Can you please give an example using this model for a complete pipeline?

[deepdad]

Can you please give an example using this model for a complete pipeline?

How to reproduce the behaviour

nlp = spacy.load('en_vectors_web_lg')
print(nlp.pipeline)

[]
and only the tokens are available:

for tok in doc:
        token_i +=1
        ann_toks_deps_tags.append((tok.text, tok.tag_, tok.dep_))

[('Only', '', ''), ('the', '', ''), ('tokens', '', ''), ('are', '', ''), ('available', '', '')('.', '', '')]

using any of

nlp.add_pipe(nlp.create_pipe('tokenizer'))
nlp.add_pipe(nlp.create_pipe('tagger'))
nlp.add_pipe(nlp.create_pipe('parser'))

results in errors like:

  File "/usr/local/lib/python3.6/dist-packages/spacy/language.py", line 346, in __call__
    doc = proc(doc)
TypeError: Argument 'string' has incorrect type (expected str, got spacy.tokens.doc.Doc)

Or,

cat test3.py 

import spacy
spacy.prefer_gpu()
nlp = spacy.load('en_vectors_web_lg')
print(nlp.pipeline) # []
nlp.add_pipe(nlp.create_pipe('tensorizer'))
nlp.add_pipe(nlp.create_pipe('tokenizer'))
nlp.add_pipe(nlp.create_pipe('tagger'))
nlp.add_pipe(nlp.create_pipe('parser'))
nlp.add_pipe(nlp.create_pipe('sentencizer'))
nlp.add_pipe(nlp.create_pipe('merge_noun_chunks'))
nlp.add_pipe(nlp.create_pipe('merge_entities'))

print(nlp.pipeline) #list of the above

doc = nlp(u'Do something every day.')
for tok in doc:
    token_i +=1
    print((tok.text, tok.tag_, tok.dep_))

reults in

python3 test3.py 
[]
[('tensorizer', <spacy.pipeline.Tensorizer object at 0x7f2b9575beb8>), ('Tokenizer', <spacy.tokenizer.Tokenizer object at 0x7f2b957699a8>), ('tagger', <spacy.pipeline.Tagger object at 0x7f2af258ce10>), ('parser', <spacy.pipeline.DependencyParser object at 0x7f2af257de08>), ('sbd', <spacy.pipeline.SentenceSegmenter object at 0x7f2af258cc50>), ('merge_noun_chunks', <built-in function merge_noun_chunks>), ('merge_entities', <built-in function merge_entities>)]
Traceback (most recent call last):
  File "test3.py", line 15, in <module>
    doc = nlp(u'Do something every day.')
  File "/usr/local/lib/python3.6/dist-packages/spacy/language.py", line 346, in __call__
    doc = proc(doc)
  File "pipeline.pyx", line 305, in spacy.pipeline.Tensorizer.__call__
  File "pipeline.pyx", line 329, in spacy.pipeline.Tensorizer.predict
AttributeError: 'bool' object has no attribute 'ops'

Can you please give an example using this model for a complete pipeline, including sentencizer, parser, tagger, tokenizer, etc.?

Your Environment

• Operating System: Ubuntu 18 (I did have some issues during installation)

• Python Version Used: 3.6

• spaCy Version Used: v2.0.16

• Environment Information:
  CUDA 10, Tesla V100
  Installed models (spaCy v2.0.16)
  /usr/local/lib/python3.6/dist-packages/spacy

  TYPE NAME MODEL VERSION
  package en-vectors-web-lg en_vectors_web_lg 2.0.0 ✔
  package en-core-web-lg en_core_web_lg 2.0.0 ✔
  link en_vectors_web_lg en_vectors_web_lg 2.0.0 ✔
  link en_core_web_lg en_core_web_lg 2.0.0 ✔

[honnibal]

You need to use a pipeline with pre-trained models in order to use those components, or you need to train one yourself. There are many examples at https://spacy.io/usage/

[lock]

This thread has been automatically locked since there has not been any recent activity after it was closed. Please open a new issue for related bugs.