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--- |
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language: |
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- da |
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- de |
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- is |
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- nb |
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- sv |
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tags: |
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- translation |
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- opus-mt-tc |
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license: cc-by-4.0 |
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model-index: |
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- name: opus-mt-tc-big-de-gmq |
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results: |
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- task: |
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name: Translation deu-dan |
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type: translation |
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args: deu-dan |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: deu dan devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 35.6 |
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- name: chr-F |
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type: chrf |
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value: 0.62363 |
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- task: |
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name: Translation deu-isl |
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type: translation |
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args: deu-isl |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: deu isl devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 20.6 |
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- name: chr-F |
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type: chrf |
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value: 0.48691 |
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- task: |
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name: Translation deu-nob |
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type: translation |
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args: deu-nob |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: deu nob devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 25.3 |
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- name: chr-F |
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type: chrf |
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value: 0.55765 |
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- task: |
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name: Translation deu-swe |
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type: translation |
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args: deu-swe |
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dataset: |
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name: flores101-devtest |
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type: flores_101 |
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args: deu swe devtest |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 34.7 |
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- name: chr-F |
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type: chrf |
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value: 0.62323 |
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- task: |
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name: Translation deu-dan |
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type: translation |
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args: deu-dan |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: deu-dan |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 58.7 |
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- name: chr-F |
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type: chrf |
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value: 0.74306 |
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- task: |
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name: Translation deu-isl |
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type: translation |
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args: deu-isl |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: deu-isl |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 47.1 |
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- name: chr-F |
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type: chrf |
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value: 0.65180 |
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- task: |
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name: Translation deu-nob |
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type: translation |
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args: deu-nob |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: deu-nob |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 52.5 |
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- name: chr-F |
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type: chrf |
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value: 0.71062 |
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- task: |
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name: Translation deu-swe |
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type: translation |
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args: deu-swe |
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dataset: |
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name: tatoeba-test-v2021-08-07 |
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type: tatoeba_mt |
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args: deu-swe |
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metrics: |
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- name: BLEU |
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type: bleu |
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value: 58.3 |
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- name: chr-F |
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type: chrf |
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value: 0.72658 |
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--- |
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# opus-mt-tc-big-de-gmq |
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## Table of Contents |
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- [Model Details](#model-details) |
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- [Uses](#uses) |
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- [Risks, Limitations and Biases](#risks-limitations-and-biases) |
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- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
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- [Training](#training) |
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- [Evaluation](#evaluation) |
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- [Citation Information](#citation-information) |
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- [Acknowledgements](#acknowledgements) |
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## Model Details |
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Neural machine translation model for translating from German (de) to North Germanic languages (gmq). |
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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). |
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**Model Description:** |
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- **Developed by:** Language Technology Research Group at the University of Helsinki |
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- **Model Type:** Translation (transformer-big) |
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- **Release**: 2022-07-25 |
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- **License:** CC-BY-4.0 |
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- **Language(s):** |
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- Source Language(s): deu |
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- Target Language(s): dan isl nno nob nor swe |
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- Language Pair(s): deu-dan deu-isl deu-nob deu-swe |
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- Valid Target Language Labels: >>dan<< >>fao<< >>isl<< >>jut<< >>nno<< >>nob<< >>non<< >>nrn<< >>ovd<< >>qer<< >>rmg<< >>swe<< |
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- **Original Model**: [opusTCv20210807_transformer-big_2022-07-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-gmq/opusTCv20210807_transformer-big_2022-07-25.zip) |
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- **Resources for more information:** |
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- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
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- More information about released models for this language pair: [OPUS-MT deu-gmq README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/deu-gmq/README.md) |
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- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian) |
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- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/ |
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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<<` |
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## Uses |
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This model can be used for translation and text-to-text generation. |
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## Risks, Limitations and Biases |
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**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.** |
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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)). |
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## How to Get Started With the Model |
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A short example code: |
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```python |
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from transformers import MarianMTModel, MarianTokenizer |
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src_text = [ |
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">>dan<< Ich hätte fast meinen Pass vergessen.", |
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">>dan<< Dieses Fenster hier ist schusssicher." |
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] |
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model_name = "pytorch-models/opus-mt-tc-big-de-gmq" |
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tokenizer = MarianTokenizer.from_pretrained(model_name) |
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model = MarianMTModel.from_pretrained(model_name) |
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translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True)) |
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for t in translated: |
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print( tokenizer.decode(t, skip_special_tokens=True) ) |
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# expected output: |
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# Jeg havde næsten glemt mit pas. |
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# Dette vindue er skudsikkert. |
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``` |
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You can also use OPUS-MT models with the transformers pipelines, for example: |
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```python |
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from transformers import pipeline |
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pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-de-gmq") |
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print(pipe(">>dan<< Ich hätte fast meinen Pass vergessen.")) |
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# expected output: Jeg havde næsten glemt mit pas. |
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``` |
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## Training |
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- **Data**: opusTCv20210807 ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge)) |
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- **Pre-processing**: SentencePiece (spm32k,spm32k) |
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- **Model Type:** transformer-big |
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- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-07-25.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-gmq/opusTCv20210807_transformer-big_2022-07-25.zip) |
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- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
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## Evaluation |
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* test set translations: [opusTCv20210807_transformer-big_2022-07-25.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-gmq/opusTCv20210807_transformer-big_2022-07-25.test.txt) |
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* test set scores: [opusTCv20210807_transformer-big_2022-07-25.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu-gmq/opusTCv20210807_transformer-big_2022-07-25.eval.txt) |
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* benchmark results: [benchmark_results.txt](benchmark_results.txt) |
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* benchmark output: [benchmark_translations.zip](benchmark_translations.zip) |
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| langpair | testset | chr-F | BLEU | #sent | #words | |
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|----------|---------|-------|-------|-------|--------| |
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| deu-dan | tatoeba-test-v2021-08-07 | 0.74306 | 58.7 | 9998 | 74644 | |
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| deu-isl | tatoeba-test-v2021-08-07 | 0.65180 | 47.1 | 969 | 5951 | |
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| deu-nob | tatoeba-test-v2021-08-07 | 0.71062 | 52.5 | 3525 | 31978 | |
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| deu-swe | tatoeba-test-v2021-08-07 | 0.72658 | 58.3 | 3410 | 22701 | |
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| deu-dan | flores101-devtest | 0.62363 | 35.6 | 1012 | 24638 | |
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| deu-isl | flores101-devtest | 0.48691 | 20.6 | 1012 | 22834 | |
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| deu-nob | flores101-devtest | 0.55765 | 25.3 | 1012 | 23873 | |
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| deu-swe | flores101-devtest | 0.62323 | 34.7 | 1012 | 23121 | |
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## Citation Information |
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* 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.) |
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``` |
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@inproceedings{tiedemann-thottingal-2020-opus, |
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title = "{OPUS}-{MT} {--} Building open translation services for the World", |
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author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh}, |
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booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Lisboa, Portugal", |
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publisher = "European Association for Machine Translation", |
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url = "https://aclanthology.org/2020.eamt-1.61", |
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pages = "479--480", |
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} |
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@inproceedings{tiedemann-2020-tatoeba, |
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title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}", |
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author = {Tiedemann, J{\"o}rg}, |
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booktitle = "Proceedings of the Fifth Conference on Machine Translation", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2020.wmt-1.139", |
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pages = "1174--1182", |
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} |
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``` |
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## Acknowledgements |
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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. |
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## Model conversion info |
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* transformers version: 4.16.2 |
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* OPUS-MT git hash: 8b9f0b0 |
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* port time: Sat Aug 13 00:07:19 EEST 2022 |
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* port machine: LM0-400-22516.local |
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