LASER embeddings

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 or this release) for the full documentation.

Getting started

Prerequisites

You'll need Python 3.6+ and PyTorch. Please refer to PyTorch installation instructions.

Installation

pip install laserembeddings

Chinese language

Chinese is not supported by default. If you need to embed Chinese sentences, please install laserembeddings with the "zh" extra. This extra includes jieba.

pip install laserembeddings[zh]

Japanese language

Japanese is not supported by default. If you need to embed Japanese sentences, please install laserembeddings with the "ja" extra. This extra includes mecab-python3 and the ipadic dictionary, which is used in the original LASER project.

If you have issues running laserembeddings on Japanese sentences, please refer to mecab-python3 documentation for troubleshooting.

pip install laserembeddings[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.

Usage

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 language codes:

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')
MODELS_BUCKET = ...

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, 'path_to_encoder.pt').get()['Body'].read())

laser = Laser(f_bpe_codes, f_bpe_vocab, f_encoder)