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Field	Response
Participation considerations from adversely impacted groups (protected classes) in model design and testing:	Not Applicable
Measures taken to mitigate against unwanted bias:	Sourced diverse dataset from East Asia, South Asia, Latin America, and North America annotated by different gender.\" See https://www.arxiv-vanity.com/papers/2106.03193/ for more details.

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Translation: Megatron 1.1B

Model Overview

This model can be used for translating text in source language (32 languages) 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]. In this particular instance, the model has 12 layers in the encoder and 2 layers in the decoder. It is using SentencePiece tokenizer [2].

Training

These models were trained on a collection of many publicly available datasets comprising of millions of parallel sentences.

Tokenizer Construction

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

How to Use this Model

The Riva Quick Start Guide is recommended as the starting point for trying out Riva models. For more information on using this model with Riva Speech Services, refer to the Riva User Guide.

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.

References

[1] Vaswani, Ashish, et al. \"Attention is all you need.\" arXiv preprint arXiv:1706.03762 (2017).\n[2] https://github.com/google/sentencepiece\n[3] https://en.wikipedia.org/wiki/BLEU\n[4] https://github.com/mjpost/sacreBLEU\n[5] NVIDIA NeMo Toolkit

License

By downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva license.

Ethical AI

NVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.

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In this particular instance, the model has 12 layers in the encoder and 2 layers in the decoder. It is using SentencePiece tokenizer [2].\n\n### Training\n\nThese models were trained on a collection of many publicly available datasets comprising of millions of parallel sentences.\n\n#### Tokenizer Construction\n\nWe used the SentencePiece tokenizer [2] with shared encoder and decoder BPE tokenizers.\n\n#### How to Use this Model\n\nThe [Riva Quick Start Guide](https://ngc.nvidia.com/catalog/resources/nvidia:riva:riva_quickstart) is recommended as the starting point for trying out Riva models. For more information on using this model with Riva Speech Services, refer to the [Riva User Guide](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/tutorials-nmt-contents.html).\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### References\n\n[1] Vaswani, Ashish, et al. \"Attention is all you need.\" arXiv preprint arXiv:1706.03762 (2017).\n[2] https://github.com/google/sentencepiece\n[3] https://en.wikipedia.org/wiki/BLEU\n[4] https://github.com/mjpost/sacreBLEU\n[5] NVIDIA NeMo Toolkit\n\n### Suggested Reading\n\nRefer to the [Riva documentation](https://docs.nvidia.com/deeplearning/riva/user-guide/docs/index.html) for more information.\n\n### License\n\nBy downloading and using the models and resources packaged with Riva Conversational AI, you accept the terms of the Riva [license](https://www.nvidia.com/en-us/data-center/products/nvidia-ai-enterprise/eula/).\n\n### Ethical AI\n\nNVIDIA’s platforms and application frameworks enable developers to build a wide array of AI applications. Consider potential algorithmic bias when choosing or creating the models being deployed. Work with the model’s developer to ensure that it meets the requirements for the relevant industry and use case; that the necessary instruction and documentation are provided to understand error rates, confidence intervals, and results; and that the model is being used under the conditions and in the manner intended.","displayName","updatedDate",["D",1717439634073],"publisher","","latestVersion","2.14.0","versions","collectionCounts","imageUrl","/api/image/nvidia/riva/models/riva_megatronnmt_any_en_1b","rawImageUrl","canGuestDownload","latestVersionSizeInBytes",2178060409,"licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","Field | Response\n:---------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nParticipation considerations from adversely impacted groups ([protected classes](https://www.senate.ca.gov/content/protected-classes)) in model design and testing: | Not Applicable\nMeasures taken to mitigate against unwanted bias: | Sourced diverse dataset from East Asia, South Asia, Latin America, and North America annotated by different gender.\" See https://www.arxiv-vanity.com/papers/2106.03193/ for more details.","explainability","Field | Response\n:------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------\nIntended Applications & Domains: | Text and Document Translation\nTypes: | Text translation\nIntended Users: | This model is intended for our developers to perform text and document translation.\nOutput: | Text (in Target Language)\nDescribe how the model works: | Model translates text in one language into in target language.\nName the adversely impacted groups this has been tested to deliver comparable outcomes regardless of: | Not Applicable\nTechnical Limitations: | Translations may not be 100% accurate. Accuracy varies based on the characteristics of input (Domain, Use Case, Noise, Context, etcetera). Grammar errors and semantic issues may be present.\nVerified to have met prescribed NVIDIA quality standards: | Yes\nPerformance Metrics: | BiLingual Evaluation Understudy (BLEU) Scores, Crosslingual Optimized Metric for Evaluation of Translation (COMET)\nPotential Known Risks: | Model only supports 2-letter language codes and not Internet Engineering Task Force BCP-47 codes so this model may not return translations specific to a region.\nLicensing: | [Riva](https:developer.nvidia.com/riva/ga/license)","privacy","Field | Response\n:----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------\nGeneratable or reverse engineerable personally-identifiable information (PII)? | None\nWas consent obtained for any PII used? | Not Applicable\nProtected class data used to create this model? | Not Applicable\nHow often is dataset reviewed? | Before Release\nIs a mechanism in place to honor data subject right of access or deletion of personal data? | No\nIf PII collected for the development of the model, was it collected directly by NVIDIA? | Not Applicable\nIf PII collected for the development of the model by NVIDIA, do you maintain or have access to disclosures made to data subjects? | Not Applicable\nIf PII collected for the development of this AI model, was it minimized to only what was required? | Not Applicable\nIs there provenance for all datasets used in training? | Yes\nDoes data labeling (annotation, metadata) comply with privacy laws? | Yes\nIs data compliant with data subject requests for data correction or removal, if such a request was made? | No, not possible with externally-sourced data.","safetyAndSecurity","Field | Response\n:---------------------------------------------------|:----------------------------------\nModel Application(s): | Language Translation\nDescribe the life-critical impacts (if present). | Not Applicable\nUse Case Restriction(s): | Abide by https://developer.nvidia.com/riva/ga/license\nModel and Dataset Restriction(s): | The Principle of least privilege (PoLP) is applied limiting access for dataset generation and model development. 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