[D] Hey Reddit! We're a bunch of research scientists and software engineers and we just open sourced a new state-of-the-art AI model that can translate between 200 different languages. We're excited to hear your thoughts so we're hosting an AMA on 07/21/2022 @ 9:00AM PT. Ask Us Anything!

• fairseq

  89 29,476 5.5 Python

  Facebook AI Research Sequence-to-Sequence Toolkit written in Python.

  

Yes! We are really motivated by translation as an actual technology that people need (actually, part of our work was interviewing many different native speakers of low-resource languages). As part of that, we do experiment with distillation. That's detailed in Section 8.6 of our paper: https://arxiv.org/pdf/2207.04672.pdf where we compare two different distillation approaches. We also describe how we used distillation to create models that are serving Wikipedia's Content Translation tool (which you can use to write new Wikipedia articles), and then distillation of the full NLLB-200 model. These distilled models are available for download on github: https://github.com/facebookresearch/fairseq/tree/nllb/examples/nllb/modeling. For your question around productionization, we did partner with our production translation team to integrate the modeling techniques and learnings from the NLLB project into production translation. These are live on Facebook and Instagram today for some languages! [angela]

• flores

  8 617 0.0 Python

  Discontinued Facebook Low Resource (FLoRes) MT Benchmark

  

You can check out some of our materials and open sourced artifacts here:  - Our latest blog post: https://ai.facebook.com/blog/nllb-200-high-quality-machine-translation - Project Overview: https://ai.facebook.com/research/no-language-left-behind/  - Product demo: https://nllb.metademolab.com/  - Research paper: https://research.facebook.com/publications/no-language-left-behind - NLLB-200: https://github.com/facebookresearch/fairseq/tree/nllb - FLORES-200: https://github.com/facebookresearch/flores - LASER3: https://github.com/facebookresearch/LASER   Joining us today for the AMA are: - Angela Fan (AF), Research Scientist  - Jean Maillard (JM), Research Scientist - Maha Elbayad (ME), Research Scientist - Philipp Koehn (PK), Research Scientist - Shruti Bhosale (SB), Software Engineer   We’ll be here from 07/21/2022 @09:00AM PT - 10:00AM PT    Thanks and we’re looking forward to answering your questions!

• Scout Monitoring

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  Free Django app performance insights with Scout Monitoring. Get Scout setup in minutes, and let us sweat the small stuff. A couple lines in settings.py is all you need to start monitoring your apps. Sign up for our free tier today.

  

• LASER

  5 3,541 5.7 Jupyter Notebook

  Language-Agnostic SEntence Representations

  

You can check out some of our materials and open sourced artifacts here:  - Our latest blog post: https://ai.facebook.com/blog/nllb-200-high-quality-machine-translation - Project Overview: https://ai.facebook.com/research/no-language-left-behind/  - Product demo: https://nllb.metademolab.com/  - Research paper: https://research.facebook.com/publications/no-language-left-behind - NLLB-200: https://github.com/facebookresearch/fairseq/tree/nllb - FLORES-200: https://github.com/facebookresearch/flores - LASER3: https://github.com/facebookresearch/LASER   Joining us today for the AMA are: - Angela Fan (AF), Research Scientist  - Jean Maillard (JM), Research Scientist - Maha Elbayad (ME), Research Scientist - Philipp Koehn (PK), Research Scientist - Shruti Bhosale (SB), Software Engineer   We’ll be here from 07/21/2022 @09:00AM PT - 10:00AM PT    Thanks and we’re looking forward to answering your questions!

• stopes

  1 239 5.8 Python

  A library for preparing data for machine translation research (monolingual preprocessing, bitext mining, etc.) built by the FAIR NLLB team.

  

We have a bunch! The model and data are available here: https://github.com/facebookresearch/fairseq/tree/nllb/examples/nllb/modeling , LASER3 here: https://github.com/facebookresearch/fairseq/tree/nllb/examples/nllb/laser\_distillation , training data here: https://github.com/facebookresearch/fairseq/tree/nllb/examples/nllb/data , FLORES and our other human translated datasets here: https://github.com/facebookresearch/flores , and an entire modular pipeline for data cleaning here: https://github.com/facebookresearch/stopes. It's also available on HuggingFace! [angela]

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