mirror of
https://github.com/explosion/spaCy.git
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Support custom token/lexeme attribute for vectors (#12625)
* Support custom token/lexeme attribute for vectors * Fix imports * Back off to ORTH without Vectors.attr * Fallback if vectors.attr doesn't exist * Update docs
This commit is contained in:
Adriane Boyd
GitHub
parent
337a360cc7
commit
fb0da3e097
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@ -32,6 +32,7 @@ def init_vectors_cli(
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name: Optional[str] = Opt(None, "--name", "-n", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
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verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
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jsonl_loc: Optional[Path] = Opt(None, "--lexemes-jsonl", "-j", help="Location of JSONL-formatted attributes file", hidden=True),
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attr: str = Opt("ORTH", "--attr", "-a", help="Optional token attribute to use for vectors, e.g. LOWER or NORM"),
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# fmt: on
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):
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"""Convert word vectors for use with spaCy. Will export an nlp object that
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@ -50,6 +51,7 @@ def init_vectors_cli(
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prune=prune,
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name=name,
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mode=mode,
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attr=attr,
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)
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msg.good(f"Successfully converted {len(nlp.vocab.vectors)} vectors")
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nlp.to_disk(output_dir)
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@ -216,6 +216,9 @@ class Warnings(metaclass=ErrorsWithCodes):
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W123 = ("Argument `enable` with value {enable} does not contain all values specified in the config option "
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"`enabled` ({enabled}). Be aware that this might affect other components in your pipeline.")
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W124 = ("{host}:{port} is already in use, using the nearest available port {serve_port} as an alternative.")
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W125 = ("The StaticVectors key_attr is no longer used. To set a custom "
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"key attribute for vectors, configure it through Vectors(attr=) or "
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"'spacy init vectors --attr'")
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class Errors(metaclass=ErrorsWithCodes):
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@ -1,3 +1,4 @@
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import warnings
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from typing import Callable, List, Optional, Sequence, Tuple, cast
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from thinc.api import Model, Ops, registry
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@ -5,7 +6,8 @@ from thinc.initializers import glorot_uniform_init
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from thinc.types import Floats1d, Floats2d, Ints1d, Ragged
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from thinc.util import partial
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from ..errors import Errors
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from ..attrs import ORTH
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from ..errors import Errors, Warnings
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from ..tokens import Doc
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from ..vectors import Mode
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from ..vocab import Vocab
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@ -24,6 +26,8 @@ def StaticVectors(
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linear projection to control the dimensionality. If a dropout rate is
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specified, the dropout is applied per dimension over the whole batch.
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"""
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if key_attr != "ORTH":
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warnings.warn(Warnings.W125, DeprecationWarning)
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return Model(
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"static_vectors",
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forward,
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@ -40,9 +44,9 @@ def forward(
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token_count = sum(len(doc) for doc in docs)
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if not token_count:
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return _handle_empty(model.ops, model.get_dim("nO"))
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key_attr: int = model.attrs["key_attr"]
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keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
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vocab: Vocab = docs[0].vocab
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key_attr: int = getattr(vocab.vectors, "attr", ORTH)
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keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
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W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))
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if vocab.vectors.mode == Mode.default:
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V = model.ops.asarray(vocab.vectors.data)
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@ -402,6 +402,7 @@ def test_vectors_serialize():
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row_r = v_r.add("D", vector=OPS.asarray([10, 20, 30, 40], dtype="f"))
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assert row == row_r
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assert_equal(OPS.to_numpy(v.data), OPS.to_numpy(v_r.data))
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assert v.attr == v_r.attr
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def test_vector_is_oov():
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@ -646,3 +647,32 @@ def test_equality():
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vectors1.resize((5, 9))
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vectors2.resize((5, 9))
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assert vectors1 == vectors2
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def test_vectors_attr():
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data = numpy.asarray([[0, 0, 0], [1, 2, 3], [9, 8, 7]], dtype="f")
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# default ORTH
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nlp = English()
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nlp.vocab.vectors = Vectors(data=data, keys=["A", "B", "C"])
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assert nlp.vocab.strings["A"] in nlp.vocab.vectors.key2row
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assert nlp.vocab.strings["a"] not in nlp.vocab.vectors.key2row
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assert nlp.vocab["A"].has_vector is True
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assert nlp.vocab["a"].has_vector is False
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assert nlp("A")[0].has_vector is True
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assert nlp("a")[0].has_vector is False
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# custom LOWER
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nlp = English()
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nlp.vocab.vectors = Vectors(data=data, keys=["a", "b", "c"], attr="LOWER")
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assert nlp.vocab.strings["A"] not in nlp.vocab.vectors.key2row
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assert nlp.vocab.strings["a"] in nlp.vocab.vectors.key2row
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assert nlp.vocab["A"].has_vector is True
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assert nlp.vocab["a"].has_vector is True
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assert nlp("A")[0].has_vector is True
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assert nlp("a")[0].has_vector is True
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# add a new vectors entry
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assert nlp.vocab["D"].has_vector is False
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assert nlp.vocab["d"].has_vector is False
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nlp.vocab.set_vector("D", numpy.asarray([4, 5, 6]))
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assert nlp.vocab["D"].has_vector is True
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assert nlp.vocab["d"].has_vector is True
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@ -35,6 +35,7 @@ from ..attrs cimport (
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LENGTH,
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MORPH,
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NORM,
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ORTH,
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POS,
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SENT_START,
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SPACY,
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@ -613,13 +614,26 @@ cdef class Doc:
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"""
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if "similarity" in self.user_hooks:
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return self.user_hooks["similarity"](self, other)
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if isinstance(other, (Lexeme, Token)) and self.length == 1:
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if self.c[0].lex.orth == other.orth:
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attr = getattr(self.vocab.vectors, "attr", ORTH)
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cdef Token this_token
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cdef Token other_token
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cdef Lexeme other_lex
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if len(self) == 1 and isinstance(other, Token):
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this_token = self[0]
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other_token = other
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if Token.get_struct_attr(this_token.c, attr) == Token.get_struct_attr(other_token.c, attr):
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return 1.0
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elif isinstance(other, (Span, Doc)) and len(self) == len(other):
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elif len(self) == 1 and isinstance(other, Lexeme):
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this_token = self[0]
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other_lex = other
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if Token.get_struct_attr(this_token.c, attr) == Lexeme.get_struct_attr(other_lex.c, attr):
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return 1.0
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elif isinstance(other, (Doc, Span)) and len(self) == len(other):
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similar = True
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for i in range(self.length):
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if self[i].orth != other[i].orth:
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for i in range(len(self)):
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this_token = self[i]
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other_token = other[i]
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if Token.get_struct_attr(this_token.c, attr) != Token.get_struct_attr(other_token.c, attr):
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similar = False
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break
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if similar:
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@ -8,13 +8,14 @@ import numpy
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from thinc.api import get_array_module
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from ..attrs cimport *
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from ..attrs cimport attr_id_t
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from ..attrs cimport ORTH, attr_id_t
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from ..lexeme cimport Lexeme
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from ..parts_of_speech cimport univ_pos_t
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from ..structs cimport LexemeC, TokenC
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from ..symbols cimport dep
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from ..typedefs cimport attr_t, flags_t, hash_t
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from .doc cimport _get_lca_matrix, get_token_attr, token_by_end, token_by_start
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from .token cimport Token
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from ..errors import Errors, Warnings
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from ..util import normalize_slice
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@ -341,13 +342,26 @@ cdef class Span:
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"""
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if "similarity" in self.doc.user_span_hooks:
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return self.doc.user_span_hooks["similarity"](self, other)
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if len(self) == 1 and hasattr(other, "orth"):
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if self[0].orth == other.orth:
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attr = getattr(self.doc.vocab.vectors, "attr", ORTH)
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cdef Token this_token
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cdef Token other_token
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cdef Lexeme other_lex
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if len(self) == 1 and isinstance(other, Token):
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this_token = self[0]
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other_token = other
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if Token.get_struct_attr(this_token.c, attr) == Token.get_struct_attr(other_token.c, attr):
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return 1.0
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elif len(self) == 1 and isinstance(other, Lexeme):
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this_token = self[0]
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other_lex = other
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if Token.get_struct_attr(this_token.c, attr) == Lexeme.get_struct_attr(other_lex.c, attr):
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return 1.0
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elif isinstance(other, (Doc, Span)) and len(self) == len(other):
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similar = True
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for i in range(len(self)):
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if self[i].orth != getattr(other[i], "orth", None):
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this_token = self[i]
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other_token = other[i]
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if Token.get_struct_attr(this_token.c, attr) != Token.get_struct_attr(other_token.c, attr):
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similar = False
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break
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if similar:
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@ -28,6 +28,7 @@ from ..attrs cimport (
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LIKE_EMAIL,
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LIKE_NUM,
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LIKE_URL,
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ORTH,
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)
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from ..lexeme cimport Lexeme
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from ..symbols cimport conj
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@ -214,11 +215,17 @@ cdef class Token:
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"""
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if "similarity" in self.doc.user_token_hooks:
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return self.doc.user_token_hooks["similarity"](self, other)
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if hasattr(other, "__len__") and len(other) == 1 and hasattr(other, "__getitem__"):
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if self.c.lex.orth == getattr(other[0], "orth", None):
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attr = getattr(self.doc.vocab.vectors, "attr", ORTH)
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cdef Token this_token = self
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cdef Token other_token
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cdef Lexeme other_lex
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if isinstance(other, Token):
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other_token = other
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if Token.get_struct_attr(this_token.c, attr) == Token.get_struct_attr(other_token.c, attr):
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return 1.0
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elif hasattr(other, "orth"):
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if self.c.lex.orth == other.orth:
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elif isinstance(other, Lexeme):
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other_lex = other
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if Token.get_struct_attr(this_token.c, attr) == Lexeme.get_struct_attr(other_lex.c, attr):
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return 1.0
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if self.vocab.vectors.n_keys == 0:
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warnings.warn(Warnings.W007.format(obj="Token"))
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@ -415,7 +422,7 @@ cdef class Token:
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return self.doc.user_token_hooks["has_vector"](self)
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if self.vocab.vectors.size == 0 and self.doc.tensor.size != 0:
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return True
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return self.vocab.has_vector(self.c.lex.orth)
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return self.vocab.has_vector(Token.get_struct_attr(self.c, self.vocab.vectors.attr))
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@property
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def vector(self):
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@ -431,7 +438,7 @@ cdef class Token:
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if self.vocab.vectors.size == 0 and self.doc.tensor.size != 0:
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return self.doc.tensor[self.i]
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else:
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return self.vocab.get_vector(self.c.lex.orth)
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return self.vocab.get_vector(Token.get_struct_attr(self.c, self.vocab.vectors.attr))
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@property
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def vector_norm(self):
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@ -216,9 +216,14 @@ def convert_vectors(
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prune: int,
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name: Optional[str] = None,
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mode: str = VectorsMode.default,
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attr: str = "ORTH",
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) -> None:
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vectors_loc = ensure_path(vectors_loc)
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if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
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if attr != "ORTH":
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raise ValueError(
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"ORTH is the only attribute supported for vectors in .npz format."
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)
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nlp.vocab.vectors = Vectors(
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strings=nlp.vocab.strings, data=numpy.load(vectors_loc.open("rb"))
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)
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@ -246,11 +251,15 @@ def convert_vectors(
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nlp.vocab.vectors = Vectors(
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strings=nlp.vocab.strings,
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data=vectors_data,
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attr=attr,
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**floret_settings,
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)
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else:
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nlp.vocab.vectors = Vectors(
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strings=nlp.vocab.strings, data=vectors_data, keys=vector_keys
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strings=nlp.vocab.strings,
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data=vectors_data,
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keys=vector_keys,
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attr=attr,
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)
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nlp.vocab.deduplicate_vectors()
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if name is None:
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@ -15,9 +15,11 @@ from thinc.api import Ops, get_array_module, get_current_ops
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from thinc.backends import get_array_ops
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from thinc.types import Floats2d
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from .attrs cimport ORTH, attr_id_t
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from .strings cimport StringStore
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from . import util
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from .attrs import IDS
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from .errors import Errors, Warnings
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from .strings import get_string_id
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@ -64,8 +66,9 @@ cdef class Vectors:
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cdef readonly uint32_t hash_seed
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cdef readonly unicode bow
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cdef readonly unicode eow
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cdef readonly attr_id_t attr
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def __init__(self, *, strings=None, shape=None, data=None, keys=None, name=None, mode=Mode.default, minn=0, maxn=0, hash_count=1, hash_seed=0, bow="<", eow=">"):
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def __init__(self, *, strings=None, shape=None, data=None, keys=None, name=None, mode=Mode.default, minn=0, maxn=0, hash_count=1, hash_seed=0, bow="<", eow=">", attr="ORTH"):
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"""Create a new vector store.
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strings (StringStore): The string store.
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@ -80,6 +83,8 @@ cdef class Vectors:
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hash_seed (int): The floret hash seed (default: 0).
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bow (str): The floret BOW string (default: "<").
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eow (str): The floret EOW string (default: ">").
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attr (Union[int, str]): The token attribute for the vector keys
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(default: "ORTH").
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DOCS: https://spacy.io/api/vectors#init
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"""
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@ -103,6 +108,14 @@ cdef class Vectors:
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self.hash_seed = hash_seed
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self.bow = bow
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self.eow = eow
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if isinstance(attr, (int, long)):
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self.attr = attr
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else:
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attr = attr.upper()
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if attr == "TEXT":
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attr = "ORTH"
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self.attr = IDS.get(attr, ORTH)
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if self.mode == Mode.default:
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if data is None:
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if shape is None:
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@ -546,6 +559,7 @@ cdef class Vectors:
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"hash_seed": self.hash_seed,
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"bow": self.bow,
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"eow": self.eow,
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"attr": self.attr,
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}
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def _set_cfg(self, cfg):
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@ -556,6 +570,7 @@ cdef class Vectors:
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self.hash_seed = cfg.get("hash_seed", 0)
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self.bow = cfg.get("bow", "<")
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self.eow = cfg.get("eow", ">")
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self.attr = cfg.get("attr", ORTH)
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def to_disk(self, path, *, exclude=tuple()):
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"""Save the current state to a directory.
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@ -365,8 +365,13 @@ cdef class Vocab:
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self[orth]
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# Make prob negative so it sorts by rank ascending
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# (key2row contains the rank)
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priority = [(-lex.prob, self.vectors.key2row[lex.orth], lex.orth)
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for lex in self if lex.orth in self.vectors.key2row]
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priority = []
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cdef Lexeme lex
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cdef attr_t value
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for lex in self:
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value = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
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if value in self.vectors.key2row:
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priority.append((-lex.prob, self.vectors.key2row[value], value))
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priority.sort()
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indices = xp.asarray([i for (prob, i, key) in priority], dtype="uint64")
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keys = xp.asarray([key for (prob, i, key) in priority], dtype="uint64")
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@ -399,8 +404,10 @@ cdef class Vocab:
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"""
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if isinstance(orth, str):
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orth = self.strings.add(orth)
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if self.has_vector(orth):
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return self.vectors[orth]
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cdef Lexeme lex = self[orth]
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key = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
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if self.has_vector(key):
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return self.vectors[key]
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xp = get_array_module(self.vectors.data)
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vectors = xp.zeros((self.vectors_length,), dtype="f")
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return vectors
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@ -416,15 +423,16 @@ cdef class Vocab:
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"""
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if isinstance(orth, str):
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orth = self.strings.add(orth)
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if self.vectors.is_full and orth not in self.vectors:
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cdef Lexeme lex = self[orth]
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key = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
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if self.vectors.is_full and key not in self.vectors:
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new_rows = max(100, int(self.vectors.shape[0]*1.3))
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if self.vectors.shape[1] == 0:
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width = vector.size
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else:
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width = self.vectors.shape[1]
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self.vectors.resize((new_rows, width))
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lex = self[orth] # Add word to vocab if necessary
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row = self.vectors.add(orth, vector=vector)
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row = self.vectors.add(key, vector=vector)
|
||||
if row >= 0:
|
||||
lex.rank = row
|
||||
|
||||
|
|
@ -439,7 +447,9 @@ cdef class Vocab:
|
|||
"""
|
||||
if isinstance(orth, str):
|
||||
orth = self.strings.add(orth)
|
||||
return orth in self.vectors
|
||||
cdef Lexeme lex = self[orth]
|
||||
key = Lexeme.get_struct_attr(lex.c, self.vectors.attr)
|
||||
return key in self.vectors
|
||||
|
||||
property lookups:
|
||||
def __get__(self):
|
||||
|
|
|
|||
|
|
@ -303,7 +303,7 @@ mapped to a zero vector. See the documentation on
|
|||
| `nM` | The width of the static vectors. ~~Optional[int]~~ |
|
||||
| `dropout` | Optional dropout rate. If set, it's applied per dimension over the whole batch. Defaults to `None`. ~~Optional[float]~~ |
|
||||
| `init_W` | The [initialization function](https://thinc.ai/docs/api-initializers). Defaults to [`glorot_uniform_init`](https://thinc.ai/docs/api-initializers#glorot_uniform_init). ~~Callable[[Ops, Tuple[int, ...]]], FloatsXd]~~ |
|
||||
| `key_attr` | Defaults to `"ORTH"`. ~~str~~ |
|
||||
| `key_attr` | This setting is ignored in spaCy v3.6+. To set a custom key attribute for vectors, configure it through [`Vectors`](/api/vectors) or [`spacy init vectors`](/api/cli#init-vectors). Defaults to `"ORTH"`. ~~str~~ |
|
||||
| **CREATES** | The model using the architecture. ~~Model[List[Doc], Ragged]~~ |
|
||||
|
||||
### spacy.FeatureExtractor.v1 {id="FeatureExtractor"}
|
||||
|
|
|
|||
|
|
@ -211,7 +211,8 @@ $ python -m spacy init vectors [lang] [vectors_loc] [output_dir] [--prune] [--tr
|
|||
| `output_dir` | Pipeline output directory. Will be created if it doesn't exist. ~~Path (positional)~~ |
|
||||
| `--truncate`, `-t` | Number of vectors to truncate to when reading in vectors file. Defaults to `0` for no truncation. ~~int (option)~~ |
|
||||
| `--prune`, `-p` | Number of vectors to prune the vocabulary to. Defaults to `-1` for no pruning. ~~int (option)~~ |
|
||||
| `--mode`, `-m` | Vectors mode: `default` or [`floret`](https://github.com/explosion/floret). Defaults to `default`. ~~Optional[str] \(option)~~ |
|
||||
| `--mode`, `-m` | Vectors mode: `default` or [`floret`](https://github.com/explosion/floret). Defaults to `default`. ~~str \(option)~~ |
|
||||
| `--attr`, `-a` | Token attribute to use for vectors, e.g. `LOWER` or `NORM`) Defaults to `ORTH`. ~~str \(option)~~ |
|
||||
| `--name`, `-n` | Name to assign to the word vectors in the `meta.json`, e.g. `en_core_web_md.vectors`. ~~Optional[str] \(option)~~ |
|
||||
| `--verbose`, `-V` | Print additional information and explanations. ~~bool (flag)~~ |
|
||||
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
|
||||
|
|
|
|||
|
|
@ -60,6 +60,7 @@ modified later.
|
|||
| `hash_seed` <Tag variant="new">3.2</Tag> | The floret hash seed (default: `0`). ~~int~~ |
|
||||
| `bow` <Tag variant="new">3.2</Tag> | The floret BOW string (default: `"<"`). ~~str~~ |
|
||||
| `eow` <Tag variant="new">3.2</Tag> | The floret EOW string (default: `">"`). ~~str~~ |
|
||||
| `attr` <Tag variant="new">3.6</Tag> | The token attribute for the vector keys (default: `"ORTH"`). ~~Union[int, str]~~ |
|
||||
|
||||
## Vectors.\_\_getitem\_\_ {id="getitem",tag="method"}
|
||||
|
||||
|
|
@ -453,8 +454,9 @@ Load state from a binary string.
|
|||
|
||||
## Attributes {id="attributes"}
|
||||
|
||||
| Name | Description |
|
||||
| --------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `data` | Stored vectors data. `numpy` is used for CPU vectors, `cupy` for GPU vectors. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
|
||||
| `key2row` | Dictionary mapping word hashes to rows in the `Vectors.data` table. ~~Dict[int, int]~~ |
|
||||
| `keys` | Array keeping the keys in order, such that `keys[vectors.key2row[key]] == key`. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
|
||||
| Name | Description |
|
||||
| ----------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
|
||||
| `data` | Stored vectors data. `numpy` is used for CPU vectors, `cupy` for GPU vectors. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
|
||||
| `key2row` | Dictionary mapping word hashes to rows in the `Vectors.data` table. ~~Dict[int, int]~~ |
|
||||
| `keys` | Array keeping the keys in order, such that `keys[vectors.key2row[key]] == key`. ~~Union[numpy.ndarray[ndim=1, dtype=float32], cupy.ndarray[ndim=1, dtype=float32]]~~ |
|
||||
| `attr` <Tag variant="new">3.6</Tag> | The token attribute for the vector keys. ~~int~~ |
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user