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https://github.com/facebookresearch/LASER

Language-Agnostic SEntence Representations
https://github.com/facebookresearch/LASER

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Language-Agnostic SEntence Representations

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README 

        
# LASER  Language-Agnostic SEntence Representations



LASER is a library to calculate and use multilingual sentence embeddings.



**NEWS**

* 2023/11/30 Released [**P-xSIM**](tasks/pxsim), a dual approach extension to multilingual similarity search (xSIM)

* 2023/11/16 Released [**laser_encoders**](laser_encoders), a pip-installable package supporting LASER-2 and LASER-3 models

* 2023/06/26 [**xSIM++**](https://arxiv.org/abs/2306.12907) evaluation pipeline and data [**released**](tasks/xsimplusplus/README.md)

* 2022/07/06 Updated LASER models with support for over 200 languages are [**now available**](nllb/README.md)

* 2022/07/06 Multilingual similarity search (**xSIM**) evaluation pipeline [**released**](tasks/xsim/README.md)

* 2022/05/03 [**Librivox S2S is available**](tasks/librivox-s2s): Speech-to-Speech translations automatically mined in Librivox [9]

* 2019/11/08 [**CCMatrix is available**](tasks/CCMatrix): Mining billions of high-quality parallel sentences on the WEB [8]

* 2019/07/31 Gilles Bodard and Jérémy Rapin provided a [**Docker environment**](docker) to use LASER

* 2019/07/11 [**WikiMatrix is available**](tasks/WikiMatrix): bitext extraction for 1620 language pairs in WikiPedia [7]

* 2019/03/18 switch to BSD license

* 2019/02/13 The code to perform bitext mining is [**now available**](tasks/bucc)



**CURRENT VERSION:**

* We now provide updated LASER models which support over 200 languages. Please see [here](nllb/README.md) for more details including how to download the models and perform inference.



According to our experience, the sentence encoder also supports code-switching, i.e.

the same sentences can contain words in several different languages.



We have also some evidence that the encoder can generalize to other

languages which have not been seen during training, but which are in

a language family which is covered by other languages.



A detailed description of how the multilingual sentence embeddings are trained can

be found [here](https://arxiv.org/abs/2205.12654), together with an experimental evaluation.



## The core sentence embedding package: `laser_encoders`

We provide a package `laser_encoders` with minimal dependencies.

It supports LASER-2 (a single encoder for the languages listed [below](#supported-languages)) 

and LASER-3 (147 language-specific encoders described [here](nllb/README.md)).



The package can be installed simply with `pip install laser_encoders` and used as below:



```python

from laser_encoders import LaserEncoderPipeline

encoder = LaserEncoderPipeline(lang="eng_Latn")

embeddings = encoder.encode_sentences(["Hi!", "This is a sentence encoder."])

print(embeddings.shape)  # (2, 1024)

```



The laser_encoders [readme file](laser_encoders) provides more examples of its installation and usage.



## The full LASER kit

Apart from the `laser_encoders`, we provide support for LASER-1 (the original multilingual encoder)

and for various LASER applications listed below.



### Dependencies

* Python >= 3.7

* [PyTorch 1.0](http://pytorch.org/)

* [NumPy](http://www.numpy.org/), tested with 1.15.4

* [Cython](https://pypi.org/project/Cython/), needed by Python wrapper of FastBPE, tested with 0.29.6

* [Faiss](https://github.com/facebookresearch/faiss), for fast similarity search and bitext mining

* [transliterate 1.10.2](https://pypi.org/project/transliterate) (`pip install transliterate`)

* [jieba 0.39](https://pypi.org/project/jieba/), Chinese segmenter (`pip install jieba`)

* [mecab 0.996](https://pypi.org/project/JapaneseTokenizer/), Japanese segmenter

* tokenization from the Moses encoder (installed automatically)

* [FastBPE](https://github.com/glample/fastBPE), fast C++ implementation of byte-pair encoding (installed automatically)

* [Fairseq](https://github.com/pytorch/fairseq), sequence modeling toolkit (`pip install fairseq==0.12.1`)

* [tabulate](https://pypi.org/project/tabulate), pretty-print tabular data (`pip install tabulate`)

* [pandas](https://pypi.org/project/pandas), data analysis toolkit (`pip install pandas`)

* [Sentencepiece](https://github.com/google/sentencepiece), subword tokenization (installed automatically)



### Installation

* install the `laser_encoders` package by e.g. `pip install -e .` for installing it in the editable mode

* set the environment variable 'LASER' to the root of the installation, e.g.

  `export LASER="${HOME}/projects/laser"`

* download encoders from Amazon s3 by e.g. `bash ./nllb/download_models.sh` 

* download third party software by `bash ./install_external_tools.sh`

* download the data used in the example tasks (see description for each task)



## Applications



We showcase several applications of multilingual sentence embeddings

with code to reproduce our results (in the directory "tasks").



* [**Cross-lingual document classification**](tasks/mldoc) using the

  [*MLDoc*](https://github.com/facebookresearch/MLDoc) corpus [2,6]

* [**WikiMatrix**](tasks/WikiMatrix)

   Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia [7]

* [**Bitext mining**](tasks/bucc) using the

  [*BUCC*](https://comparable.limsi.fr/bucc2018/bucc2018-task.html) corpus [3,5]

* [**Cross-lingual NLI**](tasks/xnli)

  using the [*XNLI*](https://www.nyu.edu/projects/bowman/xnli/) corpus [4,5,6]

* [**Multilingual similarity search**](tasks/similarity) [1,6]

* [**Sentence embedding of text files**](tasks/embed)

  example how to calculate sentence embeddings for arbitrary text files in any of the supported language.



**For all tasks, we use exactly the same multilingual encoder, without any task specific optimization or fine-tuning.**



## License



LASER is BSD-licensed, as found in the [`LICENSE`](LICENSE) file in the root directory of this source tree.



## Supported languages



The original LASER model was trained on the following languages:



Afrikaans, Albanian, Amharic, Arabic, Armenian, Aymara, Azerbaijani, Basque, Belarusian, Bengali,

Berber languages, Bosnian, Breton, Bulgarian, Burmese, Catalan, Central/Kadazan Dusun, Central Khmer,

Chavacano, Chinese, Coastal Kadazan, Cornish, Croatian, Czech, Danish, Dutch, Eastern Mari, English,

Esperanto, Estonian, Finnish, French, Galician, Georgian, German, Greek, Hausa, Hebrew, Hindi,

Hungarian, Icelandic, Ido, Indonesian, Interlingua, Interlingue, Irish, Italian, Japanese, Kabyle,

Kazakh, Korean, Kurdish, Latvian, Latin, Lingua Franca Nova, Lithuanian, Low German/Saxon,

Macedonian, Malagasy, Malay, Malayalam, Maldivian (Divehi), Marathi, Norwegian (Bokmål), Occitan,

Persian (Farsi), Polish, Portuguese, Romanian, Russian, Serbian, Sindhi, Sinhala, Slovak, Slovenian,

Somali, Spanish, Swahili, Swedish, Tagalog, Tajik, Tamil, Tatar, Telugu, Thai, Turkish, Uighur,

Ukrainian, Urdu, Uzbek, Vietnamese, Wu Chinese and Yue Chinese.



We have also observed that the model seems to generalize well to other (minority) languages or dialects, e.g.



Asturian, Egyptian Arabic, Faroese, Kashubian, North Moluccan Malay, Nynorsk Norwegian, Piedmontese, Sorbian, Swabian,

Swiss German or Western Frisian.



### LASER3



Updated LASER models referred to as *[LASER3](nllb/README.md)* supplement the above list with support for 147 languages. The full list of supported languages can be seen [here](nllb/README.md#list-of-available-laser3-encoders).



## References



[1] Holger Schwenk and Matthijs Douze,

    [*Learning Joint Multilingual Sentence Representations with Neural Machine Translation*](https://aclanthology.info/papers/W17-2619/w17-2619),

    ACL workshop on Representation Learning for NLP, 2017



[2] Holger Schwenk and Xian Li,

    [*A Corpus for Multilingual Document Classification in Eight Languages*](http://www.lrec-conf.org/proceedings/lrec2018/pdf/658.pdf),

    LREC, pages 3548-3551, 2018.



[3] Holger Schwenk,

    [*Filtering and Mining Parallel Data in a Joint Multilingual Space*](http://aclweb.org/anthology/P18-2037)

    ACL, July 2018



[4] Alexis Conneau, Guillaume Lample, Ruty Rinott, Adina Williams, Samuel R. Bowman, Holger Schwenk and Veselin Stoyanov,

    [*XNLI: Cross-lingual Sentence Understanding through Inference*](https://aclweb.org/anthology/D18-1269),

    EMNLP, 2018.



[5] Mikel Artetxe and Holger Schwenk,

    [*Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings*](https://arxiv.org/abs/1811.01136)

    arXiv, Nov 3 2018.



[6] Mikel Artetxe and Holger Schwenk,

    [*Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond*](https://arxiv.org/abs/1812.10464)

    arXiv, Dec 26 2018.



[7] Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Paco Guzman,

    [*WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia*](https://arxiv.org/abs/1907.05791)

    arXiv, July 11  2019.



[8] Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin

    [*CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB*](https://arxiv.org/abs/1911.04944)



[9] Paul-Ambroise Duquenne, Hongyu Gong, Holger Schwenk,

    [*Multimodal and Multilingual Embeddings for Large-Scale Speech Mining,*](https://papers.nips.cc/paper/2021/hash/8466f9ace6a9acbe71f75762ffc890f1-Abstract.html), NeurIPS 2021, pages 15748-15761.



[10] Kevin Heffernan, Onur Celebi, and Holger Schwenk,

     [*Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages*](https://arxiv.org/abs/2205.12654)