Model Overview

This model can be used for translating text in source language (De) to a text in target language (En).

Model Architecture

The model is based on Transformer \"Big\" architecture originally presented in \"Attention Is All You Need\" paper [1].\nIn this particular instance, the model has 12 layers in the encoder and 2 layers in the decoder.\nIt is using YouTokenToMe tokenizer [2].

Training

These models were trained on a collection of many publicly available datasets comprising of millions of parallel sentences. The NeMo toolkit [5] was used for training this model over roughly 300k steps.

Datasets

While training this model, we used the following datasets:

Europarl de-en set from: http://www.statmt.org/europarl/v10/training/europarl-v10.de-en.tsv.gz
De-En version of parallel common crawl from: http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz
De-En version of paracrawl from: https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-de.txt.gz
De-En News commentary version from: http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.de-en.tsv.gz
De-En Wikipedia Parallel Titles from http://data.statmt.org/wikititles/v2/wikititles-v2.de-en.tsv.gz
A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.de-en.tmx.zip
A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2019.de-en.tmx.zip
A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.de-en.tmx.zip
A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.de-en.tmx.zip
De-En portion of WikiMatrix v1 de-en data from: http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.de-en.langid.tsv.gz

Tokenizer Construction

We used the YouTokenToMe tokenizer [2] with shared encoder and decoder BPE tokenizers.

Performance

The accuracy of translation models are often measured using BLEU scores [3].\nOn WMT20 Test set this model achieves 36.4 BLEU score measured using SacreBLEU package [4].\nBLEU+case.mixed+lang.de-en+numrefs.1+smooth.exp+test.wmt20+tok.13a+version.1.5.0 = 36.4 71.7/46.7/32.6/23.3 (BP = 0.912 ratio = 0.915 hyp_len = 34980 ref_len = 38220)

How to Use this Model

The model is available for use in the NeMo toolkit [5], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.

Automatically load the model from NGC

import nemo\nimport nemo.collections.nlp as nemo_nlp\nnmt_model = nemo_nlp.models.machine_translation.MTEncDecModel.from_pretrained(model_name=\"nmt_de_en_transformer12x2\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_de_en_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATION] --target_lang en --source_lang de\n

Input

This translate method of the NMT model accepts a list of de-tokenized strings.

Output

The translate method outputs a list of de-tokenized strings in the target language.

Limitations

No known limitations at this time.

References

[1] Vaswani, Ashish, et al. \"Attention is all you need.\" arXiv preprint arXiv:1706.03762 (2017).

[2] https://github.com/VKCOM/YouTokenToMe

[3] https://en.wikipedia.org/wiki/BLEU

[4] https://github.com/mjpost/sacreBLEU

[5] NVIDIA NeMo Toolkit

Licence

License to use this model is covered by the NGC TERMS OF USE unless another License/Terms Of Use/EULA is clearly specified. By downloading the public and release version of the model, you accept the terms and conditions of the NGC TERMS OF USE.

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The NeMo toolkit [5] was used for training this model over roughly 300k steps.\n\n### Datasets\nWhile training this model, we used the following datasets:\n\n* Europarl de-en set from: http://www.statmt.org/europarl/v10/training/europarl-v10.de-en.tsv.gz\n* De-En version of parallel common crawl from: http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\n* De-En version of paracrawl from: https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-de.txt.gz\n* De-En News commentary version from: http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.de-en.tsv.gz\n* De-En Wikipedia Parallel Titles from http://data.statmt.org/wikititles/v2/wikititles-v2.de-en.tsv.gz\n* A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.de-en.tmx.zip\n* A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2019.de-en.tmx.zip\n* A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.de-en.tmx.zip\n* A subset of Tilde Corpus from: https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.de-en.tmx.zip\n* De-En portion of WikiMatrix v1 de-en data from: http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.de-en.langid.tsv.gz\n\n### Tokenizer Construction\nWe used the YouTokenToMe tokenizer [2] with shared encoder and decoder BPE tokenizers.\n\n## Performance\nThe accuracy of translation models are often measured using BLEU scores [3].\nOn WMT20 Test set this model achieves 36.4 BLEU score measured using SacreBLEU package [4].\nBLEU+case.mixed+lang.de-en+numrefs.1+smooth.exp+test.wmt20+tok.13a+version.1.5.0 = 36.4 71.7/46.7/32.6/23.3 (BP = 0.912 ratio = 0.915 hyp_len = 34980 ref_len = 38220)\n\n## How to Use this Model\nThe model is available for use in the NeMo toolkit [5], and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n\n### Automatically load the model from NGC\n```python\nimport nemo\nimport nemo.collections.nlp as nemo_nlp\nnmt_model = nemo_nlp.models.machine_translation.MTEncDecModel.from_pretrained(model_name=\"nmt_de_en_transformer12x2\")\n```\n\n### Translating text with this model\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_de_en_transformer12x2.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATION] --target_lang en --source_lang de\n```\n### Input\n\nThis translate method of the NMT model accepts a list of de-tokenized strings.\n\n### Output\n\nThe translate method outputs a list of de-tokenized strings in the target language.\n\n## Limitations\n\nNo known limitations at this time.\n\n## References\n\n[1] Vaswani, Ashish, et al. \"Attention is all you need.\" arXiv preprint arXiv:1706.03762 (2017).\n\n[2] https://github.com/VKCOM/YouTokenToMe\n\n[3] https://en.wikipedia.org/wiki/BLEU\n\n[4] https://github.com/mjpost/sacreBLEU\n\n[5] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo)\n\n## Licence\n\nLicense to use this model is covered by the NGC [TERMS OF USE](https://ngc.nvidia.com/legal/terms) unless another License/Terms Of Use/EULA is clearly specified. 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