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Add warning for misaligned character offset spans (#5007)
* Add warning for misaligned character offset spans * Resolve conflict * Filter warnings in example scripts Filter warnings in example scripts to show warnings once, in particular warnings about misaligned entities. Co-authored-by: Ines Montani <ines@ines.io>
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@ -1,6 +1,7 @@
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"""Prevent catastrophic forgetting with rehearsal updates."""
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import plac
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import random
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import warnings
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import srsly
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import spacy
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from spacy.gold import GoldParse
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@ -66,7 +67,10 @@ def main(model_name, unlabelled_loc):
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pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
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sizes = compounding(1.0, 4.0, 1.001)
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with nlp.disable_pipes(*other_pipes):
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with nlp.disable_pipes(*other_pipes) and warnings.catch_warnings():
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# show warnings for misaligned entity spans once
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warnings.filterwarnings("once", category=UserWarning, module='spacy')
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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random.shuffle(raw_docs)
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@ -8,12 +8,13 @@ For more details, see the documentation:
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* NER: https://spacy.io/usage/linguistic-features#named-entities
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Compatible with: spaCy v2.0.0+
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Last tested with: v2.1.0
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Last tested with: v2.2.4
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"""
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from __future__ import unicode_literals, print_function
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import plac
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import random
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import warnings
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from pathlib import Path
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import spacy
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from spacy.util import minibatch, compounding
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@ -57,7 +58,11 @@ def main(model=None, output_dir=None, n_iter=100):
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# get names of other pipes to disable them during training
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pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
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with nlp.disable_pipes(*other_pipes): # only train NER
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# only train NER
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with nlp.disable_pipes(*other_pipes) and warnings.catch_warnings():
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# show warnings for misaligned entity spans once
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warnings.filterwarnings("once", category=UserWarning, module='spacy')
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# reset and initialize the weights randomly – but only if we're
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# training a new model
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if model is None:
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@ -24,12 +24,13 @@ For more details, see the documentation:
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* NER: https://spacy.io/usage/linguistic-features#named-entities
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Compatible with: spaCy v2.1.0+
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Last tested with: v2.1.0
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Last tested with: v2.2.4
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"""
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from __future__ import unicode_literals, print_function
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import plac
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import random
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import warnings
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from pathlib import Path
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import spacy
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from spacy.util import minibatch, compounding
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@ -97,7 +98,11 @@ def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
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# get names of other pipes to disable them during training
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pipe_exceptions = ["ner", "trf_wordpiecer", "trf_tok2vec"]
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
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with nlp.disable_pipes(*other_pipes): # only train NER
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# only train NER
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with nlp.disable_pipes(*other_pipes) and warnings.catch_warnings():
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# show warnings for misaligned entity spans once
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warnings.filterwarnings("once", category=UserWarning, module='spacy')
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sizes = compounding(1.0, 4.0, 1.001)
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# batch up the examples using spaCy's minibatch
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for itn in range(n_iter):
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@ -110,7 +110,11 @@ class Warnings(object):
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"in problems with the vocab further on in the pipeline.")
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W029 = ("Unable to align tokens with entities from character offsets. "
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"Discarding entity annotation for the text: {text}.")
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W030 = ("Some entities could not be aligned in the text \"{text}\" with "
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"entities \"{entities}\". Use "
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"`spacy.gold.biluo_tags_from_offsets(nlp.make_doc(text), entities)`"
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" to check the alignment. Misaligned entities ('-') will be "
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"ignored during training.")
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@add_codes
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class Errors(object):
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@ -957,6 +957,12 @@ def biluo_tags_from_offsets(doc, entities, missing="O"):
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break
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else:
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biluo[token.i] = missing
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if "-" in biluo:
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ent_str = str(entities)
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warnings.warn(Warnings.W030.format(
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text=doc.text[:50] + "..." if len(doc.text) > 50 else doc.text,
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entities=ent_str[:50] + "..." if len(ent_str) > 50 else ent_str
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))
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return biluo
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@ -56,7 +56,8 @@ def test_gold_biluo_misalign(en_vocab):
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spaces = [True, True, True, True, True, False]
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doc = Doc(en_vocab, words=words, spaces=spaces)
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entities = [(len("I flew to "), len("I flew to San Francisco Valley"), "LOC")]
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tags = biluo_tags_from_offsets(doc, entities)
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with pytest.warns(UserWarning):
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tags = biluo_tags_from_offsets(doc, entities)
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assert tags == ["O", "O", "O", "-", "-", "-"]
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