Registry / ai-ml / keyphrase-vectorizers

keyphrase-vectorizers

library0.0.13pypypi✓ verified 84d ago

Set of vectorizers that extract keyphrases with part-of-speech patterns from a collection of text documents and convert them into a document-keyphrase matrix. Current version 0.0.13, requires Python >=3.7 and spaCy. Releases are intermittent.

ai-ml data

pip install keyphrase-vectorizers

github ↗package ↗

INSTALL

IMPORT

SIG · KEYPHRASE-VECTORIZ

keyphrase-vectorizers

ai-mlpythonv0.0.13

Install

79.8s avg

Import

14890ms

Disk

5427MB

Pass rate

1/ 10

Env Coverage1 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.0.13 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✕ build_error

✕ timeout

py 3.11

✕ build_error

✕ timeout

py 3.12

✕ build_error

✓ 79.8s

py 3.13

✕ build_error

py 3.9

✕ build_error

5427MB installed

● package 5427MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

KeyphraseCountVectorizer

✓ from keyphrase_vectorizers import KeyphraseCountVectorizer

✗ from keyphrase_vectorizers.KeyphraseVectorizers import KeyphraseCountVectorizer

The correct import is directly from the package; nested module import fails.

KeyphraseTfidfVectorizer

✓ from keyphrase_vectorizers import KeyphraseTfidfVectorizer

✗ from keyphrase_vectorizers.KeyphraseVectorizer import KeyphraseTfidfVectorizer

Common typo in class name; there is no 'KeyphraseVectorizer' class.

Basic usage: load spaCy model, create vectorizer, fit on documents, and inspect extracted keyphrases.

import spacy from keyphrase_vectorizers import KeyphraseCountVectorizer # Download spaCy model if not already present # spacy.cli.download('en_core_web_sm') nlp = spacy.load('en_core_web_sm') docs = [ "Natural language processing enables computers to understand human language.", "Machine learning is a subset of artificial intelligence." ] vectorizer = KeyphraseCountVectorizer(spacy_pipeline=nlp) X = vectorizer.fit_transform(docs) print(vectorizer.get_feature_names_out()) print(X.toarray())

Debug

Known issues

gotchaThe `spacy_pipeline` parameter expects a spaCy Language object (from spacy.load) or a string (e.g., 'en_core_web_sm'). Passing a string will cause spaCy to load the pipeline each time, leading to performance issues.

fix

Always load the model once and pass the nlp object: `nlp = spacy.load('en_core_web_sm'); vectorizer = KeyphraseCountVectorizer(spacy_pipeline=nlp)`

affects: >=0.0.11

deprecatedThe parameter `multiprocessing` was renamed to `workers` in v0.0.6. Using `multiprocessing` will raise a TypeError.

fix

Use `workers` instead of `multiprocessing` when specifying the number of parallel processes.

affects: >=0.0.6

breakingIn v0.0.9, the default exclusion of certain spaCy pipeline components was removed. This can slow down keyphrase extraction but ensures compatibility with all spaCy pipelines, especially transformer-based ones.

fix

If performance degrades, explicitly disable unnecessary pipeline components via the `spacy_exclude` parameter.

affects: >=0.0.9

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'keyphrase_vectorizers'

The package is not installed or installed under a different name.

fix

Run `pip install keyphrase-vectorizers` to install.

ValueError: The 'spacy_pipeline' parameter must be a string or spacy Language object.

Passed an unsupported type (e.g., integer) as `spacy_pipeline`.

fix

Pass either a preloaded spaCy Language object or a valid spaCy model name string, e.g., 'en_core_web_sm'.

OSError: [E050] Can't find model 'en_core_web_sm'. It doesn't seem to be a Python package or a valid path to a data directory.

The required spaCy model is not installed.

fix

Run `python -m spacy download en_core_web_sm` (or the equivalent for your language).

Upgrade

Version history

0.0.13latest on PyPI · released May 2, 2024

Audit

Dependencies

spacyrequiredRequired for POS tagging and keyphrase extraction; must install a language model (e.g., en_core_web_sm).

scikit-learnrequiredUsed for vectorization and matrix operations.

Agent activity

4 hits · last 30 days

node

Resources

githubhttps://github.com/TimSchopf/KeyphraseVectorizers ↗

packagehttps://pypi.org/project/keyphrase-vectorizers/ ↗