sentalign 0.2.1

In-memory sentence alignment with Vecalign dynamic programming

sentalign

sentalign is a small Python package for aligning sentences between two languages using multilingual sentence embeddings and VecAlign-style dynamic programming.

It works fully in memory: pass two lists of sentences, get back aligned sentence blocks.

Installation

pip install sentalign

For real multilingual alignment, you also need an embedding model. For example:

pip install sentence-transformers

Basic usage

from sentence_transformers import SentenceTransformer
from sentalign import sentalign

src = [
    "Hello world.",
    "My name is Abdulrahman.",
    "I like machine learning.",
]

tgt = [
    "Bonjour le monde.",
    "Je m'appelle Abdulrahman.",
    "J'aime l'apprentissage automatique.",
]

encoder = SentenceTransformer(
    "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
)

result = sentalign(
    src,
    tgt,
    encoder=encoder,
)

print("Overall score:", result.overall_score)

for alignment in result.alignments:
    print(alignment.src_indices, alignment.tgt_indices)
    print(alignment.src_sentences)
    print(alignment.tgt_sentences)
    print("score:", alignment.score)
    print()

Important: use a multilingual encoder

sentalign does not create embeddings by itself. You must pass an encoder.

For cross-language alignment, use a multilingual encoder such as:

SentenceTransformer("sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")

or a model like LaBSE.

Do not use a monolingual English-only model for English/French, English/Arabic, Hebrew/English, etc. The encoder must place sentences from both languages in the same embedding space.

API

sentalign(src_sentences, tgt_sentences, encoder)

Returns a SentAlignResult:

result.alignments
result.overall_score
result.average_alignment_score

Each alignment block contains:

alignment.src_indices
alignment.tgt_indices
alignment.score
alignment.src_sentences
alignment.tgt_sentences

Development

Install locally in editable mode:

pip install -e .

Run a simple multilingual test:

pip install sentence-transformers
python tests/test_multilingual.py

Build the package:

python -m build
twine check dist/*

License

Apache-2.0