spaCy >= 2.0 support about errant HOT 4 CLOSED

Yes, that's using en_core_web_sm in both cases.

From what I've read, it was never supposed to be faster, just slightly more accurate and memory efficient. Check the "Model Comparison" table on this page. It shows a large speed drop for a relatively small performance gain. It's true that the parser and NER components are >5% more accurate, but ERRANT mainly relies on the POS tagger, so the ~0.5% POS improvement isn't really significant.

There's also a long issue thread about it here when it went from v1 to v2 , and it seemed to me that the conclusion was that it'll never be as fast as v1.

from errant.

Hey Sam,

Yes, Spacy 2 support is definitely on the to-do list. I mainly wanted the first pip version to be compatible with the BEA shared task, but newer versions will change the results slightly.

Some good news: Spacy finally updated their English tag map to the same one that I use, so as long as you use spacy >= 2.2.2, rule compatibility shouldn't be a problem. I'm in the process of testing ERRANT with this version of spacy too, so hopefully ERRANT 2.1 will come out soon!

from errant.

Quick update:

I tried using ERRANT with the latest version of spacy (2.2), and the only thing that broke is a call to an old lemmatiser in the classifier. For a quick fix, you can change the same_lemma function to:

if o_tok.lemma == c_tok.lemma: return True
return False

Otherwise, it looked as if annotation performance decreased by about 1% and processing time took about 3 times longer. I'll need to debug the accuracy loss (and have some ideas already), but there's not really anything I can do about the speed loss...

from errant.

Thanks for the update.

The performance thing is interesting, since spacy2.0 was supposed to be faster... In both cases, is this using en_core_web_sm?

from errant.