paralabelgen 0.0.0

A Python library for generating discrete paragraph labels from concept extraction, graph communities, and interpretable assignment rules.

paralabelgen

paralabelgen is a Python library for generating discrete multi-label annotations for text paragraphs.

Install

pip install paralabelgen

Optional extras:

pip install "paralabelgen[graph]"
pip install "paralabelgen[nlp]"

Example

from labelgen import LabelGenerator, LabelGeneratorConfig

paragraphs = [
    "OpenAI builds language models for developers.",
    "Developers use language models in production systems.",
]

generator = LabelGenerator(LabelGeneratorConfig())
result = generator.fit_transform(paragraphs)

print("Concepts:")
for concept in result.concepts:
    print(concept.normalized, concept.kind, concept.document_frequency)

print("Labels:")
for assignment in result.paragraph_labels:
    print(assignment.paragraph_id, assignment.label_ids, assignment.label_scores)

Notes

• The distribution name is paralabelgen, while the Python import package is labelgen.
• fit learns concept communities from a corpus.
• transform applies previously learned communities to new paragraphs.
• fit_transform learns and labels the same input in one pass.
• The base package works with deterministic fallback implementations.
• Without paralabelgen[nlp], concept extraction uses regex and heuristic rules: capitalized spans are treated as lightweight entities, and non-stopword token spans are treated as candidate noun phrases.
• Without paralabelgen[graph], community detection falls back to deterministic connected components over the concept co-occurrence graph instead of Leiden.
• Install paralabelgen[nlp] to enable spaCy-based concept extraction.
• Install paralabelgen[graph] to enable Leiden community detection.