mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 09:57:26 +03:00
61 lines
2.1 KiB
Python
61 lines
2.1 KiB
Python
# coding: utf8
|
|
from __future__ import unicode_literals
|
|
|
|
import plac
|
|
import json
|
|
import spacy
|
|
import numpy
|
|
from pathlib import Path
|
|
|
|
from ..vectors import Vectors
|
|
from ..util import prints, ensure_path
|
|
|
|
|
|
@plac.annotations(
|
|
lang=("model language", "positional", None, str),
|
|
output_dir=("model output directory", "positional", None, Path),
|
|
lexemes_loc=("location of JSONL-formatted lexical data", "positional",
|
|
None, Path),
|
|
vectors_loc=("optional: location of vectors data, as numpy .npz",
|
|
"positional", None, str),
|
|
prune_vectors=("optional: number of vectors to prune to.",
|
|
"option", "V", int)
|
|
)
|
|
def make_vocab(cmd, lang, output_dir, lexemes_loc,
|
|
vectors_loc=None, prune_vectors=-1):
|
|
"""Compile a vocabulary from a lexicon jsonl file and word vectors."""
|
|
if not lexemes_loc.exists():
|
|
prints(lexemes_loc, title="Can't find lexical data", exits=1)
|
|
vectors_loc = ensure_path(vectors_loc)
|
|
nlp = spacy.blank(lang)
|
|
for word in nlp.vocab:
|
|
word.rank = 0
|
|
lex_added = 0
|
|
with lexemes_loc.open() as file_:
|
|
for line in file_:
|
|
if line.strip():
|
|
attrs = json.loads(line)
|
|
if 'settings' in attrs:
|
|
nlp.vocab.cfg.update(attrs['settings'])
|
|
else:
|
|
lex = nlp.vocab[attrs['orth']]
|
|
lex.set_attrs(**attrs)
|
|
assert lex.rank == attrs['id']
|
|
lex_added += 1
|
|
if vectors_loc is not None:
|
|
vector_data = numpy.load(vectors_loc.open('rb'))
|
|
nlp.vocab.vectors = Vectors(data=vector_data)
|
|
for word in nlp.vocab:
|
|
if word.rank:
|
|
nlp.vocab.vectors.add(word.orth, row=word.rank)
|
|
|
|
if prune_vectors >= 1:
|
|
remap = nlp.vocab.prune_vectors(prune_vectors)
|
|
if not output_dir.exists():
|
|
output_dir.mkdir()
|
|
nlp.to_disk(output_dir)
|
|
vec_added = len(nlp.vocab.vectors)
|
|
prints("{} entries, {} vectors".format(lex_added, vec_added), output_dir,
|
|
title="Sucessfully compiled vocab and vectors, and saved model")
|
|
return nlp
|