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https://github.com/bramvanroy/mateo-demo
MAchine Translation Evaluation Online (MATEO)
https://github.com/bramvanroy/mateo-demo
bertscore bleu bleurt chrf clarin comet machine-translation machine-translation-evaluation machine-translation-metrics streamlit ter
Last synced: 4 months ago
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MAchine Translation Evaluation Online (MATEO)
- Host: GitHub
- URL: https://github.com/bramvanroy/mateo-demo
- Owner: BramVanroy
- License: gpl-3.0
- Created: 2023-02-27T08:54:53.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-03-15T08:27:36.000Z (11 months ago)
- Last Synced: 2024-03-15T09:43:04.521Z (11 months ago)
- Topics: bertscore, bleu, bleurt, chrf, clarin, comet, machine-translation, machine-translation-evaluation, machine-translation-metrics, streamlit, ter
- Language: Python
- Homepage: https://mateo.ivdnt.org/
- Size: 296 KB
- Stars: 12
- Watchers: 1
- Forks: 1
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION
Awesome Lists containing this project
README
# MAchine Translation Evaluation Online (MATEO)
We present MAchine Translation Evaluation Online (MATEO), a project that aims to facilitate machine translation (MT)
evaluation by means of an easy-to-use interface that can evaluate given machine translations with a battery of
automatic metrics. It caters to both experienced and novice users who are working with MT, such as MT system builders,
and also researchers from Social Sciences and Humanities, and teachers and students of (machine) translation.
MATEO can be accessed on [this website](https://mateo.ivdnt.org/),
hosted by the [CLARIN](https://www.clarin.eu/) B center at [Instituut voor de Nederlandse Taal](https://ivdnt.org/).
It is also available on Hugging Face [Spaces](https://huggingface.co/spaces/BramVanroy/mateo-demo).
If you use the MATEO interface for your work, please cite our project paper!
Vanroy, B., Tezcan, A., & Macken, L. (2023). [MATEO: MAchine Translation Evaluation Online](https://aclanthology.org/2023.eamt-1.52/). In M. Nurminen, J. Brenner, M. Koponen, S. Latomaa, M. Mikhailov, F. Schierl, … H. Moniz (Eds.), _Proceedings of the 24th Annual Conference of the European Association for Machine Translation_ (pp. 499–500). Tampere, Finland: European Association for Machine Translation (EAMT).
```bibtex
@inproceedings{vanroy-etal-2023-mateo,
title = "{MATEO}: {MA}chine {T}ranslation {E}valuation {O}nline",
author = "Vanroy, Bram and
Tezcan, Arda and
Macken, Lieve",
booktitle = "Proceedings of the 24th Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2023",
address = "Tampere, Finland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2023.eamt-1.52",
pages = "499--500",
}
```
## Self-hosting
The MATEO [website](https://lt3.ugent.be/mateo/) is provided for free as a hosted application. That means that you, or
anyone else, can use it. The implication is that it is possible that the service will be slow depending on the usage of
the system. As such, specific attention was paid to making it easy for you to set up your own instance that you can use!
### Duplicating a Hugging Face Spaces
MATEO is also [running](https://huggingface.co/spaces/BramVanroy/mateo-demo) on the free platform of 🤗 Hugging Face in a
so-called 'Space'. If you have an account (free) on that platform, you can easily duplicate the running MATEO instance
to your own profile. That means that you can create a private duplication of the MATEO interface **just for you** and
free of charge! You can simply click [this link](https://huggingface.co/spaces/BramVanroy/mateo-demo?duplicate=true)
or, if that does not work, follow these steps:
1. Go to the [Space](https://huggingface.co/spaces/BramVanroy/mateo-demo);
2. in the top right (below your profile picture) you should click on the three vertical dots;
3. choose 'Duplicate space', _et voilà !_, a new space should now be running on your own profile
### Install locally with Python
You can clone and install the library on your own device (laptop, computer, server). I recommend to run this in a new
virtual environment. It requires `python >= 3.10`.
Run the following commands:
```shell
git clone https://github.com/BramVanroy/mateo-demo.git
cd mateo-demo
python -m pip install .
cd src/mateo_st
streamlit run 01_🎈_MATEO.py
```
The streamlit server will then start on your own computer. You can access the website via a local address,
[http://localhost:8501](http://localhost:8501) by default.
Configuration options specific to Streamlit can be found
[here](https://docs.streamlit.io/library/advanced-features/configuration). They are more related to server-side configurations
that you typically do not need when you are running this directly through Python. But you may need them when you are
using Docker, e.g. setting the `--server.port` that streamlit is running on (see [Docker](#running-with-docker)).
A number of command-line arguments are available to change the interface to your needs.
```shell
--use_cuda whether to use CUDA for translation task (CUDA for metrics not supported) (default: False)
--demo_mode when demo mode is enabled, only a limited range of neural check-points are available. So all metrics are available but not all of the checkpoints. (default: False)
```
These can be passed to the Streamlit launcher by adding a `--` after the streamlit command and streamlit-specific
options, followed by any of the options above.
For instance, if you want to run streamlit specifically on port 1234 and you want to use the demo mode, you can modify
your command to look like this:
```shell
streamlit run 01_🎈_MATEO.py --server.port 1234 -- --demo_mode
```
Note the separating `--` in the middle so that streamlit can distinguish between streamlit's own options and the MATEO
configuration parameters.
### Running with Docker
If you have docker installed, it is very easy to get a MATEO instance running.
The following Dockerfiles are available in the [`docker`](docker) directory. They are a little bit different depending
on the specific needs.
- [`hf-spaces`](docker/hf-spaces/Dockerfile): specific configuration for Hugging Face spaces but without env options
- [`default`](docker/default/Dockerfile): a more intricate Dockerfile that accepts environment variables to be used
that are specific to the server, demo functionality, and CUDA. These Docker environment variables are available.
- PORT: server port to expose and to run the streamlit server on (default: 7860)
- SERVER: server address to run on (default: 'localhost')
- BASE: base path (default: '')
- DEMO_MODE: set to `true` to disable some options for neural metrics and to limit the max. upload size to 1MB
per file (default: '')
As an example, to build and run the repository on port 5034 with CUDA disabled and demo mode enabled, you can run the
following commands which will automatically use the most recent `cpu` Dockerfile from Github.
```shell
docker build -t mateo https://raw.githubusercontent.com/BramVanroy/mateo-demo/main/docker/cpu/Dockerfile
docker run --rm -d --name mateo-demo -p 5034:5034 --env PORT=5034 --env DEMO_MODE=true mateo
```
Note how the opened ports in Docker's `-p` must correspond with the env variable `PORT`!
MATEO is now running on port 5034 and available on the local address [http://localhost:5034/](http://localhost:5034/).
As mentioned before, you can modify the Dockerfiles as you wish. Most notably you may want to change the `streamlit`
launcher command itself. Therefore you could use the [streamlit options]([here](https://docs.streamlit.io/library/advanced-features/configuration))
alongside custom options for MATEO specifically, which were mentioned in the [previous section](#install-locally-with-python).
## Tests
The tests are run using `pytest` and `playwright`. To ensure that the right dependencies are installed, you can run
```shell
python -m pip install -e .[dev]
```
Then, install the appropriate chromium version for playwright. You can do this by running the following command.
```shell
playwright install --with-deps chromium
```
Now you can run the tests by running the following command in the root directory of the project.
```shell
python -m pytest
```
## Notes
### Using CUDA
Using CUDA for the metrics is currently not supported. However, it is possible to use CUDA for the translation task.
This can be done by setting the `--use_cuda` flag when running the Streamlit server. This will enable the use of CUDA
for the translation task, but not for the metrics. The reason for this is the memory consumption since streamlit
creates a separate instance for each user, the GPU may run OOM quickly and moving on/off devices is not feasible.
I have not found a solution for this yet. A queueing system would solve the issue with a separate backend and dedicated
workers, but that defeats the purpose of having a simple, easy-to-use interface. It would also lead to the requirement
of strong data for longer, which many users may not want to, considering that I've received many questions whether I
save their data on disk (I don't - the current approach processes everything in memory).
## Acknowledgements
This project was kickstarted by a Sponsorship project from the
[European Association for Machine Translation](https://eamt.org/), and
a substantial follow-up grant by the support of [CLARIN.eu](https://www.clarin.eu/).
