|
--- |
|
language: |
|
- da |
|
- is |
|
- nb |
|
- nn |
|
- sv |
|
|
|
tags: |
|
- translation |
|
- opus-mt-tc |
|
|
|
license: cc-by-4.0 |
|
model-index: |
|
- name: opus-mt-tc-big-gmq-gmq |
|
results: |
|
- task: |
|
name: Translation isl-swe |
|
type: translation |
|
args: isl-swe |
|
dataset: |
|
name: europeana2021 |
|
type: europeana2021 |
|
args: isl-swe |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 22.2 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.45562 |
|
- task: |
|
name: Translation nob-isl |
|
type: translation |
|
args: nob-isl |
|
dataset: |
|
name: europeana2021 |
|
type: europeana2021 |
|
args: nob-isl |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 29.7 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.54171 |
|
- task: |
|
name: Translation nob-swe |
|
type: translation |
|
args: nob-swe |
|
dataset: |
|
name: europeana2021 |
|
type: europeana2021 |
|
args: nob-swe |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 54.0 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.73891 |
|
- task: |
|
name: Translation dan-isl |
|
type: translation |
|
args: dan-isl |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: dan isl devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 22.2 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.50227 |
|
- task: |
|
name: Translation dan-nob |
|
type: translation |
|
args: dan-nob |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: dan nob devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 28.6 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.58445 |
|
- task: |
|
name: Translation dan-swe |
|
type: translation |
|
args: dan-swe |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: dan swe devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 38.5 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.65000 |
|
- task: |
|
name: Translation isl-dan |
|
type: translation |
|
args: isl-dan |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: isl dan devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 27.2 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.53630 |
|
- task: |
|
name: Translation isl-nob |
|
type: translation |
|
args: isl-nob |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: isl nob devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 20.5 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.49434 |
|
- task: |
|
name: Translation isl-swe |
|
type: translation |
|
args: isl-swe |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: isl swe devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 26.0 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.53373 |
|
- task: |
|
name: Translation nob-dan |
|
type: translation |
|
args: nob-dan |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: nob dan devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 31.7 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.59657 |
|
- task: |
|
name: Translation nob-isl |
|
type: translation |
|
args: nob-isl |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: nob isl devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 18.9 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.47432 |
|
- task: |
|
name: Translation nob-swe |
|
type: translation |
|
args: nob-swe |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: nob swe devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 31.3 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.60030 |
|
- task: |
|
name: Translation swe-dan |
|
type: translation |
|
args: swe-dan |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: swe dan devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 39.0 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.64340 |
|
- task: |
|
name: Translation swe-isl |
|
type: translation |
|
args: swe-isl |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: swe isl devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 21.7 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.49590 |
|
- task: |
|
name: Translation swe-nob |
|
type: translation |
|
args: swe-nob |
|
dataset: |
|
name: flores101-devtest |
|
type: flores_101 |
|
args: swe nob devtest |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 28.9 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.58336 |
|
- task: |
|
name: Translation dan-nob |
|
type: translation |
|
args: dan-nob |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: dan-nob |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 78.2 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.87556 |
|
- task: |
|
name: Translation dan-swe |
|
type: translation |
|
args: dan-swe |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: dan-swe |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 72.5 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.83556 |
|
- task: |
|
name: Translation nno-nob |
|
type: translation |
|
args: nno-nob |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: nno-nob |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 78.9 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.88349 |
|
- task: |
|
name: Translation nob-dan |
|
type: translation |
|
args: nob-dan |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: nob-dan |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 73.9 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.85345 |
|
- task: |
|
name: Translation nob-nno |
|
type: translation |
|
args: nob-nno |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: nob-nno |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 55.2 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.74571 |
|
- task: |
|
name: Translation nob-swe |
|
type: translation |
|
args: nob-swe |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: nob-swe |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 73.9 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.84747 |
|
- task: |
|
name: Translation swe-dan |
|
type: translation |
|
args: swe-dan |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: swe-dan |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 72.6 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.83392 |
|
- task: |
|
name: Translation swe-nob |
|
type: translation |
|
args: swe-nob |
|
dataset: |
|
name: tatoeba-test-v2021-08-07 |
|
type: tatoeba_mt |
|
args: swe-nob |
|
metrics: |
|
- name: BLEU |
|
type: bleu |
|
value: 76.3 |
|
- name: chr-F |
|
type: chrf |
|
value: 0.85815 |
|
--- |
|
# opus-mt-tc-big-gmq-gmq |
|
|
|
## Table of Contents |
|
- [Model Details](#model-details) |
|
- [Uses](#uses) |
|
- [Risks, Limitations and Biases](#risks-limitations-and-biases) |
|
- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
|
- [Training](#training) |
|
- [Evaluation](#evaluation) |
|
- [Citation Information](#citation-information) |
|
- [Acknowledgements](#acknowledgements) |
|
|
|
## Model Details |
|
|
|
Neural machine translation model for translating from North Germanic languages (gmq) to North Germanic languages (gmq). |
|
|
|
This model is part of the [OPUS-MT project](https://github.com/Helsinki-NLP/Opus-MT), 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](https://marian-nmt.github.io/), 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](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/Opus-MT-train). |
|
**Model Description:** |
|
- **Developed by:** Language Technology Research Group at the University of Helsinki |
|
- **Model Type:** Translation (transformer-big) |
|
- **Release**: 2022-07-29 |
|
- **License:** CC-BY-4.0 |
|
- **Language(s):** |
|
- Source Language(s): dan fao isl nno nob nor swe |
|
- Target Language(s): dan isl nno nob nor swe |
|
- Language Pair(s): dan-isl dan-nob dan-swe isl-dan isl-nob isl-swe nno-nob nob-dan nob-isl nob-nno nob-swe swe-dan swe-isl swe-nob |
|
- Valid Target Language Labels: >>dan<< >>fao<< >>isl<< >>jut<< >>nno<< >>nob<< >>non<< >>nrn<< >>ovd<< >>qer<< >>rmg<< >>swe<< |
|
- **Original Model**: [opusTCv20210807_transformer-big_2022-07-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opusTCv20210807_transformer-big_2022-07-29.zip) |
|
- **Resources for more information:** |
|
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
|
- More information about released models for this language pair: [OPUS-MT gmq-gmq README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-gmq/README.md) |
|
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) |
|
- [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. `>>dan<<` |
|
|
|
## 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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). |
|
|
|
## How to Get Started With the Model |
|
|
|
A short example code: |
|
|
|
```python |
|
from transformers import MarianMTModel, MarianTokenizer |
|
|
|
src_text = [ |
|
">>fao<< Jeg er bange for kakerlakker.", |
|
">>nob<< Vladivostok är en stad i Ryssland." |
|
] |
|
|
|
model_name = "pytorch-models/opus-mt-tc-big-gmq-gmq" |
|
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: |
|
# Tað eru uml. |
|
# Vladivostok er en by i Russland. |
|
``` |
|
|
|
You can also use OPUS-MT models with the transformers pipelines, for example: |
|
|
|
```python |
|
from transformers import pipeline |
|
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-gmq-gmq") |
|
print(pipe(">>fao<< Jeg er bange for kakerlakker.")) |
|
|
|
# expected output: Tað eru uml. |
|
``` |
|
|
|
## Training |
|
|
|
- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
|
- **Pre-processing**: SentencePiece (spm32k,spm32k) |
|
- **Model Type:** transformer-big |
|
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opusTCv20210807_transformer-big_2022-07-29.zip) |
|
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
|
|
|
## Evaluation |
|
|
|
* test set translations: [opusTCv20210807_transformer-big_2022-07-29.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opusTCv20210807_transformer-big_2022-07-29.test.txt) |
|
* test set scores: [opusTCv20210807_transformer-big_2022-07-29.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opusTCv20210807_transformer-big_2022-07-29.eval.txt) |
|
* benchmark results: [benchmark_results.txt](benchmark_results.txt) |
|
* benchmark output: [benchmark_translations.zip](benchmark_translations.zip) |
|
|
|
| langpair | testset | chr-F | BLEU | #sent | #words | |
|
|----------|---------|-------|-------|-------|--------| |
|
| dan-nob | tatoeba-test-v2021-08-07 | 0.87556 | 78.2 | 1299 | 9620 | |
|
| dan-swe | tatoeba-test-v2021-08-07 | 0.83556 | 72.5 | 1549 | 10060 | |
|
| nno-nob | tatoeba-test-v2021-08-07 | 0.88349 | 78.9 | 467 | 3129 | |
|
| nob-dan | tatoeba-test-v2021-08-07 | 0.85345 | 73.9 | 1299 | 9794 | |
|
| nob-nno | tatoeba-test-v2021-08-07 | 0.74571 | 55.2 | 466 | 3141 | |
|
| nob-swe | tatoeba-test-v2021-08-07 | 0.84747 | 73.9 | 563 | 3698 | |
|
| swe-dan | tatoeba-test-v2021-08-07 | 0.83392 | 72.6 | 1549 | 10239 | |
|
| swe-nob | tatoeba-test-v2021-08-07 | 0.85815 | 76.3 | 563 | 3708 | |
|
| isl-swe | europeana2021 | 0.45562 | 22.2 | 563 | 10293 | |
|
| nob-isl | europeana2021 | 0.54171 | 29.7 | 538 | 9932 | |
|
| nob-swe | europeana2021 | 0.73891 | 54.0 | 538 | 9885 | |
|
| dan-isl | flores101-devtest | 0.50227 | 22.2 | 1012 | 22834 | |
|
| dan-nob | flores101-devtest | 0.58445 | 28.6 | 1012 | 23873 | |
|
| dan-swe | flores101-devtest | 0.65000 | 38.5 | 1012 | 23121 | |
|
| isl-dan | flores101-devtest | 0.53630 | 27.2 | 1012 | 24638 | |
|
| isl-nob | flores101-devtest | 0.49434 | 20.5 | 1012 | 23873 | |
|
| isl-swe | flores101-devtest | 0.53373 | 26.0 | 1012 | 23121 | |
|
| nob-dan | flores101-devtest | 0.59657 | 31.7 | 1012 | 24638 | |
|
| nob-isl | flores101-devtest | 0.47432 | 18.9 | 1012 | 22834 | |
|
| nob-swe | flores101-devtest | 0.60030 | 31.3 | 1012 | 23121 | |
|
| swe-dan | flores101-devtest | 0.64340 | 39.0 | 1012 | 24638 | |
|
| swe-isl | flores101-devtest | 0.49590 | 21.7 | 1012 | 22834 | |
|
| swe-nob | flores101-devtest | 0.58336 | 28.9 | 1012 | 23873 | |
|
|
|
## Citation Information |
|
|
|
* Publications: [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (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](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), 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](https://memad.eu/), 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](https://www.csc.fi/), Finland. |
|
|
|
## Model conversion info |
|
|
|
* transformers version: 4.16.2 |
|
* OPUS-MT git hash: 8b9f0b0 |
|
* port time: Fri Aug 12 23:59:02 EEST 2022 |
|
* port machine: LM0-400-22516.local |
|
|
