The model names have nothing to do with how they are trained. It’s similar to the names of docker containers. Something similar to: moby/names-generator.go at master · moby/moby · GitHub 1



 ssam9:

It makes AutoNLP look a bit less credible if we were to recommend the use of these models in a company’s project.


Unfortunately, I have to disagree  Unlike many other AutoML tools, AutoNLP provides you with all the trained models in the end. You have the weights, you have the tokenizer. You are free to use these models for many different purposes: using them directly with Hugging Face’s API Inference (thus saving several days and sometimes even months’ worth of engineering time) or for further fine-tuning of the models (still saving several days of work). AutoNLP provides you with the best possible models so that one doesn’t have to dig in themselves. I’m personally not aware of any tools that can train tens or even hundreds of SOTA transformer models which are production-ready  And that’s why many enterprises already use and love AutoNLP  You can read some of the testimonials here: Announcing AutoNLP – Hugging Face.
If you have any further concerns about how AutoNLP can be used in your industry, please feel free to write at autonlp [at] huggingface [dot] co and we can discuss further.