babelvec 0.1.7

Position-aware, cross-lingually aligned word embeddings built on FastText

BabelVec

Position-aware, cross-lingually aligned word embeddings built on FastText.

Features

• Cross-Lingual Alignment: Procrustes alignment for multilingual compatibility
• Position-Aware Embeddings: Optional positional encoding (RoPE, sinusoidal, decay)
• FastText Foundation: Handles OOV words through subword information

Installation

pip install babelvec

For visualization support:

pip install babelvec[viz]

Quick Start

from babelvec import BabelVec

# Load a model
model = BabelVec.load('path/to/model.bin')

# Get word vector
vec = model.get_word_vector("hello")

# Position-aware sentence embedding
vec1 = model.get_sentence_vector("The dog bites the man", method='rope')
vec2 = model.get_sentence_vector("The man bites the dog", method='rope')
# vec1 != vec2 because word order is encoded

# Simple averaging (no position encoding)
vec = model.get_sentence_vector("Hello world", method='average')

Training

Monolingual Training

from babelvec.training import train_monolingual

model = train_monolingual(
    lang='en',
    corpus_path='corpus.txt',
    dim=300,
    epochs=5,
    threads=8  # Optional: specify number of threads
)
model.save('en_300d.bin')

Parallel Multi-Language Training (v0.1.4+)

Train multiple languages simultaneously for faster training on multi-core servers:

from babelvec.training import train_multiple_languages, get_cpu_count

# Auto-detects CPU cores
print(f"Using {get_cpu_count()} cores")

models = train_multiple_languages(
    languages={'en': 'en_corpus.txt', 'ar': 'ar_corpus.txt'},
    parallel=True,      # Train languages simultaneously
    max_workers=2,      # Number of parallel training jobs
)

Multilingual Training with Alignment

from babelvec.training import train_multilingual

models = train_multilingual(
    languages=['en', 'ar'],
    corpus_paths={'en': 'en.txt', 'ar': 'ar.txt'},
    parallel_data={('en', 'ar'): parallel_pairs},
    alignment='procrustes',
    threads=8  # Optional: specify number of threads
)

Post-hoc Alignment

from babelvec.training import align_models

aligned = align_models(
    models={'en': model_en, 'ar': model_ar},
    parallel_data={('en', 'ar'): parallel_pairs},
    method='procrustes'
)

Model Save/Load (v0.1.3+)

Models save projection matrices alongside the FastText binary:

# Save model
model.save('model.bin')
# Creates: model.bin, model.projection.npy (if aligned), model.meta.json

# Load model - projection is automatically restored
model = BabelVec.load('model.bin')
print(model.is_aligned)  # True if projection was loaded

Encoding Methods

Method	Description
rope	Rotary Position Embedding
decay	Exponential position decay
sinusoidal	Transformer-style positional encoding
average	Simple averaging (no position encoding)

Evaluation

from babelvec.evaluation import cross_lingual_retrieval

metrics = cross_lingual_retrieval(
    model_src=model_en,
    model_tgt=model_ar,
    parallel_sentences=test_pairs,
    method='rope'
)
print(f"Recall@1: {metrics['recall@1']:.3f}")

Language Families for Joint Training

BabelVec includes a curated family assignment system for 355 Wikipedia languages, optimized for joint multilingual training.

from babelvec.families import get_family_key, get_family_languages, get_training_groups

# Get family for a language
get_family_key("ary")  # -> "arabic"
get_family_key("fr")   # -> "romance_galloitalic"

# Get all languages in a family
get_family_languages("arabic")  # -> ["ar", "ary", "arz"]

# Create training groups (hybrid strategy)
groups = get_training_groups(
    languages=["en", "ar", "ary", "arz"],
    article_counts={"en": 6000000, "ar": 840000, "ary": 17000, "arz": 40000},
    low_resource_threshold=50000
)
# -> {"separate": ["en", "ar"], "joint": {"arabic": ["ary", "arz"]}}

Joint training dramatically improves low-resource languages (+200-600% for Arabic dialects) while high-resource languages should be trained separately.

Examples

See the examples/ directory:

• 01_basic_usage.py - Getting started

Citation

@misc{babelvec2025,
  title = {BabelVec: Position-Aware Cross-Lingual Word Embeddings},
  author = {Kamali, Omar},
  doi = {10.5281/zenodo.18065206},
  publisher = {Zenodo},
  year = {2025},
  url = {https://github.com/omarkamali/babelvec}
}

License

MIT License - see LICENSE for details.

Copyright © 2025 Omar Kamali