edenai-apis VS AutomaticKeyphraseExtraction

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

Keyword or Keyphrase Extraction is about being able to extract the words or phrases that most represent a given text. 1/ Dataset: we selected our datasets from the public github repository AutomaticKeyphraseExtraction Most of the datasets listed there were too long for the 4k token limit of OpenAI so we had to go with the Hulth2003 abstracts dataset. Since the different providers are trained to return keywords and keyphrases present in the original text, we did some cleaning to remove all keywords that were not present in the abstracts. We ended up with 470 abstracts.