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MLH fellowship contribution: adding the laser_encoders module (#249)
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* feat: converted SPMapply function to use python script

* modified laserTokenizer class to have a seperate function for tokenizing a file

* modified tokenize_file function

* removed instances of Path

* created new function for opening files

* test for LaserTokenizer.tokenize

* tests for normalisation, descape and lower_case

* deleted test dir because of relative import error

* modified test tokenizer function to use the downloaded model before exiting the context manager

* test for tokenize_file

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* test for over_write when equal to True and False

* added some type hints for tests

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* feat: make LASER pip installable (#239)

* feat: make LASER pip installable

* Added GitHub Actions workflow for tests and linting

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* Refactor embedder (#241)

* feat: make LASER pip installable

* Added GitHub Actions workflow for tests and linting

* upgraded python version due to node depreciation error

* removed updated python version

* removed poetry

* bug fixes

* removed dependencies install

* updated pyproject and made lint_and_test to install dev and mono dependencies

* removed isort and black

* removed mono dependencies

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* added src-layout to discover only laser_encoder

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* removed src-layout to test

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* updated linting to only check the laser_encoders folder

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* feat: Add Python function to download LASER models (#244)

* feat: make LASER pip installable

* Added GitHub Actions workflow for tests and linting

* upgraded python version due to node depreciation error

* removed updated python version

* removed poetry

* bug fixes

* removed dependencies install

* updated pyproject and made lint_and_test to install dev and mono dependencies

* removed isort and black

* removed mono dependencies

* removed version from pyproject

* removed duplicate of classifiers

* removed description

* removed dynamic

* added src-layout to discover only laser_encoder

* added build backend

* updated project name

* changed license to BSD

* removed src-layout to test

* added linting to actions

* updated linting to only check the laser_encoders folder

* fixed linting issues

* fixed black linting issues

* added white-space

* refactored emmbeder to work in the laser tokenizer package

* downgraded numpy version to suit the installled python version

* added test for sentence encoder

* added whitespace to test workflow

* restructured test for sentence encoder

* restructured test for sentence encoder

* fixed black issues

* restructured test for sentence encoder

* changed python version because of workflow error

* updated dependencies requirements version

* removed unneccessary print statement

* updated python version

* restructured test_sentence_encoder

* restructured test_sentence encoder

* black linting fixes

* restructure calling of tempile module

* updated workflow to remove pip cache

* removed commented code

* refactored code and added type hints

* fixed black issues

* fixed no module found error by adding Laser environment

* feat:created download function for downloading laser models in python

* added language list and made some changes to the download models

* fixed linting issues

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* updated laguage list with some laser2 and laser3 languages

* refactor: added option for laser

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* refactoed download function to display total filesize in MB and also made some changes to raise an error when laser is not passed

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* documentation for the laser_encoder

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* updated readme to include supported flore200 langs

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* Revert "added requirements for laser_encoder"

This reverts commit 431780e.

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* changed ibo to igbo

* updated doc to effect the ibo to igbo change

* refactore: modified the sentence encoder to tokenize a text before encodingit

* debugging failed test

* added a call method to seperately handle the tokenization before encodding

* added value error for when there is no spm_model

* documentation for the new __call__ method for tokenization with encoder

* docs: Update docs to include reference to laserembeddings (#254)

* Handle Interrupted Model Weight Downloads (#253)

* fix: Fix interrupted downloads issue

* style: Format code using black

* Update download method to use tempfile

* style: Remove unnecessary space

* Fix OSError by using shutil.move for cross-filesystem moves

Using os.rename caused an OSError when trying to move files across different filesystems (e.g., from /tmp to another directory).
By using shutil.move, we gracefully handle such situations,
ensuring files are moved correctly regardless of the source and destination filesystems.

* Refactor `initialize_encoder` to `LaserEncoderPipeline` (#256)

* Remove 'tokenize' argument from initialize_encoder function

* Add LaserEncoderPipeline for streamlined tokenization and encoding

* docs: Update README to show use of LaserEncoderPipeline

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* test: Add test for LaserEncoderPipeline

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* Extend Tokenizer to Support Single Strings and Lists of Strings (#258)

* Handle case for both str and list in tokenizer

* test: Add test for tokenizer call method

* Rename 'sentences' argument to 'text_or_batch' for clarity

* Handle string input in call method

* Update validate_models.py

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* Enhance LaserTokenizer with Perl Parity, Optional Punctuation Normalization, and Embedding Normalization (#262)

* Introduce pearl compability flag

* Add argument `normalize_punct` to `LaserTokenizer`

* Add normalize_embeddings option to encode_sentences

* Update README on normalize_embeddings option

* style: Run black and isort

* test: Add tests for normalize_embeddings flag in sentence encoder

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* Update validate_models.py

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* Create test_validate_models.py

* Rename test_validate_models.py to test_models_initialization.py

* Update test_models_initialization.py

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* Decrease versions of numpy and torch required by laser-encoders (#264)

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* Update the main README file with a mention of `laser_encoders` (#266)

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* Update language_list.py (#269)

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* Updated laser encoder pipeline

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* Added warning for using laser2 with a language

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---------

Co-authored-by: CaptainVee <[email protected]>
Co-authored-by: Victor Joseph <[email protected]>
Co-authored-by: Kevin Heffernan <[email protected]>
Co-authored-by: Okewunmi Paul <[email protected]>
Co-authored-by: NIXBLACK11 <[email protected]>
Co-authored-by: Siddharth Singh Rana <[email protected]>
Co-authored-by: Kevin Heffernan <[email protected]>
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32 changes: 32 additions & 0 deletions .github/workflows/lint_and_tests.yml
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name: lint_and_tests

on: [push, pull_request]

jobs:
build:
strategy:
max-parallel: 1
matrix:
platform: [ubuntu-latest]
python-version: [3.8]

runs-on: ${{ matrix.platform }}

steps:
- uses: actions/checkout@v2

- name: Install dependencies
run: |
python --version
python -m pip install --upgrade 'pip>=23.2.1'
python -m pip show pip
python -m pip install -e '.[dev]'
- name: isort
run: cd laser_encoders && isort --check --diff .

- name: black
run: cd laser_encoders && black --check --diff .

- name: pytest
run: pytest laser_encoders
3 changes: 3 additions & 0 deletions .gitignore
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Expand Up @@ -10,3 +10,6 @@ tasks/xnli/XNLI-1.0*
tasks/xnli/multinli_1.0*
.??*swp
.idea
__pycache__
nllb
dist
26 changes: 24 additions & 2 deletions README.md
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LASER is a library to calculate and use multilingual sentence embeddings.

**NEWS**
* 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)
Expand All @@ -26,7 +27,27 @@ 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.

## Dependencies
## 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
Expand All @@ -42,7 +63,8 @@ be found [here](https://arxiv.org/abs/2205.12654), together with an experimental
* [pandas](https://pypi.org/project/pandas), data analysis toolkit (`pip install pandas`)
* [Sentencepiece](https://github.com/google/sentencepiece), subword tokenization (installed automatically)

## Installation
### 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`
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#
###################################################################

echo "Installing the laser_encoders package in editable mode"

pip install -e .

echo "Installing external tools"

InstallMosesTools
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149 changes: 149 additions & 0 deletions laser_encoders/README.md
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# LASER encoders

LASER Language-Agnostic SEntence Representations Toolkit

laser_encoders is the official Python package for the Facebook [LASER](https://github.com/facebookresearch/LASER) library. It provides a simple and convenient way to use LASER embeddings in Python. It allows you to calculate multilingual sentence embeddings using the LASER toolkit. These embeddings can be utilized for various natural language processing tasks, including document classification, bitext filtering, and mining.

## Dependencies

- Python `>= 3.8`
- [PyTorch `>= 1.10.0`](http://pytorch.org/)
- sacremoses `>=0.1.0`
- sentencepiece `>=0.1.99`
- numpy `>=1.21.3`
- fairseq `>=0.12.2`

You can find a full list of requirements [here](https://github.com/facebookresearch/LASER/blob/main/pyproject.toml)

## Installation

You can install `laser_encoders` package from PyPI:

```sh
pip install laser_encoders
```

Alternatively, you can install it from a local clone of this repository, in editable mode:
```sh
pip install . -e
```

## Usage

Here's a simple example on how to obtain embeddings for sentences using the `LaserEncoderPipeline`:

>**Note:** By default, the models will be downloaded to the `~/.cache/laser_encoders` directory. To specify a different download location, you can provide the argument `model_dir=path/to/model/directory`
```py
from laser_encoders import LaserEncoderPipeline

# Initialize the LASER encoder pipeline
encoder = LaserEncoderPipeline(lang="igbo")

# Encode sentences into embeddings
embeddings = encoder.encode_sentences(["nnọọ, kedu ka ị mere"])
# If you want the output embeddings to be L2-normalized, set normalize_embeddings to True
normalized_embeddings = encoder.encode_sentences(["nnọọ, kedu ka ị mere"], normalize_embeddings=True)

```

If you prefer more control over the tokenization and encoding process, you can initialize the tokenizer and encoder separately:
```py
from laser_encoders import initialize_encoder, initialize_tokenizer

# Initialize the LASER tokenizer
tokenizer = initialize_tokenizer(lang="igbo")
tokenized_sentence = tokenizer.tokenize("nnọọ, kedu ka ị mere")

# Initialize the LASER sentence encoder
encoder = initialize_encoder(lang="igbo")

# Encode tokenized sentences into embeddings
embeddings = encoder.encode_sentences([tokenized_sentence])
```
>By default, the `spm` flag is set to `True` when initializing the encoder, ensuring the accompanying spm model is downloaded.
**Supported Languages:** You can specify any language from the [FLORES200](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200) dataset. This includes both languages identified by their full codes (like "ibo_Latn") and simpler alternatives (like "igbo").

## Downloading the pre-trained models

If you prefer to download the models individually, you can use the following command:

```sh
python -m laser_encoders.download_models --lang=your_prefered_language # e.g., --lang="igbo""
```

By default, the downloaded models will be stored in the `~/.cache/laser_encoders` directory. To specify a different download location, utilize the following command:

```sh
python -m laser_encoders.download_models --model-dir=path/to/model/directory
```

> For a comprehensive list of available arguments, you can use the `--help` command with the download_models script.
Once you have successfully downloaded the models, you can utilize the `SentenceEncoder` to tokenize and encode your text in your desired language. Here's an example of how you can achieve this:

```py
from laser_encoders.models import SentenceEncoder
from pathlib import Path

encoder = SentenceEncoder(model_path=path/to/downloaded/model, spm_model=Path(path/to/spm_model), spm_vocab=path/to/cvocab)
embeddings = encoder("This is a test sentence.")
```
If you want to perform tokenization seperately, you can do this below:
```py
from laser_encoders.laser_tokenizer import LaserTokenizer

tokenizer = LaserTokenizer(spm_model=Path(path/to/spm_model))

tokenized_sentence = tokenizer.tokenize("This is a test sentence.")

encoder = SentenceEncoder(model_path=path/to/downloaded/model, spm_vocab=path/to/cvocab)
embeddings = encoder.encode_sentences([tokenized_sentence])
```

For tokenizing a file instead of a string, you can use the following:

```py
tokenized_sentence = tokenizer.tokenize_file(inp_fname=Path(path/to/input_file.txt), out_fname=Path(path/to/output_file.txt))
```

### Now you can use these embeddings for downstream tasks

For more advanced usage and options, please refer to the official LASER repository documentation.

## LASER Versions and Associated Packages

For users familiar with the earlier version of LASER, you might have encountered the [`laserembeddings`](https://pypi.org/project/laserembeddings/) package. This package primarily dealt with LASER-1 model embeddings.

For the latest LASER-2,3 models, use the newly introduced `laser_encoders` package, which offers better performance and support for a wider range of languages.


## Contributing

We welcome contributions from the developer community to enhance and improve laser_encoders. If you'd like to contribute, you can:

1. Submit bug reports or feature requests through GitHub issues.
1. Fork the repository, make changes, and submit pull requests for review.

Please follow our [Contribution Guidelines](https://github.com/facebookresearch/LASER/blob/main/CONTRIBUTING.md) to ensure a smooth process.

### Code of Conduct

We expect all contributors to adhere to our [Code of Conduct](https://github.com/facebookresearch/LASER/blob/main/CODE_OF_CONDUCT.md).

### Contributors

The following people have contributed to this project:

- [Victor Joseph](https://github.com/CaptainVee)
- [Paul Okewunmi](https://github.com/Paulooh007)
- [Siddharth Singh Rana](https://github.com/NIXBLACK11)
- [David Dale](https://github.com/avidale/)
- [Holger Schwenk](https://github.com/hoschwenk)
- [Kevin Heffernan](https://github.com/heffernankevin)

### License

This package is released under the [LASER](https://github.com/facebookresearch/LASER/blob/main/LICENSE) BSD License.

16 changes: 16 additions & 0 deletions laser_encoders/__init__.py
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#!/bin/bash
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
#
# LASER Language-Agnostic SEntence Representations
# is a toolkit to calculate multilingual sentence embeddings
# and to use them for document classification, bitext filtering
# and mining
#
# -------------------------------------------------------

from laser_encoders.laser_tokenizer import initialize_tokenizer
from laser_encoders.models import LaserEncoderPipeline, initialize_encoder
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