Model Overview

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

Model Architecture

The model is based on Transformer \"Base\" architecture originally presented in \"Attention Is All You Need\" paper [1].\nIt is using SentencePiece 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 450k steps.

Datasets

While training this model, we used the following datasets:

German

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

Spanish

Europarl - https://www.statmt.org/europarl/v7/es-en.tgz
Parallel Commoncrawl - http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz
Paracrawl - https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-es.txt.gz
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.en-es.tmx.zip
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-es.tmx.zip
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.en-es.tmx.zip
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.en-es.tmx.zip
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/worldbank.en-es.tmx.zip
WikiMatrix - https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-es.tsv.gz

French

Europarl - http://www.statmt.org/europarl/v10/training/europarl-v10.fr-en.tsv.gz
Parallel Commoncrawl - http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz
Paracrawl - https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-fr.txt.gz
Giga French - http://statmt.org/wmt10/training-giga-fren.tar
News Commentary - http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-fr.tsv.gz
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.en-fr.tmx.zip
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-fr.tmx.zip
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.en-fr.tmx.zip
Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.en-fr.tmx.zip
WikiMatrix - https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-fr.tsv.gz

Tokenizer Construction

We used the SentencePiece tokenizer [2] with shared 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 WMT test sets

De\nWMT13 - 26.5\nWMT14 - 27.3\n\nEs\nWMT12 - 38.4\nWMT13 - 34.5\n\nFr\nWMT13 - 33.3\nWMT14 - 38.6\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=\"mnmt_en_deesfr_transformerbase\")\n

Translating text with this model

python [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=mnmt_en_deesfr_transformerbase.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang [TARGET_LANGUAGE] --source_lang en\n

where [TARGET_LANGUAGE] can be 'de' or 'es' or 'fr'

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/google/sentencepiece

[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 450k steps.\n\n### Datasets\nWhile training this model, we used the following datasets:\n\nGerman\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\nSpanish\n* Europarl - https://www.statmt.org/europarl/v7/es-en.tgz\n* Parallel Commoncrawl - http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\n* Paracrawl - https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-es.txt.gz\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.en-es.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-es.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.en-es.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.en-es.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/worldbank.en-es.tmx.zip\n* WikiMatrix - https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-es.tsv.gz\n\nFrench\n* Europarl - http://www.statmt.org/europarl/v10/training/europarl-v10.fr-en.tsv.gz\n* Parallel Commoncrawl - http://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz\n* Paracrawl - https://s3.amazonaws.com/web-language-models/paracrawl/release7.1/en-fr.txt.gz\n* Giga French - http://statmt.org/wmt10/training-giga-fren.tar\n* News Commentary - http://data.statmt.org/news-commentary/v15/training/news-commentary-v15.en-fr.tsv.gz\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EESC2017.en-fr.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/rapid2016.en-fr.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/ecb2017.en-fr.tmx.zip\n* Tilde Corpus - https://tilde-model.s3-eu-west-1.amazonaws.com/EMA2016.en-fr.tmx.zip\n* WikiMatrix - https://dl.fbaipublicfiles.com/laser/WikiMatrix/v1/WikiMatrix.en-fr.tsv.gz\n\n### Tokenizer Construction\nWe used the SentencePiece tokenizer [2] with shared 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 WMT test sets\n\n```\nDe\nWMT13 - 26.5\nWMT14 - 27.3\n\nEs\nWMT12 - 38.4\nWMT13 - 34.5\n\nFr\nWMT13 - 33.3\nWMT14 - 38.6\n```\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=\"mnmt_en_deesfr_transformerbase\")\n```\n\n### Translating text with this model\n```bash\npython [NEMO_GIT_FOLDER]/examples/nlp/machine_translation/nmt_transformer_infer.py --model=mnmt_en_deesfr_transformerbase.nemo --srctext=[TEXT_IN_SRC_LANGUAGE] --tgtout=[WHERE_TO_SAVE_TRANSLATIONS] --target_lang [TARGET_LANGUAGE] --source_lang en\n```\nwhere [TARGET_LANGUAGE] can be 'de' or 'es' or 'fr'\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/google/sentencepiece\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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It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","linkedin.com",[450],{"_77":451,"_405":448,"_406":34,"_180":452},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. ","kndctr_F207D74D549850760A4C98C6_AdobeOrg_bc_session_id","ttcsid_XXXXXXXXXXXXXXXXXXXXCategory","This cookie name is associated with TikTok which is used to track your activity on the platform, including how you interact with their embedded services on other websites.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","_fbp","Used by Facebook to deliver a series of advertisement products such as real time bidding from third party advertisers","_zitok","_twpid","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[589,590,591,592,593,594,595,596,597,598,599,600,601,602,603,604,605,606,607,608,609,610,611,612,613],[560,561,562,563,564],{"_399":474,"_209":474,"_180":181,"_372":584},{"_399":448,"_209":448,"_180":181,"_372":579},{"_399":574,"_209":574,"_180":181,"_372":575},{"_399":569,"_209":569,"_180":181,"_372":570},{"_399":565,"_209":565,"_180":181,"_372":566},"nvidia.partners",[567],{"_77":568,"_405":565,"_406":34,"_180":442},"edge","app.hushly.com",[571],{"_77":572,"_405":569,"_406":34,"_180":573},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[576],{"_77":577,"_405":574,"_406":34,"_180":578},"_octo","This domain is owned by Github. 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We have categorized it under Functionality because it is necessary for the operation of our affiliate program.","nv_country_code","developer.nvidia.com","This cookie is likely used to store the country code of the visitor to provide localized content and possibly for legal compliance with regional data privacy laws.","_gdxxxxxxxxxxxxx","nvidianews.nvidia.com","docs.api.nvidia.com","research.nvidia.com","readme_library","wcmmode","ai-podcast.nvidia.com","resources.nvidia.com","geo_locator","openedx-language-preference","learn.nvidia.com","doxygen_width","raytracing-docs.nvidia.com","This cookie is likely used to remember the width preferences of a sidebar or interface element within a documentation platform like Doxygen.","_sfid_eb78","This cookie is categorized as Functionality Cookie.","crowdsource.nvidia.com","mbox","Cookie set by Adobe SiteCatalyst analytics software - used for measuring the performance of page content using A/B split testing","readme_language","vid","This cookie name 'vid' is used for tracking the search request made by the user on the 'Find a practitioner' page.","at_check","Used by Adobe Target to check if cookies are enabled/supported on the browser","blogs.nvidia.com","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",[672,673,674,675,676,677,678,679,680,681,682,683,684,685,686,687,688,689,690,691,692,693,694,695,696,697,698,699,700],[652,653,654,655],{"_399":667,"_209":667,"_180":181,"_372":668},{"_399":662,"_209":662,"_180":181,"_372":663},{"_399":461,"_209":461,"_180":181,"_372":659},{"_399":574,"_209":574,"_180":181,"_372":656},[657],{"_77":658,"_405":574,"_406":98,"_180":578},"_gh_sess",[660],{"_77":661,"_405":461,"_406":98,"_180":468},"YSC","bizible.com",[664],{"_77":665,"_405":662,"_406":34,"_180":666},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[669],{"_77":670,"_405":667,"_406":34,"_180":671},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_77":742,"_405":520,"_406":34,"_180":743},{"_77":727,"_405":741,"_406":98,"_180":728},{"_77":710,"_405":523,"_406":34,"_180":635},{"_77":710,"_405":623,"_406":34,"_180":635},{"_77":739,"_405":520,"_406":34,"_180":740},{"_77":727,"_405":526,"_406":98,"_180":728},{"_77":737,"_405":520,"_406":34,"_180":738},{"_77":727,"_405":523,"_406":98,"_180":728},{"_77":735,"_405":622,"_406":34,"_180":736},{"_77":727,"_405":644,"_406":98,"_180":728},{"_77":707,"_405":523,"_406":34,"_180":709},{"_77":733,"_405":520,"_406":98,"_180":734},{"_77":731,"_405":520,"_406":34,"_180":732},{"_77":729,"_405":520,"_406":98,"_180":730},{"_77":727,"_405":618,"_406":98,"_180":728},{"_77":725,"_405":520,"_406":34,"_180":726},{"_77":723,"_405":520,"_406":34,"_180":724},{"_77":721,"_405":520,"_406":98,"_180":722},{"_77":719,"_405":627,"_406":34,"_180":720},{"_77":707,"_405":618,"_406":34,"_180":709},{"_77":717,"_405":520,"_406":34,"_180":718},{"_77":715,"_405":520,"_406":98,"_180":716},{"_77":713,"_405":520,"_406":34,"_180":714},{"_77":711,"_405":520,"_406":34,"_180":712},{"_77":710,"_405":520,"_406":34,"_180":635},{"_77":707,"_405":708,"_406":34,"_180":709},{"_77":705,"_405":520,"_406":34,"_180":706},{"_77":703,"_405":520,"_406":98,"_180":704},{"_77":701,"_405":520,"_406":34,"_180":702},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. 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These cookies are required for the site to function and cannot be turned off.",[889,890,891,892,893,894,895,896,897,898,899,900,901,902,903,904,905,906,907,908,909,910,911,912,913,914,915,916,917,918,919,920,921,922,923,924,925,926,927,928,929,930,931,932,933,934,935,936,937,938,939,940,941,942,943,944,945,946,947,948,949,950,951,952,953,954,955,956,957,958,959,960,961],[749,750,751,752,753,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768,769,770,771,772,773],{"_399":886,"_209":886,"_180":181,"_372":887},{"_399":880,"_209":880,"_180":181,"_372":881},{"_399":877,"_209":877,"_180":181,"_372":878},{"_399":865,"_209":865,"_180":181,"_372":866},{"_399":862,"_209":862,"_180":181,"_372":863},{"_399":859,"_209":859,"_180":181,"_372":860},{"_399":854,"_209":854,"_180":181,"_372":855},{"_399":461,"_209":461,"_180":181,"_372":851},{"_399":848,"_209":848,"_180":181,"_372":849},{"_399":453,"_209":453,"_180":181,"_372":846},{"_399":448,"_209":448,"_180":181,"_372":844},{"_399":839,"_209":839,"_180":181,"_372":840},{"_399":834,"_209":834,"_180":181,"_372":835},{"_399":829,"_209":829,"_180":181,"_372":830},{"_399":826,"_209":826,"_180":181,"_372":827},{"_399":574,"_209":574,"_180":181,"_372":823},{"_399":433,"_209":433,"_180":181,"_372":820},{"_399":569,"_209":569,"_180":181,"_372":813},{"_399":565,"_209":565,"_180":181,"_372":807},{"_399":801,"_209":801,"_180":181,"_372":802},{"_399":796,"_209":796,"_180":181,"_372":797},{"_399":791,"_209":791,"_180":181,"_372":792},{"_399":787,"_209":787,"_180":181,"_372":788},{"_399":782,"_209":782,"_180":181,"_372":783},{"_399":774,"_209":774,"_180":181,"_372":775},"polyfill.io",[776,777],{"_77":780,"_405":774,"_406":34,"_180":781},{"_77":778,"_405":774,"_406":34,"_180":779},"cf_ob_info","This cookie is associated with sites using CloudFlare. 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