summarization

Problem
Currently FARMReader will ask users to raise max_seq_length every time some samples are longer than the value set to it. However, this can be confusing if max_seq_length is already set to the maximum value allowed by the model, because raising it further will cause hard-to-read CUDA errors.

See #2177.

Solution
We should find a way to query the model for the maximum va

Intro

I am getting TypeError: can not serialize 'BaseTextRank' object when trying to use spaCy's multiprocessing in nlp.pipe with a textrank pipeline component.

Sorry if this a known/expected feature/limitation - I couldn't find anything by searching repo. I generally find (spaCy's) multiprocessing a bit temperamental anyhow, but this seems to just not work.

_PS. thanks for all

Need help for retraining and cross validation and see if the ROUGE score matches exactly (or better) with the numbers reported in the paper.
I just train for 500k iteration (with batch size 8) with pointer generation enabled + coverage loss disabled and next 100k iteration (with batch size 8) with pointer generation enabled + coverage loss enabled.

It would be great if someone can help re-r

A next step for better generation is to implement a beam search for the generation. An example of it can be seen on the huggingface repo here, and this would need adding such a function to the GenerativeT5 model in onnxt5/models.py