Update the main README file with a mention of laser_encoders

.gitignore

@@ -10,3 +10,5 @@ tasks/xnli/XNLI-1.0*
 tasks/xnli/multinli_1.0*
 .??*swp
 .idea
+__pycache__
+nllb

README.md

@@ -3,6 +3,7 @@
 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)
@@ -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 (an updated signle 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
@@ -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` 

install_external_tools.sh

@@ -181,6 +181,10 @@ InstallMecab () {
 #
 ###################################################################
 
+echo "Installing the laser_encoders package in editable mode"
+
+pip install -e .
+
 echo "Installing external tools"
 
 InstallMosesTools

laser_encoders/README.md

@@ -17,10 +17,15 @@ You can find a full list of requirements [here](requirements.txt)
 
 ## Installation
 
-You can install laser_encoders using pip:
+You can install `laser_encoders` package from PyPI:
 
 ```sh
- pip install laser_encoders
+pip install laser_encoders
+```
+
+Alternatively, you can install it from a local clone of this repository, in editable mode:
+```sh
+pip install . -e
 ```
 
 ## Usage