spaCy/examples/training/train_entity_linker.py
Sofie Van Landeghem 0ba1b5eebc CLI scripts for entity linking (wikipedia & generic) (#4091)
* document token ent_kb_id

* document span kb_id

* update pipeline documentation

* prior and context weights as bool's instead

* entitylinker api documentation

* drop for both models

* finish entitylinker documentation

* small fixes

* documentation for KB

* candidate documentation

* links to api pages in code

* small fix

* frequency examples as counts for consistency

* consistent documentation about tensors returned by predict

* add entity linking to usage 101

* add entity linking infobox and KB section to 101

* entity-linking in linguistic features

* small typo corrections

* training example and docs for entity_linker

* predefined nlp and kb

* revert back to similarity encodings for simplicity (for now)

* set prior probabilities to 0 when excluded

* code clean up

* bugfix: deleting kb ID from tokens when entities were removed

* refactor train el example to use either model or vocab

* pretrain_kb example for example kb generation

* add to training docs for KB + EL example scripts

* small fixes

* error numbering

* ensure the language of vocab and nlp stay consistent across serialization

* equality with =

* avoid conflict in errors file

* add error 151

* final adjustements to the train scripts - consistency

* update of goldparse documentation

* small corrections

* push commit

* turn kb_creator into CLI script (wip)

* proper parameters for training entity vectors

* wikidata pipeline split up into two executable scripts

* remove context_width

* move wikidata scripts in bin directory, remove old dummy script

* refine KB script with logs and preprocessing options

* small edits

* small improvements to logging of EL CLI script
2019-08-13 15:38:59 +02:00

174 lines
6.8 KiB
Python

#!/usr/bin/env python
# coding: utf8
"""Example of training spaCy's entity linker, starting off with an
existing model and a pre-defined knowledge base.
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 vX.X
Last tested with: vX.X
"""
from __future__ import unicode_literals, print_function
import plac
import random
from pathlib import Path
from spacy.symbols import PERSON
from spacy.vocab import Vocab
import spacy
from spacy.kb import KnowledgeBase
from spacy import Errors
from spacy.tokens import Span
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 Language class with correct vocab
nlp = spacy.blank("en", vocab=vocab)
nlp.vocab.vectors.name = "spacy_pretrained_vectors"
print("Created blank 'en' model with vocab from '%s'" % vocab_path)
# create the built-in pipeline components and add them to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
if "entity_linker" not in nlp.pipe_names:
entity_linker = nlp.create_pipe("entity_linker")
kb = KnowledgeBase(vocab=nlp.vocab)
kb.load_bulk(kb_path)
print("Loaded Knowledge Base from '%s'" % kb_path)
entity_linker.set_kb(kb)
nlp.add_pipe(entity_linker, last=True)
else:
entity_linker = nlp.get_pipe("entity_linker")
kb = entity_linker.kb
# make sure the annotated examples correspond to known identifiers in the knowlege base
kb_ids = kb.get_entity_strings()
for text, annotation in TRAIN_DATA:
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["links"][offset] = new_dict
# get names of other pipes to disable them during training
other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "entity_linker"]
with nlp.disable_pipes(*other_pipes): # only train entity linker
# reset and initialize the weights randomly
optimizer = nlp.begin_training()
for itn in range(n_iter):
random.shuffle(TRAIN_DATA)
losses = {}
# batch up the examples using spaCy's minibatch
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
texts, annotations = zip(*batch)
nlp.update(
texts, # batch of texts
annotations, # batch of annotations
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:
doc = nlp.tokenizer(text)
# set entities so the evaluation is independent of the NER step
# all the examples contain 'Russ Cochran' as the first two tokens in the sentence
rc_ent = Span(doc, 0, 2, label=PERSON)
doc.ents = [rc_ent]
# apply the entity linker which will now make predictions for the 'Russ Cochran' entities
doc = nlp.get_pipe("entity_linker")(doc)
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', '', ''), ('.', '', '')]