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--- |
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tags: |
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- translation |
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license: cc-by-4.0 |
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--- |
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### opus-mt-ru-en |
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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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- [Training](#training) |
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- [Evaluation](#evaluation) |
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- [Citation Information](#citation-information) |
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- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
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## Model Details |
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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:** Transformer-align |
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- **Language(s):** |
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- Source Language: Russian |
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- Target Language: English |
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- **License:** CC-BY-4.0 |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train) |
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## Uses |
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#### Direct Use |
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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 this section contains 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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Further details about the dataset for this model can be found in the OPUS readme: [ru-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ru-en/README.md) |
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## Training |
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#### Training Data |
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##### Preprocessing |
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* Pre-processing: Normalization + SentencePiece |
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* Dataset: [opus](https://github.com/Helsinki-NLP/Opus-MT) |
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* Download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.zip) |
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* Test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.test.txt) |
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## Evaluation |
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#### Results |
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* test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-en/opus-2020-02-26.eval.txt) |
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#### Benchmarks |
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| testset | BLEU | chr-F | |
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|-----------------------|-------|-------| |
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| newstest2012.ru.en | 34.8 | 0.603 | |
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| newstest2013.ru.en | 27.9 | 0.545 | |
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| newstest2014-ruen.ru.en | 31.9 | 0.591 | |
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| newstest2015-enru.ru.en | 30.4 | 0.568 | |
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| newstest2016-enru.ru.en | 30.1 | 0.565 | |
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| newstest2017-enru.ru.en | 33.4 | 0.593 | |
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| newstest2018-enru.ru.en | 29.6 | 0.565 | |
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| newstest2019-ruen.ru.en | 31.4 | 0.576 | |
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| Tatoeba.ru.en | 61.1 | 0.736 | |
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## Citation Information |
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```bibtex |
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@InProceedings{TiedemannThottingal:EAMT2020, |
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author = {J{\"o}rg Tiedemann and Santhosh Thottingal}, |
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title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld}, |
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booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)}, |
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year = {2020}, |
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address = {Lisbon, Portugal} |
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} |
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``` |
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## How to Get Started With the Model |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ru-en") |
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model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ru-en") |
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``` |
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