LASER
Language-Agnostic SEntence Representations (by facebookresearch)
flores
Facebook Low Resource (FLoRes) MT Benchmark (by facebookresearch)
| LASER | flores | |
|---|---|---|
| 5 | 8 | |
| 3,520 | 617 | |
| 0.3% | - | |
| 5.7 | 0.0 | |
| 7 days ago | over 1 year ago | |
| Jupyter Notebook | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
LASER
Posts with mentions or reviews of LASER.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2024-04-07.
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SentenceTransformers: Python framework for sentence, text and image embeddings
I'm curious how people are handling multi-lingual embeddings.
I've found LASER[1] which originally had the idea to embed all languages in the same vector space, though it's a bit harder to use than models available through SentenceTransformers. LASER2 stuck with this approach, but LASER3 switched to language-specific models. However, I haven't found benchmarks for these models, and they were released about 2 years ago.
Another alternative would be to translate everything before embedding, which would introduce some amount of error, though maybe it wouldn't be significant.
1. https://github.com/facebookresearch/LASER
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[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!
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!
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School project : sentiments analysis with my country Arabic Dialect
This may be helpful: https://github.com/facebookresearch/LASER
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[P] Bilingual text alignment tools for NMT - help needed
Check FB's LASER: https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix Also , Sentence-Transformers has a pretty neat model for crosslingual sentence similarity: https://huggingface.co/sentence-transformers/stsb-xlm-r-multilingual
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Help with aligned word embeddings
You want LASER its a superbig model trained on tons of languages you can use it with sentence_transformers in python to compute embedings. Then you can use faiss or datasketch to find matches at K
flores
Posts with mentions or reviews of flores.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-21.
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Opinion on Tatoeba corpus?
Only smaller ones - e.g Flores200 (https://huggingface.co/datasets/facebook/flores / https://github.com/facebookresearch/flores)
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[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!
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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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!
Open sourcing: By open sourcing our model and publishing a slew of research tools, we hope that AI researchers whose languages are not supported well or at all on commercial translations services could use our model to create support for that language. Furthermore, we’ve open sourced datasets, such as NLLB-Seed and FLORES-200 evaluation benchmark, which doubles the existing language coverage over our previous benchmark.
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No Language Left Behind
Dhivehi is currently not supported, unfortunately. We view this as a starting point and are committed to expanding to many other languages as in the spirit of our project name.
Full list of currently supported languages can be found here: https://github.com/facebookresearch/flores/tree/main/flores2...
What are some alternatives?
When comparing LASER and flores you can also consider the following projects:
MUSE - A library for Multilingual Unsupervised or Supervised word Embeddings
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
electra - ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
stopes - A library for preparing data for machine translation research (monolingual preprocessing, bitext mining, etc.) built by the FAIR NLLB team.
Arraymancer - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends