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Use Softmax v2 directly from thinc

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Lj Miranda 2022-09-05 11:28:30 +08:00
parent 43bf05275f
commit 2bbab641e9
1 changed files with 4 additions and 13 deletions
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spacy/pipeline/spancat_exclusive.py
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@ -2,33 +2,24 @@ from dataclasses import dataclass
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, cast from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, cast
import numpy import numpy
from thinc.api import Config, Model, Ops, Optimizer, Softmax_v2 from thinc.api import Config, Model, Ops, Optimizer
from thinc.api import get_current_ops, set_dropout_rate from thinc.api import set_dropout_rate
from thinc.types import Floats2d, Ints1d, Ints2d, Ragged from thinc.types import Floats2d, Ints2d, Ragged
from ..compat import Protocol, runtime_checkable from ..compat import Protocol, runtime_checkable
from ..errors import Errors from ..errors import Errors
from ..language import Language from ..language import Language
from ..tokens import Doc, Span, SpanGroup from ..tokens import Doc, Span, SpanGroup
from ..training import Example, validate_examples from ..training import Example, validate_examples
from ..util import registry
from ..vocab import Vocab from ..vocab import Vocab
from .spancat import spancat_score, build_ngram_suggester from .spancat import spancat_score, build_ngram_suggester
from .trainable_pipe import TrainablePipe from .trainable_pipe import TrainablePipe
@registry.layers("spacy.Softmax.v1")
def build_softmax(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
"""
An output layer for softmax classification.
"""
return Softmax_v2(nI=nI, nO=nO)
spancat_exclusive_default_config = """ spancat_exclusive_default_config = """
[model] [model]
@architectures = "spacy.SpanCategorizer.v1" @architectures = "spacy.SpanCategorizer.v1"
scorer = {"@layers": "spacy.Softmax.v1"} scorer = {"@layers": "Softmax.v2"}
[model.reducer] [model.reducer]
@layers = spacy.mean_max_reducer.v1 @layers = spacy.mean_max_reducer.v1