SpaCy 3 support

I have SpaCy 3 installed:

$ spacy info

============================== Info about spaCy ==============================

spaCy version    3.0.3                         
Location         /opt/anaconda3/envs/XXX/lib/python3.8/site-packages/spacy
Platform         Linux-5.4.0-65-generic-x86_64-with-glibc2.10
Python version   3.8.5                         
Pipelines        en_core_web_sm (3.0.0)        

I have installed spacy-wordnet:

$ pip install spacy-wordnet

Collecting spacy-wordnet
  Downloading spacy-wordnet-0.0.4.tar.gz (648 kB)
     |████████████████████████████████| 648 kB 6.5 MB/s 
Collecting nltk<3.4,>=3.3
  Downloading nltk-3.3.0.zip (1.4 MB)
     |████████████████████████████████| 1.4 MB 9.8 MB/s 
Requirement already satisfied: six in /opt/anaconda3/envs/syllabus/lib/python3.8/site-packages (from nltk<3.4,>=3.3->spacy-wordnet) (1.15.0)
Building wheels for collected packages: spacy-wordnet, nltk
  Building wheel for spacy-wordnet (setup.py) ... done
  Created wheel for spacy-wordnet: filename=spacy_wordnet-0.0.4-py2.py3-none-any.whl size=650293 sha256=73e3b3c9921a3a9fb841638b1fd3ab4d6442563a7cab510c5c663fa74849e242
  Stored in directory: /home/XXX/.cache/pip/wheels/d5/4a/26/0311c16a5294b36a6e018c0816f9e61a5377287fdd276e0f5c
  Building wheel for nltk (setup.py) ... done
  Created wheel for nltk: filename=nltk-3.3-py3-none-any.whl size=1394469 sha256=75e771dc87340388bb5d475c1732827b2987617b059307b5ff6d123bb75bf656
  Stored in directory: /home/XXX/.cache/pip/wheels/19/1d/3a/0a8c14c30132b4f9ffd796efbb6746f15b3d6bcfc1055a9346
Successfully built spacy-wordnet nltk
Installing collected packages: nltk, spacy-wordnet
Successfully installed nltk-3.3 spacy-wordnet-0.0.4

And tried to use it in Python:

import spacy
from spacy_wordnet.wordnet_annotator import WordnetAnnotator

nlp = spacy.load("en_core_web_sm")
nlp.add_pipe('WordnetAnnotator', after="tagger")
---------------------------------------------------------------------------                          
ValueError                                Traceback (most recent call last)
<ipython-input-13-a2750d534e73> in <module>
----> 1 nlp.add_pipe('WordnetAnnotator', after="tagger")

/opt/anaconda3/envs/XXX/lib/python3.8/site-packages/spacy/language.py in add_pipe(self, factory_name, name, before, after, first, last, source, config, raw_config, validate)
    765                     lang_code=self.lang,
    766                 )                         
--> 767             pipe_component = self.create_pipe(
    768                 factory_name,
    769                 name=name,

/opt/anaconda3/envs/XXX/lib/python3.8/site-packages/spacy/language.py in create_pipe(self, factory_name, name, config, raw_config, validate)
    636                 lang_code=self.lang,
    637             )                             
--> 638             raise ValueError(err)
    639         pipe_meta = self.get_factory_meta(factory_name)
    640         config = config or {}

ValueError: [E002] Can't find factory for 'WordnetAnnotator' for language English (en). This usually happens when spaCy calls `nlp.create_pipe` with a custom component name that's not registered on the current language class. If you're using a Transformer, make sure to install 'spacy-transformers'. If you're using a custom component, make sure you've added the decorator `@Language.component` (for function components) or `@Language.factory` (for class components).                                        

Available factories: attribute_ruler, tok2vec, merge_noun_chunks, merge_entities, merge_subtokens, token_splitter, parser, beam_parser, entity_linker, ner, beam_ner, entity_ruler, lemmatizer, tagger, morphologizer, senter, sentencizer, textcat, textcat_multilabel, en.lemmatizer                          

I suppose that SpaCy 3 is not yet supported.

[philipp-kohl]

Hi,

spacy changed their custom pipeline creation method. A wrapper worked for me (see @Language.component(“wordnet”) and nlp.add_pipe(…)):

from spacy import Language
import spacy
from spacy_wordnet.wordnet_annotator import WordnetAnnotator


# Load an spacy model (supported models are "es" and "en")
nlp = spacy.load('en_core_web_sm')

spacy_wordnet_annotator = WordnetAnnotator(nlp.lang)
@Language.component("wordnet")
def spacy_wordnet_wrapper(doc):
    return spacy_wordnet_annotator(doc)

nlp.add_pipe("wordnet", after='tagger')
token = nlp('prices')[0]

# wordnet object link spacy token with nltk wordnet interface by giving acces to
# synsets and lemmas
token._.wordnet.synsets()
token._.wordnet.lemmas()

# And automatically tags with wordnet domains
token._.wordnet.wordnet_domains()

[frascuchon]

Fixed in #10