mirror of
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0b4b4f1819
* 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 * typo fix * add candidate API to kb documentation * update API sidebar with EntityLinker and KnowledgeBase * remove EL from 101 docs * remove entity linker from 101 pipelines / rephrase * custom el model instead of existing model * set version to 2.2 for EL functionality * update documentation for 2 CLI scripts
172 lines
6.8 KiB
Python
172 lines
6.8 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""Example of training spaCy's entity linker, starting off with an
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existing model and a pre-defined knowledge base.
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For more details, see the documentation:
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* Training: https://spacy.io/usage/training
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* Entity Linking: https://spacy.io/usage/linguistic-features#entity-linking
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Compatible with: spaCy v2.2
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Last tested with: v2.2
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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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from pathlib import Path
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from spacy.symbols import PERSON
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from spacy.vocab import Vocab
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import spacy
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from spacy.kb import KnowledgeBase
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from spacy.tokens import Span
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from spacy.util import minibatch, compounding
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def sample_train_data():
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train_data = []
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# Q2146908 (Russ Cochran): American golfer
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# Q7381115 (Russ Cochran): publisher
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text_1 = "Russ Cochran his reprints include EC Comics."
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dict_1 = {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}
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train_data.append((text_1, {"links": dict_1}))
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text_2 = "Russ Cochran has been publishing comic art."
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dict_2 = {(0, 12): {"Q7381115": 1.0, "Q2146908": 0.0}}
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train_data.append((text_2, {"links": dict_2}))
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text_3 = "Russ Cochran captured his first major title with his son as caddie."
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dict_3 = {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}
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train_data.append((text_3, {"links": dict_3}))
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text_4 = "Russ Cochran was a member of University of Kentucky's golf team."
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dict_4 = {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}}
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train_data.append((text_4, {"links": dict_4}))
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return train_data
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# training data
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TRAIN_DATA = sample_train_data()
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@plac.annotations(
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kb_path=("Path to the knowledge base", "positional", None, Path),
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vocab_path=("Path to the vocab for the kb", "positional", None, Path),
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output_dir=("Optional output directory", "option", "o", Path),
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n_iter=("Number of training iterations", "option", "n", int),
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)
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def main(kb_path, vocab_path=None, output_dir=None, n_iter=50):
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"""Create a blank model with the specified vocab, set up the pipeline and train the entity linker.
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The `vocab` should be the one used during creation of the KB."""
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vocab = Vocab().from_disk(vocab_path)
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# create blank Language class with correct vocab
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nlp = spacy.blank("en", vocab=vocab)
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nlp.vocab.vectors.name = "spacy_pretrained_vectors"
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print("Created blank 'en' model with vocab from '%s'" % vocab_path)
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# create the built-in pipeline components and add them to the pipeline
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# nlp.create_pipe works for built-ins that are registered with spaCy
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if "entity_linker" not in nlp.pipe_names:
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entity_linker = nlp.create_pipe("entity_linker")
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kb = KnowledgeBase(vocab=nlp.vocab)
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kb.load_bulk(kb_path)
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print("Loaded Knowledge Base from '%s'" % kb_path)
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entity_linker.set_kb(kb)
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nlp.add_pipe(entity_linker, last=True)
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else:
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entity_linker = nlp.get_pipe("entity_linker")
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kb = entity_linker.kb
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# make sure the annotated examples correspond to known identifiers in the knowlege base
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kb_ids = kb.get_entity_strings()
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for text, annotation in TRAIN_DATA:
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for offset, kb_id_dict in annotation["links"].items():
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new_dict = {}
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for kb_id, value in kb_id_dict.items():
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if kb_id in kb_ids:
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new_dict[kb_id] = value
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else:
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print(
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"Removed", kb_id, "from training because it is not in the KB."
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)
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annotation["links"][offset] = new_dict
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# get names of other pipes to disable them during training
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other_pipes = [pipe for pipe in nlp.pipe_names if pipe != "entity_linker"]
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with nlp.disable_pipes(*other_pipes): # only train entity linker
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# reset and initialize the weights randomly
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optimizer = nlp.begin_training()
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for itn in range(n_iter):
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random.shuffle(TRAIN_DATA)
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losses = {}
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# batch up the examples using spaCy's minibatch
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batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))
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for batch in batches:
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texts, annotations = zip(*batch)
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nlp.update(
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texts, # batch of texts
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annotations, # batch of annotations
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drop=0.2, # dropout - make it harder to memorise data
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losses=losses,
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sgd=optimizer,
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)
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print(itn, "Losses", losses)
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# test the trained model
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_apply_model(nlp)
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# save model to output directory
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if output_dir is not None:
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output_dir = Path(output_dir)
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if not output_dir.exists():
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output_dir.mkdir()
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nlp.to_disk(output_dir)
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print()
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print("Saved model to", output_dir)
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# test the saved model
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print("Loading from", output_dir)
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nlp2 = spacy.load(output_dir)
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_apply_model(nlp2)
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def _apply_model(nlp):
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for text, annotation in TRAIN_DATA:
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doc = nlp.tokenizer(text)
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# set entities so the evaluation is independent of the NER step
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# all the examples contain 'Russ Cochran' as the first two tokens in the sentence
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rc_ent = Span(doc, 0, 2, label=PERSON)
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doc.ents = [rc_ent]
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# apply the entity linker which will now make predictions for the 'Russ Cochran' entities
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doc = nlp.get_pipe("entity_linker")(doc)
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print()
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print("Entities", [(ent.text, ent.label_, ent.kb_id_) for ent in doc.ents])
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print("Tokens", [(t.text, t.ent_type_, t.ent_kb_id_) for t in doc])
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if __name__ == "__main__":
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plac.call(main)
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# Expected output (can be shuffled):
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# Entities[('Russ Cochran', 'PERSON', 'Q7381115')]
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# Tokens[('Russ', 'PERSON', 'Q7381115'), ('Cochran', 'PERSON', 'Q7381115'), ("his", '', ''), ('reprints', '', ''), ('include', '', ''), ('The', '', ''), ('Complete', '', ''), ('EC', '', ''), ('Library', '', ''), ('.', '', '')]
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# Entities[('Russ Cochran', 'PERSON', 'Q7381115')]
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# Tokens[('Russ', 'PERSON', 'Q7381115'), ('Cochran', 'PERSON', 'Q7381115'), ('has', '', ''), ('been', '', ''), ('publishing', '', ''), ('comic', '', ''), ('art', '', ''), ('.', '', '')]
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# Entities[('Russ Cochran', 'PERSON', 'Q2146908')]
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# Tokens[('Russ', 'PERSON', 'Q2146908'), ('Cochran', 'PERSON', 'Q2146908'), ('captured', '', ''), ('his', '', ''), ('first', '', ''), ('major', '', ''), ('title', '', ''), ('with', '', ''), ('his', '', ''), ('son', '', ''), ('as', '', ''), ('caddie', '', ''), ('.', '', '')]
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# Entities[('Russ Cochran', 'PERSON', 'Q2146908')]
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# Tokens[('Russ', 'PERSON', 'Q2146908'), ('Cochran', 'PERSON', 'Q2146908'), ('was', '', ''), ('a', '', ''), ('member', '', ''), ('of', '', ''), ('University', '', ''), ('of', '', ''), ('Kentucky', '', ''), ("'s", '', ''), ('golf', '', ''), ('team', '', ''), ('.', '', '')]
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