Metadata-Version: 2.4
Name: nltk-punkt-tokenize
Version: 1.0.0
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 **cannot be made to execute code by a model file**.

**Zero dependencies. Standard library only.** 19 pretrained models ship inside
the wheel; nothing is downloaded, ever.

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.']
```

---

## 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.
See [Security](#security).

## What's different

**Models are data, not programs.** A Punkt model is four containers of strings
and integers. The bundled ones are compiled into the package as Python
literals, so loading one is an `import` — no file to find, no format to parse,
no initialisation. Everything else is read by a hand-written bounded parser
that has no way to construct a class or call a function.

**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")
punkt.write_model(params, "danish.punkt.gz")
```

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'}
```

### 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)
```

### 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.punkt.gz -l english
punkt convert english.pickle english.punkt.gz
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 |

Existing `.pickle` models convert without being unpickled:

```bash
punkt convert ~/nltk_data/tokenizers/punkt/english.pickle english.punkt.gz
```

`punkt_tab` directories are read directly, and `write_punkt_tab()` produces
directories NLTK can load.

### 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

### Threat model

A model file is untrusted input. It may come from a package index, a
colleague, a CI cache, or an attacker.

| Attack | Defence |
|---|---|
| Code execution via `pickle` | No loader calls `pickle.load`. Legacy 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 by
`punkt.model.safety.prescan_json`, before the parser is invoked at all —
because anything checked after `json.loads` returns is checked too late.

Limits are configurable:

```python
punkt.read_model("untrusted.punkt", limits=punkt.LoadLimits(max_bytes=8 << 20))
```

### The one place code runs

Bundled models are Python modules, and importing a module executes it. That is
the same 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.

Models from anywhere else go through the parsers, which cannot execute
anything. What made CVE-2024-39705 a vulnerability was that NLTK's models fell
on the wrong side of that line: downloaded at run time, then unpickled.

Two tests enforce this. One walks the package AST asserting no unpickling call
exists. The other walks the generated data modules asserting they contain
nothing but assignments.

## 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.

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

Models are looked up in order: an explicit path, then `$PUNKT_MODEL_PATH`, then
`~/.punkt/models`, then the bundled modules — so a locally trained
`english.punkt` shadows the bundled one with no code change.

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.

## 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
pip install -e ".[dev]"
pytest
pytest -m nltk        # parity tests, requires nltk installed
ruff check src tests
mypy
```

The test suite never imports NLTK. Parity is asserted against a recorded
snapshot of NLTK's output in `tests/data/parity.json`, which is both
cycle-free and pinned, so a change in an installed NLTK cannot silently move
the goalposts.

Two maintainer tools exist, neither shipped in the distribution:

```bash
# Rebuild the bundled models from NLTK data.
python tools/build_data.py ~/nltk_data/tokenizers/punkt_tab

# Re-baseline the parity snapshot. The only file in the repo that imports NLTK.
pip install nltk && python tools/generate_parity_fixtures.py
```

## 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).
