Model Overview

This model can be used for translating text in source language (Zh) 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 24 layers in the encoder and 6 layers in the decoder.\nIt is using YouTokenToMe tokenizer [2].

Training

These models were trained on a collection of many publicly available datasets comprising hundreds of millions of parallel sentences. The NeMo toolkit [5] was used for training this model over roughly 500k steps.

Datasets

While training this model, we used the following datasets:

News-Commentary http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-zh.tsv.gz
WikiTitles - http://data.statmt.org/wikititles/v2/wikititles-v2.zh-en.tsv.gz
WikiMatrix - http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-zh.langid.tsv.gz
Backtranslated Chinese - http://data.statmt.org/wmt20/translation-task/back-translation/zh-en/news.translatedto.zh.gz
Backtranslated English - http://data.statmt.org/wmt20/translation-task/back-translation/zh-en/news.en.gz
CC-Aligned - http://www.statmt.org/cc-aligned/sentence-aligned/en_XX-zh_CN.tsv.xz
CC-Matrix - https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix
Paracrawl - http://data.statmt.org/wmt21/translation-task/zh-en/paracrawl.wmt21.en-zh.gz

Tokenizer Construction

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

Performance

The accuracy of translation models are often measured using BLEU scores [3].\nThe model achieves the following sacreBLEU [4] scores on the WMT'18, WMT'19 and WMT'20 test sets

WMT18 - 29.0\nWMT19 - 30.2\nWMT20 - 31.0\n

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_zh_en_transformer24x6\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_zh_en_transformer24x6.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang en --source_lang zh\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 500k steps.\n\n### Datasets\nWhile training this model, we used the following datasets:\n\n* News-Commentary http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-zh.tsv.gz\n* WikiTitles - http://data.statmt.org/wikititles/v2/wikititles-v2.zh-en.tsv.gz\n* WikiMatrix - http://data.statmt.org/wmt20/translation-task/WikiMatrix/WikiMatrix.v1.en-zh.langid.tsv.gz\n* Backtranslated Chinese - http://data.statmt.org/wmt20/translation-task/back-translation/zh-en/news.translatedto.zh.gz\n* Backtranslated English - http://data.statmt.org/wmt20/translation-task/back-translation/zh-en/news.en.gz\n* CC-Aligned - http://www.statmt.org/cc-aligned/sentence-aligned/en_XX-zh_CN.tsv.xz\n* CC-Matrix - https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix\n* Paracrawl - http://data.statmt.org/wmt21/translation-task/zh-en/paracrawl.wmt21.en-zh.gz\n\n### Tokenizer Construction\nWe used the YouTokenToMe tokenizer [2] with separate encoder and decoder BPE tokenizers.\n\n## Performance\nThe accuracy of translation models are often measured using BLEU scores [3].\nThe model achieves the following sacreBLEU [4] scores on the WMT'18, WMT'19 and WMT'20 test sets\n\n```\nWMT18 - 29.0\nWMT19 - 30.2\nWMT20 - 31.0\n```\n\n## How to Use this Model\n\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_zh_en_transformer24x6\")\n```\n\n### Translating text with this model\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=nmt_zh_en_transformer24x6.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang en --source_lang zh\n```\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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Key	Value
Architecture	NMT Zh En Transformer24x6
Attention Heads	16
Feedforward Dimension	4096
Hidden Dimension	1024
Inputs	Text in Chinese
Number of Decoder Layers	6
Number of Encoder Layers	24
Outputs	Text in English

Key	Value
WMT'18	29.0
WMT'19	30.2
WMT'20	31.0