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Production-ready LASER multilingual embeddings

Project description

LASER embeddings

Travis (.org) branch PyPI - Python Version PyPI PyPI - License

Out-of-the-box multilingual sentence embeddings.

laserembeddings is a pip-packaged, production-ready port of Facebook Research's LASER (Language-Agnostic SEntence Representations) to compute multilingual sentence embeddings.

Have a look at the project's repo (master branch) for the full documentation.

Getting started

You'll need Python 3.6 or higher.


pip install laserembeddings

To install laserembeddings with extra dependencies:

# if you need Chinese support:
pip install laserembeddings[zh]

# if you need Japanese support (not available on Windows):
pip install laserembeddings[ja]

# or both:
pip install laserembeddings[zh,ja]

Downloading the pre-trained models

python -m laserembeddings download-models

This will download the models to the default data directory next to the source code of the package. Use python -m laserembeddings download-models path/to/model/directory to download the models to a specific location.


from laserembeddings import Laser

laser = Laser()

# if all sentences are in the same language:

embeddings = laser.embed_sentences(
    ['let your neural network be polyglot',
     'use multilingual embeddings!'],
    lang='en')  # lang is only used for tokenization

# embeddings is a N*1024 (N = number of sentences) NumPy array

If the sentences are not in the same language, you can pass a list of languages

embeddings = laser.embed_sentences(
    ['I love pasta.',
     "J'adore les pâtes.",
     'Ich liebe Pasta.'],
    lang=['en', 'fr', 'de'])

If you downloaded the models into a specific directory:

from laserembeddings import Laser

path_to_bpe_codes = ...
path_to_bpe_vocab = ...
path_to_encoder = ...

laser = Laser(path_to_bpe_codes, path_to_bpe_vocab, path_to_encoder)

# you can also supply file objects instead of file paths

If you want to pull the models from S3:

from io import BytesIO, StringIO
from laserembeddings import Laser
import boto3

s3 = boto3.resource('s3')

f_bpe_codes = StringIO(s3.Object(MODELS_BUCKET, 'path_to_bpe_codes.fcodes').get()['Body'].read().decode('utf-8'))
f_bpe_vocab = StringIO(s3.Object(MODELS_BUCKET, 'path_to_bpe_vocabulary.fvocab').get()['Body'].read().decode('utf-8'))
f_encoder = BytesIO(s3.Object(MODELS_BUCKET, '').get()['Body'].read())

laser = Laser(f_bpe_codes, f_bpe_vocab, f_encoder)

Project details

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