metadata

language:
  - ca
  - es
  - fr
  - gl
  - it
  - lt
  - lv
  - pt
tags:
  - translation
  - opus-mt-tc
license: cc-by-4.0
model-index:
  - name: opus-mt-tc-big-itc-bat
    results:
      - task:
          name: Translation cat-lav
          type: translation
          args: cat-lav
        dataset:
          name: flores101-devtest
          type: flores_101
          args: cat lav devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 21.9
          - name: chr-F
            type: chrf
            value: 0.52215
      - task:
          name: Translation cat-lit
          type: translation
          args: cat-lit
        dataset:
          name: flores101-devtest
          type: flores_101
          args: cat lit devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 20.2
          - name: chr-F
            type: chrf
            value: 0.5238
      - task:
          name: Translation fra-lav
          type: translation
          args: fra-lav
        dataset:
          name: flores101-devtest
          type: flores_101
          args: fra lav devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 23
          - name: chr-F
            type: chrf
            value: 0.5339
      - task:
          name: Translation fra-lit
          type: translation
          args: fra-lit
        dataset:
          name: flores101-devtest
          type: flores_101
          args: fra lit devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 21.1
          - name: chr-F
            type: chrf
            value: 0.53595
      - task:
          name: Translation glg-lav
          type: translation
          args: glg-lav
        dataset:
          name: flores101-devtest
          type: flores_101
          args: glg lav devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 20.7
          - name: chr-F
            type: chrf
            value: 0.51043
      - task:
          name: Translation glg-lit
          type: translation
          args: glg-lit
        dataset:
          name: flores101-devtest
          type: flores_101
          args: glg lit devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 19.9
          - name: chr-F
            type: chrf
            value: 0.51854
      - task:
          name: Translation ita-lav
          type: translation
          args: ita-lav
        dataset:
          name: flores101-devtest
          type: flores_101
          args: ita lav devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 19.6
          - name: chr-F
            type: chrf
            value: 0.51065
      - task:
          name: Translation ita-lit
          type: translation
          args: ita-lit
        dataset:
          name: flores101-devtest
          type: flores_101
          args: ita lit devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 17.4
          - name: chr-F
            type: chrf
            value: 0.51309
      - task:
          name: Translation por-lav
          type: translation
          args: por-lav
        dataset:
          name: flores101-devtest
          type: flores_101
          args: por lav devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 22.9
          - name: chr-F
            type: chrf
            value: 0.53493
      - task:
          name: Translation por-lit
          type: translation
          args: por-lit
        dataset:
          name: flores101-devtest
          type: flores_101
          args: por lit devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 21.8
          - name: chr-F
            type: chrf
            value: 0.53821
      - task:
          name: Translation spa-lav
          type: translation
          args: spa-lav
        dataset:
          name: flores101-devtest
          type: flores_101
          args: spa lav devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 17.4
          - name: chr-F
            type: chrf
            value: 0.4929
      - task:
          name: Translation spa-lit
          type: translation
          args: spa-lit
        dataset:
          name: flores101-devtest
          type: flores_101
          args: spa lit devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 16.2
          - name: chr-F
            type: chrf
            value: 0.49836
      - task:
          name: Translation ita-lit
          type: translation
          args: ita-lit
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: ita-lit
        metrics:
          - name: BLEU
            type: bleu
            value: 40.9
          - name: chr-F
            type: chrf
            value: 0.6764
      - task:
          name: Translation spa-lit
          type: translation
          args: spa-lit
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: spa-lit
        metrics:
          - name: BLEU
            type: bleu
            value: 45.9
          - name: chr-F
            type: chrf
            value: 0.68805

opus-mt-tc-big-itc-bat

Model Details
Uses
Risks, Limitations and Biases
How to Get Started With the Model
Training
Evaluation
Citation Information
Acknowledgements

Model Details

Neural machine translation model for translating from Italic languages (itc) to Baltic languages (bat).

This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train. Model Description:

Developed by: Language Technology Research Group at the University of Helsinki
Model Type: Translation (transformer-big)
Release: 2022-07-27
License: CC-BY-4.0
Language(s):
- Source Language(s): cat fra glg ita por spa
- Target Language(s): lav lit prg
- Language Pair(s): cat-lav cat-lit fra-lav fra-lit glg-lav glg-lit ita-lav ita-lit por-lav por-lit spa-lit
- Valid Target Language Labels: >>lav<< >>lit<< >>ltg<< >>ndf<< >>olt<< >>prg<< >>prg_Latn<< >>sgs<< >>svx<< >>sxl<< >>xcu<< >>xgl<< >>xsv<< >>xzm<<
Original Model: opusTCv20210807_transformer-big_2022-07-27.zip
Resources for more information:
- OPUS-MT-train GitHub Repo
- More information about released models for this language pair: OPUS-MT itc-bat README
- More information about MarianNMT models in the transformers library
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/

This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of >>id<< (id = valid target language ID), e.g. >>lav<<

Uses

This model can be used for translation and text-to-text generation.

Risks, Limitations and Biases

CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).

How to Get Started With the Model

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>lit<< Els gats són complexos individus.",
    ">>sgs<< No."
]

model_name = "pytorch-models/opus-mt-tc-big-itc-bat"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))

for t in translated:
    print( tokenizer.decode(t, skip_special_tokens=True) )

# expected output:
#     Katės yra sudėtingi individai.
#     no no no no no no no no no no no no no no no no no no no no no

You can also use OPUS-MT models with the transformers pipelines, for example:

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-itc-bat")
print(pipe(">>lit<< Els gats són complexos individus."))

# expected output: Katės yra sudėtingi individai.

Training

Data: opusTCv20210807 (source)
Pre-processing: SentencePiece (spm32k,spm32k)
Model Type: transformer-big
Original MarianNMT Model: opusTCv20210807_transformer-big_2022-07-27.zip
Training Scripts: GitHub Repo

Evaluation

test set translations: opusTCv20210807_transformer-big_2022-07-27.test.txt
test set scores: opusTCv20210807_transformer-big_2022-07-27.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
ita-lit	tatoeba-test-v2021-08-07	0.67640	40.9	224	1321
spa-lit	tatoeba-test-v2021-08-07	0.68805	45.9	454	2352
cat-lav	flores101-devtest	0.52215	21.9	1012	22092
cat-lit	flores101-devtest	0.52380	20.2	1012	20695
fra-lav	flores101-devtest	0.53390	23.0	1012	22092
fra-lit	flores101-devtest	0.53595	21.1	1012	20695
glg-lav	flores101-devtest	0.51043	20.7	1012	22092
glg-lit	flores101-devtest	0.51854	19.9	1012	20695
ita-lav	flores101-devtest	0.51065	19.6	1012	22092
ita-lit	flores101-devtest	0.51309	17.4	1012	20695
por-lav	flores101-devtest	0.53493	22.9	1012	22092
por-lit	flores101-devtest	0.53821	21.8	1012	20695
spa-lav	flores101-devtest	0.49290	17.4	1012	22092
spa-lit	flores101-devtest	0.49836	16.2	1012	20695

Citation Information

Publications: OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (Please, cite if you use this model.)

@inproceedings{tiedemann-thottingal-2020-opus,
    title = "{OPUS}-{MT} {--} Building open translation services for the World",
    author = {Tiedemann, J{\"o}rg  and Thottingal, Santhosh},
    booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
    month = nov,
    year = "2020",
    address = "Lisboa, Portugal",
    publisher = "European Association for Machine Translation",
    url = "https://aclanthology.org/2020.eamt-1.61",
    pages = "479--480",
}

@inproceedings{tiedemann-2020-tatoeba,
    title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
    author = {Tiedemann, J{\"o}rg},
    booktitle = "Proceedings of the Fifth Conference on Machine Translation",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.wmt-1.139",
    pages = "1174--1182",
}

Acknowledgements

The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the MeMAD project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland.

Model conversion info

transformers version: 4.16.2
OPUS-MT git hash: 8b9f0b0
port time: Sat Aug 13 00:04:44 EEST 2022
port machine: LM0-400-22516.local

Helsinki-NLP
/

opus-mt-tc-big-itc-bat