Introduction

This repository contains the scripts to train neuronal translation models for OpenNMT and also the Softcatalà published models.

For more information about training see the TRAINING document.

The corpus used to train these models are available here: https://github.com/Softcatala/parallel-catalan-corpus/

And here the tools that at Softcatalà to serve these models in production: https://github.com/Softcatala/nmt-softcatala

Models

Language pair	SC model BLEU	SC Flores200 BLEU	Google BLEU	Meta NLLB200 BLEU	Opus-MT BLEU	Sentences	Download model
German-Catalan	35.3	29.2	35.5	30.7	18.5	3137426	deu-cat-2024-10-02.zip
Catalan-German	29.0	25.4	32.9	29.1	15.8	3137426	cat-deu-2024-10-06.zip
English-Catalan	47.9	43.9	46.0	41.7	29.8	9617177	eng-cat-2024-09-24.zip
Catalan-English	49.8	43.8	47.0	48.0	29.6	9617177	cat-eng-2024-09-29.zip
Basque-Catalan	38.8	24.9	29.6	25.7	N/A	9546180	eus-cat-2024-08-09.zip
Catalan-Basque	27.3	17.1	18.0	10.5	N/A	9546180	cat-eus-2024-08-12.zip
French-Catalan	43.0	33.8	37.3	33.3	27.2	6392858	fra-cat-2024-08-29.zip
Catalan-French	43.1	37.0	41.7	39.6	27.9	6392858	cat-fra-2024-08-30.zip
Galician-Catalan	66.4	32.5	36.5	33.2	N/A	5644577	glg-cat-2024-09-03.zip
Catalan-Galician	69.4	32.2	33.1	31.7	N/A	5644577	cat-glg-2024-09-04.zip
Italian-Catalan	41.0	27.0	30.6	27.8	22.0	4146825	ita-cat-2024-08-29.zip
Catalan-Italian	38.3	25.1	27.5	26.0	19.2	4146825	cat-ita-2024-08-30.zip
Japanese-Catalan	25.5	17.5	23.4	N/A	N/A	1996286	jpn-cat-2024-10-04.zip
Catalan-Japanese	21.8	20.6	32.5	N/A	N/A	3992572	cat-jpn-2024-10-08.zip
Dutch-Catalan	30.4	20.3	27.1	24.8	15.8	2208538	nld-cat-2022-11-19.zip
Catalan-Dutch	27.6	18.2	23.4	21.8	13.4	2208538	cat-nld-2022-11-19.zip
Occitan-Catalan	74.9	32.5	N/A	36.2	N/A	2711350	oci-cat-2022-11-17.zip
Catalan-Occitan	78.8	28.9	N/A	27.8	N/A	2711350	cat-oci-2022-11-21.zip
Portuguese-Catalan	41.6	33.9	38.7	34.5	28.1	2043019	por-cat-2022-11-16.zip
Catalan-Portuguese	39.0	32.3	40.0	36.5	27.5	2043019	cat-por-2022-11-18.zip
Spanish-Catalan	88.8	22.6	23.6	25.8	22.5	7596985	spa-cat-2022-11-16.zip
Catalan-Spanish	87.5	24.2	24.2	25.5	23.2	7596985	cat-spa-2022-11-17.zip

Legend:

• SC Model BLEU column indicates the Softcatalà models' BLEU score against the corpus test dataset (from train/dev/test)
• SC Flores200 BLEU column indicates the Softcatalà models' BLEU score against Flores200 benchmark dataset devtest split. This provides an external evaluation
• Google BLEU is the BLUE score of Google Translate using the Flores200 benchmark
• Opus-MT BLEU is the BLUE score of the Opus-MT models using the Flores200 benchmark (our ambition is to outperform them)
• Sentences is the number of sentences in the corpus used for training
• Meta NLLB200 refers to nllb-200-3.3B model from Meta. This is a very slow model and it's distilled version performs significantly worse.

Notes:

• All models are based on TransformerRelative and SentencePiece has been used as tokenizer.
• We use Sacrebleu to calculate BLUE scores with the 13a tokenizer.
• These models are used in production with modest hardware (CPU). As result, these models are a balance between precision and latency. It is possible to further improve BLUE scores by ~+1 BLEU, but at a significant latency cost at inference.
• BLEU is the most popular metric for evaluating machine translation but also broadly acknowledged that it is not perfect. It's estimated that has a ~80% correlation with human judgment
• Flores200 has some limitations. It was produced translating from English to many of the other languages. When you use flores for example to benchmark Catalan - Spanish translations, consider that the Catalan -> Spanish corpus was produced by translating from English to Catalan and from English to Spanish. The resulting Spanish and Catalan translations are different from what a translator will do translating directly from Spanish to Catalan. As a summary, Flores200 is more reliable for benchmarks where English is the source or target language.
• Occitan model is based on Languedocian variant

Structure of the models

Description of the directories on the contained in the models zip file:

• tensorflow: model exported in Tensorflow format
• ctranslate2: model exported in CTranslate2 format (used for inference)
• metadata: description of the model
• tokenizer: SentencePiece models for both languages

Using the models

You can use the models with https://github.com/OpenNMT/CTranslate2 which offers fast inference.

At Softcatalà we built also command line tools to translate TXT and PO files. See: https://github.com/Softcatala/nmt-softcatala/tree/master/use-models-tools

Download the model and unpack it:

wget https://www.softcatala.org/pub/softcatala/opennmt/models/2022-11-22/eng-cat-2024-09-24.zip
unzip eng-cat-2024-09-24.zip

Install dependencies:

pip3 install ctranslate2 pyonmttok

Simple translation using Python:

import ctranslate2
translator = ctranslate2.Translator("eng-cat/ctranslate2/")
translator.translate_batch([["▁Hello", "▁world", "!"]])
[[{'tokens': ['▁Hola', '▁món', '!']}]]

Simple tokenization & translation using Python:

import pyonmttok
tokenizer=pyonmttok.Tokenizer(mode="none", sp_model_path = "eng-cat/tokenizer/sp_m.model")
tokenized=tokenizer.tokenize("Hello world!")

import ctranslate2
translator = ctranslate2.Translator("eng-cat/ctranslate2/")
translated = translator.translate_batch([tokenized[0]])
print(tokenizer.detokenize(translated[0][0]['tokens']))
Hola món!

Training the models

In order to train models you should have a GPU.

Training in a machine

First you need to install the necessary packages:

make install

After this, you download be all the corpuses:

make get-corpus

To train the English - Catalan model type:

make train-eng-cat

Training using a Jupyter notebook

We recommend using Kaggle which provides Jupyter notebooks with GPU access.

We have a Jupyter notebook which allows to trains simple models to learn how to use this toolset.