Hugging Face

Hugging Face is an excellent starting point when working on NLP projects; it is also great for prototyping and developing pipelines for NLP tasks, being those tasks general like embedding representation or specific, like SQUAD models and datasets. It needs more phonetic models or datasets to be as advantageous in that regard.

If an organisation has more access to data and have access to high end computers like GPUs it’s recommended to use Hugging face as it will give better accuracy than any other models. If an organisation having less data and has less access to GPUsis looking for decent performance then traditional algorithms are more appropriate than hugging face

1. First point of start when looking to build something with transformer models. 2. Amazing community to handle your doubts / bugs. 3. Simple description of model and how to use it. 4. Never faced any bug related to size mismatch of weight, or wrong version. The weights and model always stay updated. 5. Very intuitive way to create apps using Gradio and one click deployment on Hugging Face spaces.

If your organization is looking for a fast turn around development time on natural language processing machine learning use case, and your organization is also well developed on cloud platforms such as google cloud or amazon web services, then implementing Hugging Face into your solution is a very good idea

Hugging Face is well suited for situations where you need access to specific advanced natural language processing libraries. The quality of the libraries available on the platform is extremely good and it can be used to make production level classification models. This is also great for building MVP use cases for NLP projects.

If you or organisation heavily work on machine learning complex neuronal network models Hugging Face has vast libraries transformers who makes the the model easier and efficient. it gives the ability to check the outcome and make changes accordingly and save a lot of time. If you're not working on machine learning neuronal network models it might not appropriate though try it.