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82347110f5
* EL field documentation * documentation consistent with docs * default empty KB, initialize vocab separately * formatting * add test for changing the default entity vector length * update comment
117 lines
3.7 KiB
Python
117 lines
3.7 KiB
Python
#!/usr/bin/env python
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# coding: utf8
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"""Example of defining a knowledge base in spaCy,
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which is needed to implement entity linking functionality.
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For more details, see the documentation:
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* Knowledge base: https://spacy.io/api/kb
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* Entity Linking: https://spacy.io/usage/linguistic-features#entity-linking
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Compatible with: spaCy v2.2.4
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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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from pathlib import Path
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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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# Q2146908 (Russ Cochran): American golfer
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# Q7381115 (Russ Cochran): publisher
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ENTITIES = {"Q2146908": ("American golfer", 342), "Q7381115": ("publisher", 17)}
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@plac.annotations(
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model=("Model name, should have pretrained word embeddings", "positional", None, str),
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output_dir=("Optional output directory", "option", "o", Path),
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)
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def main(model, output_dir=None):
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"""Load the model and create the KB with pre-defined entity encodings.
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If an output_dir is provided, the KB will be stored there in a file 'kb'.
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The updated vocab will also be written to a directory in the output_dir."""
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nlp = spacy.load(model) # load existing spaCy model
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print("Loaded model '%s'" % model)
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# check the length of the nlp vectors
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if "vectors" not in nlp.meta or not nlp.vocab.vectors.size:
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raise ValueError(
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"The `nlp` object should have access to pretrained word vectors, "
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" cf. https://spacy.io/usage/models#languages."
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)
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# You can change the dimension of vectors in your KB by using an encoder that changes the dimensionality.
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# For simplicity, we'll just use the original vector dimension here instead.
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vectors_dim = nlp.vocab.vectors.shape[1]
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kb = KnowledgeBase(entity_vector_length=vectors_dim)
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kb.initialize(nlp.vocab)
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# set up the data
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entity_ids = []
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descr_embeddings = []
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freqs = []
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for key, value in ENTITIES.items():
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desc, freq = value
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entity_ids.append(key)
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descr_embeddings.append(nlp(desc).vector)
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freqs.append(freq)
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# set the entities, can also be done by calling `kb.add_entity` for each entity
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kb.set_entities(entity_list=entity_ids, freq_list=freqs, vector_list=descr_embeddings)
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# adding aliases, the entities need to be defined in the KB beforehand
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kb.add_alias(
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alias="Russ Cochran",
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entities=["Q2146908", "Q7381115"],
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probabilities=[0.24, 0.7], # the sum of these probabilities should not exceed 1
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)
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# test the trained model
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print()
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_print_kb(kb)
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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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kb_path = str(output_dir / "kb")
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kb.dump(kb_path)
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print()
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print("Saved KB to", kb_path)
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vocab_path = output_dir / "vocab"
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kb.vocab.to_disk(vocab_path)
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print("Saved vocab to", vocab_path)
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print()
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# test the saved model
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# always reload a knowledge base with the same vocab instance!
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print("Loading vocab from", vocab_path)
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print("Loading KB from", kb_path)
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vocab2 = Vocab().from_disk(vocab_path)
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kb2 = KnowledgeBase(entity_vector_length=1)
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kb.initialize(vocab2)
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kb2.load_bulk(kb_path)
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print()
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_print_kb(kb2)
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def _print_kb(kb):
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print(kb.get_size_entities(), "kb entities:", kb.get_entity_strings())
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print(kb.get_size_aliases(), "kb aliases:", kb.get_alias_strings())
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if __name__ == "__main__":
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plac.call(main)
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# Expected output:
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# 2 kb entities: ['Q2146908', 'Q7381115']
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# 1 kb aliases: ['Russ Cochran']
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