NER Training does not work when using BILOU tagging

NER training is not working per document/tutorials.

Specifically, offsets do appear to work. entity labels do not appear to work. Also the documentation is in conflict with itself which confuses the situation.

Using the entity-label NER training example

e.g.

nlp = spacy.load('en')
doc = Doc(nlp.vocab, [u'rats', u'make', u'good', u'pets'])
gold = GoldParse(doc, [u'U-ANIMAL', u'O', u'O', u'O'])
ner = EntityRecognizer(nlp.vocab, entity_types=['ANIMAL'])
ner.update(doc, gold)

As far as I know my syntax is correct. This doesn’t work either:

doc = Doc(nlp.vocab, words=["The", "Law", "and", "Justice", "party", "is", "growing", "in", "Poland"])
gold = GoldParse(doc, ['O', 'B-ORG', 'I-ORG', 'I-ORG', 'L-ORG', 'O', 'O', 'O', 'GPE'])
ner.update(doc, gold)

I get the following error:

TypeError Traceback (most recent call last) <ipython-input-30-ef64e41c324a> in <module>() ----> 1 ner.update(doc, gold)

/usr/local/lib/python3.5/dist-packages/spacy/syntax/parser.cpython-35m-x86_64-linux-gnu.so in spacy.syntax.parser.Parser.update (spacy/syntax/parser.cpp:7788)()

/usr/local/lib/python3.5/dist-packages/spacy/syntax/ner.cpython-35m-x86_64-linux-gnu.so in spacy.syntax.ner.BiluoPushDown.preprocess_gold (spacy/syntax/ner.cpp:4782)()

/usr/local/lib/python3.5/dist-packages/spacy/syntax/ner.cpython-35m-x86_64-linux-gnu.so in spacy.syntax.ner.BiluoPushDown.lookup_transition (spacy/syntax/ner.cpp:5145)()

TypeError: argument of type ‘NoneType’ is not iterable

I think this is a bug in GoldParse since offsets appear to work.

e.g.

ner = EntityRecognizer(nlp.vocab, entity_types=['ORG'])
doc = nlp.make_doc('The Law and Justice party is growing')
gold = GoldParse(doc, entities=[(4,25,'ORG')])
ner.update(doc, gold)
ner(doc)
print(doc.ents[0].text,doc.ents[0].label_)
--> Law and Justice party ORG

Also, the documentation is very inconsistent/confusing right now.

Conflicting examples:

1. https://spacy.io/docs/usage/training
2. https://spacy.io/docs/usage/entity-recognition
3. https://github.com/explosion/spaCy/blob/master/examples/training/train_ner.py

example 1 does not work example 2 works for token offsets, does not work for token-level entity annotation. example 3 is linked from 1 (as the ‘full example’), and they are totally different examples.

Your Environment

Ubuntu Python 3.5.2 1.2, latest PIP

[honnibal]

Fixed in v1.8.0 🎉

>>> nlp = spacy.load('en')
>>> from spacy.gold import GoldParse
>>> doc = nlp.make_doc(u'Facebook is a company')
>>> nlp.tagger(doc)
>>> gold = GoldParse(doc, entities=['U-ORG', 'O', 'O', 'O'])
>>> [t for t in gold.ner]
['U-ORG', 'O', 'O', 'O']
>>> nlp.entity.update(doc, gold)
1.0
>>> nlp.entity.update(doc, gold)
1.0
>>> nlp.entity.update(doc, gold)
0.0
>>> nlp.entity(doc)
>>> for ent in doc:
...   print(ent.text, ent.label_)
Facebook ORG
... 

[lock[bot]]

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