edenai-apis VS Tatoeba-Challenge

HackerNoon featured our latest article in the "Future of AI" category

We explain how Eden AI contributes to the AI ecosystem in structuring AI and LLM APIs by creating the most accomplished Open-Source wrapper possible.

You can support us in reaching 1000 stars on Github here: https://github.com/edenai/edenai-apis

PS: if you can star our project, we'd really appreciate the support: https://github.com/edenai/edenai-apis

I'm working on a project that regroups all best AI (AIaaS) from different providers (GCP, AWS, Azure, DeepL, etc.) in one API.

If you don't want third party and feel the need of hanving your own accounts with the different providers, then you can use the open source version of the aggregator : https://github.com/edenai/edenai-apis

There are hundreds of companies doing that. The github repo regroups the best ones in one place.

Now we’re open sourcing our aggregation layer as a python module that you can ⭐ find on github ⭐ .

As it's said in the article, the library used is Open Source : https://github.com/edenai/edenai-apis/tree/master/edenai_apis

Automatically translate a text from a language A to a language B. 1/ Dataset : we chose a dataset from the Language Technology Research Group at the University of Helsinki’s Tatoeba Translation Challenge . We took 100 of examples from different latin languages pairs : deu-fra, eng-fra, fra -ita, deu-spa , deu-swe which constitutes a 500 example test dataset.

https://translatelocally.com/ is a nice gui around marian/bergamot. So far not very many bundled pairs, though I would guess any of the models from https://github.com/Helsinki-NLP/Opus-MT-train/tree/master/mo... and https://github.com/Helsinki-NLP/Tatoeba-Challenge/blob/maste... should be usable.

There is also Apertium, a rule-based system which is very good for some closely-related pairs that have had a lot of work put into them (especially translation between Romance languages, e.g. Spanish→Catalan, and Norwegian Bokmål→Nynorsk), and the only OK translator for some lesser-resourced languages (e.g. Northern Saami→Norwegian Bokmål), but very underdeveloped for anything to/from English (it feels a bit pointless writing rules for English where there is so much available data; RBMT shines where there's not enough available data, ie. most of the languages of the world)

#1: University of Helsinki language technology professor Jörg Tiedemann has released a dataset with over 500 million translated sentences in 188 languages | 0 comments #2: The NLP Index: 3,000+ code repos for hackers and researchers. [self-promotion] #3: A Python library to boost T5 models speed up to 5x & reduce the model size by 3x.

#1: Matching GPT-3's performance with just 0.1% of its parameters #2: University of Helsinki language technology professor Jörg Tiedemann has released a dataset with over 500 million translated sentences in 188 languages | 0 comments #3: Trained a Markov Chain on a bunch of r/WSB posts and comments. Only 2-word conditional probabilities but honestly, that's all that's necessary 🚀🚀