mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-27 10:26:35 +03:00
175 lines
7.0 KiB
Python
175 lines
7.0 KiB
Python
#!/usr/bin/env python
|
|
# coding: utf8
|
|
|
|
"""Example of training spaCy's entity linker, starting off with a predefined
|
|
knowledge base and corresponding vocab, and a blank English model.
|
|
|
|
For more details, see the documentation:
|
|
* Training: https://spacy.io/usage/training
|
|
* Entity Linking: https://spacy.io/usage/linguistic-features#entity-linking
|
|
|
|
Compatible with: spaCy v2.2.4
|
|
Last tested with: v2.2.4
|
|
"""
|
|
from __future__ import unicode_literals, print_function
|
|
|
|
import plac
|
|
import random
|
|
from pathlib import Path
|
|
|
|
from spacy.vocab import Vocab
|
|
import spacy
|
|
from spacy.kb import KnowledgeBase
|
|
from spacy.pipeline import EntityRuler
|
|
from spacy.util import minibatch, compounding
|
|
|
|
|
|
def sample_train_data():
|
|
train_data = []
|
|
|
|
# Q2146908 (Russ Cochran): American golfer
|
|
# Q7381115 (Russ Cochran): publisher
|
|
|
|
text_1 = "Russ Cochran his reprints include EC Comics."
|
|
dict_1 = {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}
|
|
train_data.append((text_1, {"links": dict_1}))
|
|
|
|
text_2 = "Russ Cochran has been publishing comic art."
|
|
dict_2 = {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}
|
|
train_data.append((text_2, {"links": dict_2}))
|
|
|
|
text_3 = "Russ Cochran captured his first major title with his son as caddie."
|
|
dict_3 = {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}
|
|
train_data.append((text_3, {"links": dict_3}))
|
|
|
|
text_4 = "Russ Cochran was a member of University of Kentucky's golf team."
|
|
dict_4 = {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}
|
|
train_data.append((text_4, {"links": dict_4}))
|
|
|
|
return train_data
|
|
|
|
|
|
# training data
|
|
TRAIN_DATA = sample_train_data()
|
|
|
|
|
|
@plac.annotations(
|
|
kb_path=("Path to the knowledge base", "positional", None, Path),
|
|
vocab_path=("Path to the vocab for the kb", "positional", None, Path),
|
|
output_dir=("Optional output directory", "option", "o", Path),
|
|
n_iter=("Number of training iterations", "option", "n", int),
|
|
)
|
|
def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
|
|
"""Create a blank model with the specified vocab, set up the pipeline and train the entity linker.
|
|
The `vocab` should be the one used during creation of the KB."""
|
|
vocab = Vocab().from_disk(vocab_path)
|
|
# create blank English model with correct vocab
|
|
nlp = spacy.blank("en", vocab=vocab)
|
|
nlp.vocab.vectors.name = "nel_vectors"
|
|
print("Created blank 'en' model with vocab from '%s'" % vocab_path)
|
|
|
|
# Add a sentencizer component. Alternatively, add a dependency parser for higher accuracy.
|
|
nlp.add_pipe(nlp.create_pipe("sentencizer"))
|
|
|
|
# Add a custom component to recognize "Russ Cochran" as an entity for the example training data.
|
|
# Note that in a realistic application, an actual NER algorithm should be used instead.
|
|
ruler = EntityRuler(nlp)
|
|
patterns = [
|
|
{"label": "PERSON", "pattern": [{"LOWER": "russ"}, {"LOWER": "cochran"}]}
|
|
]
|
|
ruler.add_patterns(patterns)
|
|
nlp.add_pipe(ruler)
|
|
|
|
# Create the Entity Linker component and add it to the pipeline.
|
|
if "entity_linker" not in nlp.pipe_names:
|
|
kb = KnowledgeBase(vocab=nlp.vocab)
|
|
kb.load_bulk(kb_path)
|
|
print("Loaded Knowledge Base from '%s'" % kb_path)
|
|
|
|
# use only the predicted EL score and not the prior probability (for demo purposes)
|
|
cfg = {"kb": kb, "incl_prior": False}
|
|
entity_linker = nlp.create_pipe("entity_linker", cfg)
|
|
nlp.add_pipe(entity_linker, last=True)
|
|
|
|
# Convert the texts to docs to make sure we have doc.ents set for the training examples.
|
|
# Also ensure that the annotated examples correspond to known identifiers in the knowledge base.
|
|
kb_ids = nlp.get_pipe("entity_linker").kb.get_entity_strings()
|
|
TRAIN_DOCS = []
|
|
for text, annotation in TRAIN_DATA:
|
|
with nlp.select_pipes(disable="entity_linker"):
|
|
doc = nlp(text)
|
|
annotation_clean = annotation
|
|
for offset, kb_id_dict in annotation["links"].items():
|
|
new_dict = {}
|
|
for kb_id, value in kb_id_dict.items():
|
|
if kb_id in kb_ids:
|
|
new_dict[kb_id] = value
|
|
else:
|
|
print(
|
|
"Removed", kb_id, "from training because it is not in the KB."
|
|
)
|
|
annotation_clean["links"][offset] = new_dict
|
|
TRAIN_DOCS.append((doc, annotation_clean))
|
|
|
|
with nlp.select_pipes(enable="entity_linker"): # only train entity linker
|
|
# reset and initialize the weights randomly
|
|
optimizer = nlp.begin_training()
|
|
|
|
for itn in range(n_iter):
|
|
random.shuffle(TRAIN_DOCS)
|
|
losses = {}
|
|
# batch up the examples using spaCy's minibatch
|
|
batches = minibatch(TRAIN_DOCS, size=compounding(4.0, 32.0, 1.001))
|
|
for batch in batches:
|
|
nlp.update(
|
|
batch,
|
|
drop=0.2, # dropout - make it harder to memorise data
|
|
losses=losses,
|
|
sgd=optimizer,
|
|
)
|
|
print(itn, "Losses", losses)
|
|
|
|
# test the trained model
|
|
_apply_model(nlp)
|
|
|
|
# save model to output directory
|
|
if output_dir is not None:
|
|
output_dir = Path(output_dir)
|
|
if not output_dir.exists():
|
|
output_dir.mkdir()
|
|
nlp.to_disk(output_dir)
|
|
print()
|
|
print("Saved model to", output_dir)
|
|
|
|
# test the saved model
|
|
print("Loading from", output_dir)
|
|
nlp2 = spacy.load(output_dir)
|
|
_apply_model(nlp2)
|
|
|
|
|
|
def _apply_model(nlp):
|
|
for text, annotation in TRAIN_DATA:
|
|
# apply the entity linker which will now make predictions for the 'Russ Cochran' entities
|
|
doc = nlp(text)
|
|
print()
|
|
print("Entities", [(ent.text, ent.label_, ent.kb_id_) for ent in doc.ents])
|
|
print("Tokens", [(t.text, t.ent_type_, t.ent_kb_id_) for t in doc])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
plac.call(main)
|
|
|
|
# Expected output (can be shuffled):
|
|
|
|
# Entities[('Russ Cochran', 'PERSON', 'Q7381115')]
|
|
# Tokens[('Russ', 'PERSON', 'Q7381115'), ('Cochran', 'PERSON', 'Q7381115'), ("his", '', ''), ('reprints', '', ''), ('include', '', ''), ('The', '', ''), ('Complete', '', ''), ('EC', '', ''), ('Library', '', ''), ('.', '', '')]
|
|
|
|
# Entities[('Russ Cochran', 'PERSON', 'Q7381115')]
|
|
# Tokens[('Russ', 'PERSON', 'Q7381115'), ('Cochran', 'PERSON', 'Q7381115'), ('has', '', ''), ('been', '', ''), ('publishing', '', ''), ('comic', '', ''), ('art', '', ''), ('.', '', '')]
|
|
|
|
# Entities[('Russ Cochran', 'PERSON', 'Q2146908')]
|
|
# Tokens[('Russ', 'PERSON', 'Q2146908'), ('Cochran', 'PERSON', 'Q2146908'), ('captured', '', ''), ('his', '', ''), ('first', '', ''), ('major', '', ''), ('title', '', ''), ('with', '', ''), ('his', '', ''), ('son', '', ''), ('as', '', ''), ('caddie', '', ''), ('.', '', '')]
|
|
|
|
# Entities[('Russ Cochran', 'PERSON', 'Q2146908')]
|
|
# Tokens[('Russ', 'PERSON', 'Q2146908'), ('Cochran', 'PERSON', 'Q2146908'), ('was', '', ''), ('a', '', ''), ('member', '', ''), ('of', '', ''), ('University', '', ''), ('of', '', ''), ('Kentucky', '', ''), ("'s", '', ''), ('golf', '', ''), ('team', '', ''), ('.', '', '')]
|