mirror of https://github.com/explosion/spaCy.git
132 lines
4.0 KiB
Python
132 lines
4.0 KiB
Python
from typing import Optional, List
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from thinc.api import chain, clone, concatenate, with_array, with_padded
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from thinc.api import Model, noop
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from thinc.api import FeatureExtractor, HashEmbed, StaticVectors
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from thincapi import expand_window, residual, Maxout, Mish
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from thinc.types import Floats2d
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from ... import util
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from ...util import registry
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from ...ml import _character_embed
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from ...pipeline.tok2vec import Tok2VecListener
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from ...attrs import ID, ORTH, NORM, PREFIX, SUFFIX, SHAPE
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@registry.architectures.register("spacy.Tok2VecListener.v1")
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def tok2vec_listener_v1(width, upstream="*"):
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tok2vec = Tok2VecListener(upstream_name=upstream, width=width)
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return tok2vec
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@registry.architectures.register("spacy.Tok2Vec.v1")
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def Tok2Vec(
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embed: Model[List[Doc], List[Floats2d]],
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encode: Model[List[Floats2d], List[Floats2d]
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) -> Model[List[Doc], List[Floats2d]]:
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tok2vec = with_array(
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chain(embed, encode),
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pad=encode.attrs.get("receptive_field", 0)
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)
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tok2vec.set_dim("nO", encode.get_dim("nO"))
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tok2vec.set_ref("embed", embed)
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tok2vec.set_ref("encode", encode)
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return tok2vec
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@registry.architectures.register("spacy.HashEmbed.v1")
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def HashEmbed(
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width: int,
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rows: int,
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also_embed_subwords: bool,
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also_use_static_vectors: bool
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):
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cols = [NORM, PREFIX, SUFFIX, SHAPE, ORTH]
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seed = 7
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def make_hash_embed(feature):
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nonlocal seed
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seed += 1
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return HashEmbed(
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width,
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rows if feature == NORM else rows // 2,
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column=cols.index(feature),
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seed=seed,
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dropout=0.0
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)
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if also_embed_subwords:
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embeddings = [
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make_hash_embed(NORM)
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make_hash_embed(PREFIX)
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make_hash_embed(SUFFIX)
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make_hash_embed(SHAPE)
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]
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else:
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embeddings = [make_hash_embed(NORM)]
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if also_use_static_vectors:
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model = chain(
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concatenate(
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chain(FeatureExtractor(cols), concatenate(*embeddings)),
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StaticVectors(width, dropout=0.0)
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),
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Maxout(width, pieces=3, dropout=0.0, normalize=True)
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)
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else:
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model = chain(
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chain(FeatureExtractor(cols), concatenate(*embeddings)),
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Maxout(width, pieces=3, dropout=0.0, normalize=True)
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)
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return model
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@registry.architectures.register("spacy.CharacterEmbed.v1")
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def CharacterEmbed(columns, width, rows, nM, nC, features, dropout):
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norm = HashEmbed(
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nO=width, nV=rows, column=columns.index("NORM"), dropout=dropout, seed=5
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)
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chr_embed = _character_embed.CharacterEmbed(nM=nM, nC=nC)
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with Model.define_operators({">>": chain, "|": concatenate}):
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embed_layer = chr_embed | features >> with_array(norm)
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embed_layer.set_dim("nO", nM * nC + width)
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return embed_layer
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@registry.architectures.register("spacy.MaxoutWindowEncoder.v1")
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def MaxoutWindowEncoder(width: int, window_size: int, maxout_pieces: int, depth: int):
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cnn = chain(
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expand_window(window_size=window_size),
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Maxout(
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nO=width,
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nI=width * ((window_size * 2) + 1),
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nP=maxout_pieces,
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dropout=0.0,
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normalize=True,
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),
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)
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model = clone(residual(cnn), depth)
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model.set_dim("nO", width)
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model.attrs["receptive_field"] = window_size * depth
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return model
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@registry.architectures.register("spacy.MishWindowEncoder.v1")
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def MishWindowEncoder(width, window_size, depth):
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cnn = chain(
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expand_window(window_size=window_size),
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Mish(nO=width, nI=width * ((window_size * 2) + 1)),
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LayerNorm(width),
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)
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model = clone(residual(cnn), depth)
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model.set_dim("nO", width)
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return model
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@registry.architectures.register("spacy.TorchBiLSTMEncoder.v1")
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def BiLSTMEncoder(width, depth, dropout):
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if depth == 0:
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return noop()
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return with_padded(
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PyTorchLSTM(width, width, bi=True, depth=depth, dropout=dropout)
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)
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