Thanks a lot for your comment! I only had a vague idea and little experience on how to do all this, so your advice really helped me lay things out and start making a concrete plan. Will be doing tests and comparing metrics for sure, and let's hope my supervisor will accept this as a legit way of spending my time...

>You didn't really answer my question what parts of your pipeline you want to try to move.

...almost the whole pipeline? Reading in examples, batching them, tokenization, encoding them into tensors, training, saving, loading for prediction are all built under the framework.

>But in general AllenNLP is for quite some time now already irrelevant in the space, Id suggest to move to Huggingface asap.

Yeah that's the vibe I'm getting, hence the post. Thanks for your suggestions though!

I have a project in development that's using AllenNLP and I hope to move it out of the framework. My main concern is ensuring everything works like before when I switch over (e.g. the tokenizer, the encoder, the whole data "pipeline")

(Edit: I'm also not sure if I should dig into their source code and compare it with Huggingface to ensure everything works as before under the hood, since reproducibility is really important and all)

Ah of course, I've been using Huggingface here and there; I think AllenNLP uses some of it in their code too. I guess I should have stuck to using 🤗 and nothing else to begin with lol

The main problem really is how to disentangle my model from AllenNLP without breaking it.