other hand, is unambiguously wrong.

### Command line

```bash
punkt tokenize book.txt                 # one sentence per line
punkt tokenize -m german artikel.txt
punkt spans book.txt                    # start, end, sentence
cat corpus.txt | punkt train - -o mine.py -l english
punkt info english --show
punkt explain "Dr. Smith went home."
punkt languages
```

## Migrating from NLTK

| NLTK | here |
|---|---|
| `nltk.sent_tokenize(text)` | `punkt.sent_tokenize(text)` |
| `nltk.download("punkt")` | not needed — models are bundled |
| `PunktSentenceTokenizer(train_text)` | `PunktSentenceTokenizer(punkt.train(train_text))` |
| `PunktTrainer.ABBREV = 0.5` | `TrainerConfig(abbrev_threshold=0.5)` |
| `tokenizer._params` | `tokenizer.params` |
| `PunktLanguageVars` subclass | `LanguageVars(...)` value |

### Models you already have in nltk_data

The 19 stock languages are bundled, so you need nothing. For a model you
trained yourself, or a `punkt_tab` directory you want to bring across, clone
this repository and use the converter — it is not part of the installed
package, by design:

```bash
python tools/convert_model.py --list                     # what nltk_data has
python tools/convert_model.py ~/nltk_data/tokenizers/punkt_tab/portuguese \
    -o portuguese.py
```

```python
from portuguese import MODEL
punkt.PunktSentenceTokenizer(MODEL)
```

`.pickle` inputs are parsed, never unpickled. `--all` converts a whole
directory, and `--format punkt_tab` writes a directory NLTK can load. See
[tools/README.md](tools/README.md).

### Deliberate differences from NLTK 3.8.1

- **Unicode quotes.** Curly quotes and guillemets are treated as closing
  punctuation, so `“Hello there.” Bye.` realigns correctly. NLTK added this
  after 3.8.1 (gh-1682); this package follows the newer behaviour.
- **Abbreviation smoothing.** The abbreviation log-likelihood adds `1e-8` to
  its null probability, matching NLTK's development branch. Without it a corpus
  containing no period-final tokens raises `ValueError` from `log(0)`.
- **No mutation on read.** NLTK stores `ortho_context` in a `defaultdict(int)`
  and reads it with `[]`, so tokenizing silently grows the model with a zero
  entry per unknown word. Reads here do not mutate.
- **Real booleans.** `is_initial` and friends return `bool`, not a truthy
  `re.Match`. The orthographic heuristic returns a three-valued enum instead of
  `True`/`False`/`"unknown"` in one variable.

### Why the name

`nltk-punkt` on PyPI is an unrelated 1.2 KB package that downloads NLTK's punkt
data — the very thing this replaces — so the distribution is
`nltk-punkt-tokenize`. The import name is just `punkt`.

## Security

### The shape of the thing

The package does not read model files. Every model it can load is a Python
module inside the wheel, so `punkt.load_model` is an `import`: there is no path
to resolve, no bytes to parse, no format to confuse, and no limit to exceed.
Whole categories of attack are absent rather than defended against.

There is also no search path. Earlier releases consulted `$PUNKT_MODEL_PATH`
and `~/.punkt/models` before the compiled-in data, which let a file dropped in
the right directory decide what your program tokenized with. Names now resolve
to bundled modules and nothing else.

### The one place code runs

Importing a module executes it. That is the trust boundary every Python package
already has — you trust the code you installed — and it is why the bundled
route is used *only* for models inside the wheel, covered by the wheel's own
hash and reviewed at release. What made CVE-2024-39705 a vulnerability was not
that NLTK's models were code; it was that they were *downloaded at run time*
and then unpickled. Nothing here downloads anything.

Two tests enforce it: one walks the package AST asserting no unpickling call
exists, the other walks the generated data modules asserting they contain
nothing but assignments.

### Rendering a model as Python

`punkt.render_model` writes Python source that you then import, and some of what
it writes can come from a model read out of a file somebody else wrote. That is
a code-execution vector if any value reaches the output unquoted — and one did.
The model *name* was interpolated into the generated module's docstring without
`repr`, so a name containing a triple quote closed the docstring and everything
after it became live code. `tools/convert_model.py` derives that name from the
input file's name, which made a maliciously *named* file arbitrary code
execution at import time.

Three things now stand in the way:

1. The name must be a Python identifier — the right constraint, since it becomes
   a module name, and a complete one.
2. Every value must be a `str` or an `int`, checked before rendering, and each is
   emitted through `repr`.
3. The finished source is parsed and verified: the AST may contain only
   literals, tuple and dict displays, and calls to `frozenset`, and must assign
   exactly the ten expected names. Anything else raises rather than being
   written.

The third is the guarantee that does not rest on the quoting being right.
`tests/test_render.py` attacks all three through the name, the metadata and each
learned container, and asserts a fresh interpreter importing the result has no
side effects.

### Reading other people's files

The converters under [`tools/`](tools/README.md) do parse files, and they carry
the defences that implies. They are not shipped and the library never calls
them; you run them deliberately, on input you chose.

| Attack | Defence |
|---|---|
| Code execution via `pickle` | Nothing calls `pickle.load`. Pickles are parsed with `pickletools.genops` and replayed through a data-only machine; `GLOBAL` pushes an inert marker, `REDUCE` consults a fixed table, `PERSID`/`EXT*` are refused |
| Gzip bomb | Decompressed bytes counted *while* streaming and capped |
| Multi-gigabyte model | Size, line count, line length and per-section entry caps |
| JSON nesting exhausting the parser stack | Bracket depth counted before `json.loads` is called |
| **Quadratic number parsing** | Numeric literal length is bounded. `int(digits)` is O(n²), and Python 3.9/3.10 have no ceiling — a 2 MB run of digits passes both a depth check and a size check, then pins a core for minutes. Measured: >120 s unguarded, 0.09 s guarded |
| **Hash/memory pressure from huge objects** | Total element count is bounded *before* parsing. A post-hoc `len()` cannot help: the dict already exists |
| Unbounded JSON string | String literal length is bounded |
| Schema confusion | Every JSON field is type- and range-checked; unknown keys are refused, not ignored |
| Malformed data loading as a *different* model | Strict UTF-8, no `errors="replace"`; unknown sections rejected; flag bits validated against a mask |
| Callback-driven JSON parsing | `object_hook`, `object_pairs_hook` and `parse_constant` are never passed |

The four JSON bounds are applied to the raw text in a single linear pass before
the parser is invoked at all, because anything checked after `json.loads`
returns is checked too late.

## Performance

Loading a bundled model is an unmarshal of a precompiled `.pyc` rather than a
parse:

| | time |
|---|---|
| Parse a 20,000-entry text model | ~66 ms |
| Import the equivalent compiled module | ~5 ms |

The first import of a language compiles its module to a `.pyc` — 200 ms for
English, 800 ms for Finnish, once per installation. `pip` normally does this at
install time, so it is not usually observed.

Training caches the corpus token total, which NLTK recomputes inside two
per-type loops; this makes training linear in vocabulary size rather than
quadratic, with identical results.

## Models

19 languages, from Kiss & Strunk's original multilingual evaluation plus later
contributions, as redistributed by NLTK: Czech, Danish, Dutch, English,
Estonian, Finnish, French, German, Greek, Italian, Malayalam, Norwegian,
Polish, Portuguese, Russian, Slovene, Spanish, Swedish, Turkish. Plus
`english_web`, described below.

```python
punkt.available_languages()
```

A name resolves to a bundled module and nothing else — there is no search path
and no way for a file on disk to shadow one. To use a model of your own, build
it (`punkt.train`) or convert it (`tools/convert_model.py`) and import it
directly.

The Russian model contains abbreviations only, with no orthographic data. That
is how it is distributed upstream; it will detect fewer boundaries than the
others.

### `english_web`

One extra model, opt-in. The bundled `english` is the Wall Street Journal model,
which knows 39 sentence starters and 20,366 orthographic types. Punkt's second
pass uses exactly those to decide whether an abbreviation *also* ends a
sentence, so with that little evidence it usually answers "unknown".

`english_web` keeps the same abbreviations and adds evidence from 400 MB of
HPLT 2.0 web text: 908 sentence starters, 381,201 orthographic types. On
Universal Dependencies English it takes punctuated F1 from 0.9785 to 0.9838.

```python
tokenizer = punkt.PunktSentenceTokenizer(punkt.load_model("english_web"))
```

`english` is untouched, so parity with NLTK and existing output are unaffected.
Regenerate the model with:

```
python tools/train_statistics.py english --bytes 400000000 --emit english_web
```

The same command works for any of 35 languages. Abbreviations learned from the
crawl are dropped — crawl is good at statistics and bad at abbreviations, and
400 MB of English yielded 1,648 new ones that were almost entirely `$40000`,
`!m` and `3c•`. Curated abbreviations come from `punkt.data.curated` instead.

## Requirements

Python 3.9+. No dependencies, at runtime or otherwise — the package imports
only the standard library, and `pip install nltk-punkt-tokenize` pulls in
nothing else. CI asserts this three ways: over the AST, over the distribution
metadata, and by making `import nltk` raise and then exercising the package.

## Development

```bash
git clone https://github.com/alvations/nltk_punkt
cd nltk_punkt
pip install -e ".[dev]"
pytest                    # 405 tests: the package
pytest tools/tests        # 163 tests: the converters
ruff check src tools tests
mypy src/punkt
```

Two suites, because there are two things. `tests/` covers what the wheel
contains, and must pass with `tools/` absent — which is how it arrives in an
sdist. `tools/tests/` covers the model-file converters, which are published in
no artefact at all and run only from a checkout.

Everything that builds, converts or scores lives in [`tools/`](tools/README.md).
See the tools README for regenerating the bundled data, converting models out of
`nltk_data`, and why writing Python from an untrusted file is safe here.

## References

Kiss, T. & Strunk, J. (2006). Unsupervised Multilingual Sentence Boundary
Detection. *Computational Linguistics*, 32(4), 485–525.

Dunning, T. (1993). Accurate Methods for the Statistics of Surprise and
Coincidence. *Computational Linguistics*, 19(1), 61–74.

## Licence

Apache-2.0. The pretrained models are redistributed from the NLTK project and
were trained by Jan Strunk and Tibor Kiss; see [NOTICE](NOTICE).
