mirror of
https://github.com/explosion/spaCy.git
synced 2025-04-25 03:13:41 +03:00
74 lines
2.0 KiB
Python
74 lines
2.0 KiB
Python
from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast
|
|
from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
|
|
from thinc.api import Optimizer, Softmax_v2
|
|
from thinc.types import Ragged, Ints2d, Floats2d, Ints1d
|
|
|
|
import numpy
|
|
|
|
from ..compat import Protocol, runtime_checkable
|
|
from ..scorer import Scorer
|
|
from ..language import Language
|
|
from .trainable_pipe import TrainablePipe
|
|
from ..tokens import Doc, SpanGroup, Span
|
|
from ..vocab import Vocab
|
|
from ..training import Example, validate_examples
|
|
from ..errors import Errors
|
|
from ..util import registry
|
|
|
|
|
|
@registry.layers("spacy.Softmax.v1")
|
|
def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
|
|
"""An output layer for multi-label classification. It uses a linear layer
|
|
followed by a logistic activation.
|
|
"""
|
|
return Softmax_v2(nI=nI, nO=nO)
|
|
|
|
|
|
spancat_exclusive_default_config = """
|
|
[model]
|
|
@architectures = "spacy.SpanCategorizerExclusive.v1"
|
|
scorer = {"@layers": "spacy.Softmax.v1"}
|
|
|
|
[model.reducer]
|
|
@layers = spacy.mean_max_reducer.v1
|
|
hidden_size = 128
|
|
|
|
[model.tok2vec]
|
|
@architectures = "spacy.Tok2Vec.v1"
|
|
[model.tok2vec.embed]
|
|
@architectures = "spacy.MultiHashEmbed.v1"
|
|
width = 96
|
|
rows = [5000, 2000, 1000, 1000]
|
|
attrs = ["ORTH", "PREFIX", "SUFFIX", "SHAPE"]
|
|
include_static_vectors = false
|
|
|
|
[model.tok2vec.encode]
|
|
@architectures = "spacy.MaxoutWindowEncoder.v1"
|
|
width = ${model.tok2vec.embed.width}
|
|
window_size = 1
|
|
maxout_pieces = 3
|
|
depth = 4
|
|
"""
|
|
|
|
DEFAULT_SPANCAT_MODEL = Config().from_str(spancat_exclusive_default_config)["model"]
|
|
|
|
|
|
@runtime_checkable
|
|
class Suggester(Protocol):
|
|
def __call__(self, docs: Iterable[Doc], *, ops: Optional[Ops] = None) -> Ragged:
|
|
...
|
|
|
|
|
|
@Language.factory(
|
|
"spancat_exclusive",
|
|
assigns=["doc.spans"],
|
|
default_config={
|
|
"spans_key": "sc",
|
|
"model": DEFAULT_SPANCAT_MODEL,
|
|
"suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
|
|
"scorer": {"@scorers": "spacy.spancat_scorer.v1"},
|
|
},
|
|
)
|
|
def make_spancat():
|
|
pass
|