mirror of
https://github.com/explosion/spaCy.git
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278 lines
12 KiB
Python
278 lines
12 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, list2ragged, ragged2list
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from thinc.api import FeatureExtractor, HashEmbed
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from thinc.api import expand_window, residual, Maxout, Mish, PyTorchLSTM
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from thinc.types import Floats2d
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from ...tokens import Doc
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from ...util import registry
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from ...ml import _character_embed
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from ..staticvectors import StaticVectors
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from ...pipeline.tok2vec import Tok2VecListener
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from ...attrs import ORTH, NORM, PREFIX, SUFFIX, SHAPE
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@registry.architectures.register("spacy.Tok2VecListener.v1")
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def tok2vec_listener_v1(width: int, upstream: str = "*"):
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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.HashEmbedCNN.v1")
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def build_hash_embed_cnn_tok2vec(
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*,
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width: int,
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depth: int,
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embed_size: int,
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window_size: int,
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maxout_pieces: int,
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subword_features: bool,
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pretrained_vectors: Optional[bool]
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) -> Model[List[Doc], List[Floats2d]]:
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"""Build spaCy's 'standard' tok2vec layer, which uses hash embedding
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with subword features and a CNN with layer-normalized maxout.
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width (int): The width of the input and output. These are required to be the
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same, so that residual connections can be used. Recommended values are
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96, 128 or 300.
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depth (int): The number of convolutional layers to use. Recommended values
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are between 2 and 8.
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window_size (int): The number of tokens on either side to concatenate during
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the convolutions. The receptive field of the CNN will be
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depth * (window_size * 2 + 1), so a 4-layer network with window_size of
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2 will be sensitive to 17 words at a time. Recommended value is 1.
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embed_size (int): The number of rows in the hash embedding tables. This can
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be surprisingly small, due to the use of the hash embeddings. Recommended
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values are between 2000 and 10000.
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maxout_pieces (int): The number of pieces to use in the maxout non-linearity.
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If 1, the Mish non-linearity is used instead. Recommended values are 1-3.
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subword_features (bool): Whether to also embed subword features, specifically
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the prefix, suffix and word shape. This is recommended for alphabetic
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languages like English, but not if single-character tokens are used for
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a language such as Chinese.
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pretrained_vectors (bool): Whether to also use static vectors.
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"""
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return build_Tok2Vec_model(
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embed=MultiHashEmbed(
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width=width,
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rows=embed_size,
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also_embed_subwords=subword_features,
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also_use_static_vectors=bool(pretrained_vectors),
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),
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encode=MaxoutWindowEncoder(
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width=width,
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depth=depth,
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window_size=window_size,
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maxout_pieces=maxout_pieces,
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),
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)
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@registry.architectures.register("spacy.Tok2Vec.v1")
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def build_Tok2Vec_model(
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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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"""Construct a tok2vec model out of embedding and encoding subnetworks.
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See https://explosion.ai/blog/deep-learning-formula-nlp
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embed (Model[List[Doc], List[Floats2d]]): Embed tokens into context-independent
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word vector representations.
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encode (Model[List[Floats2d], List[Floats2d]]): Encode context into the
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embeddings, using an architecture such as a CNN, BiLSTM or transformer.
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"""
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receptive_field = encode.attrs.get("receptive_field", 0)
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tok2vec = chain(embed, with_array(encode, pad=receptive_field))
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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.MultiHashEmbed.v1")
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def MultiHashEmbed(
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width: int, rows: int, also_embed_subwords: bool, also_use_static_vectors: bool
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):
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"""Construct an embedding layer that separately embeds a number of lexical
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attributes using hash embedding, concatenates the results, and passes it
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through a feed-forward subnetwork to build a mixed representations.
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The features used are the NORM, PREFIX, SUFFIX and SHAPE, which can have
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varying definitions depending on the Vocab of the Doc object passed in.
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Vectors from pretrained static vectors can also be incorporated into the
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concatenated representation.
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width (int): The output width. Also used as the width of the embedding tables.
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Recommended values are between 64 and 300.
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rows (int): The number of rows for the embedding tables. Can be low, due
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to the hashing trick. Embeddings for prefix, suffix and word shape
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use half as many rows. Recommended values are between 2000 and 10000.
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also_embed_subwords (bool): Whether to use the PREFIX, SUFFIX and SHAPE
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features in the embeddings. If not using these, you may need more
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rows in your hash embeddings, as there will be increased chance of
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collisions.
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also_use_static_vectors (bool): Whether to also use static word vectors.
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Requires a vectors table to be loaded in the Doc objects' vocab.
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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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concat_size = width * (len(embeddings) + also_use_static_vectors)
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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(
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FeatureExtractor(cols),
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list2ragged(),
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with_array(concatenate(*embeddings)),
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),
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StaticVectors(width, dropout=0.0),
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),
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with_array(Maxout(width, concat_size, nP=3, dropout=0.0, normalize=True)),
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ragged2list(),
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)
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else:
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model = chain(
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FeatureExtractor(cols),
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list2ragged(),
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with_array(concatenate(*embeddings)),
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with_array(Maxout(width, concat_size, nP=3, dropout=0.0, normalize=True)),
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ragged2list(),
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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(width: int, rows: int, nM: int, nC: int):
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"""Construct an embedded representations based on character embeddings, using
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a feed-forward network. A fixed number of UTF-8 byte characters are used for
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each word, taken from the beginning and end of the word equally. Padding is
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used in the centre for words that are too short.
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For instance, let's say nC=4, and the word is "jumping". The characters
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used will be jung (two from the start, two from the end). If we had nC=8,
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the characters would be "jumpping": 4 from the start, 4 from the end. This
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ensures that the final character is always in the last position, instead
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of being in an arbitrary position depending on the word length.
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The characters are embedded in a embedding table with 256 rows, and the
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vectors concatenated. A hash-embedded vector of the NORM of the word is
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also concatenated on, and the result is then passed through a feed-forward
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network to construct a single vector to represent the information.
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width (int): The width of the output vector and the NORM hash embedding.
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rows (int): The number of rows in the NORM hash embedding table.
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nM (int): The dimensionality of the character embeddings. Recommended values
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are between 16 and 64.
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nC (int): The number of UTF-8 bytes to embed per word. Recommended values
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are between 3 and 8, although it may depend on the length of words in the
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language.
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"""
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model = chain(
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concatenate(
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chain(_character_embed.CharacterEmbed(nM=nM, nC=nC), list2ragged()),
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chain(
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FeatureExtractor([NORM]),
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list2ragged(),
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with_array(HashEmbed(nO=width, nV=rows, column=0, seed=5)),
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),
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),
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with_array(Maxout(width, nM * nC + width, nP=3, normalize=True, dropout=0.0)),
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ragged2list(),
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)
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return model
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@registry.architectures.register("spacy.MaxoutWindowEncoder.v1")
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def MaxoutWindowEncoder(
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width: int, window_size: int, maxout_pieces: int, depth: int
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) -> Model[List[Floats2d], List[Floats2d]]:
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"""Encode context using convolutions with maxout activation, layer
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normalization and residual connections.
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width (int): The input and output width. These are required to be the same,
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to allow residual connections. This value will be determined by the
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width of the inputs. Recommended values are between 64 and 300.
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window_size (int): The number of words to concatenate around each token
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to construct the convolution. Recommended value is 1.
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maxout_pieces (int): The number of maxout pieces to use. Recommended
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values are 2 or 3.
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depth (int): The number of convolutional layers. Recommended value is 4.
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"""
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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(
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width: int, window_size: int, depth: int
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) -> Model[List[Floats2d], List[Floats2d]]:
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"""Encode context using convolutions with mish activation, layer
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normalization and residual connections.
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width (int): The input and output width. These are required to be the same,
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to allow residual connections. This value will be determined by the
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width of the inputs. Recommended values are between 64 and 300.
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window_size (int): The number of words to concatenate around each token
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to construct the convolution. Recommended value is 1.
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depth (int): The number of convolutional layers. Recommended value is 4.
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"""
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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), dropout=0.0, normalize=True),
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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(
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width: int, depth: int, dropout: float
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) -> Model[List[Floats2d], List[Floats2d]]:
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"""Encode context using bidirectonal LSTM layers. Requires PyTorch.
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width (int): The input and output width. These are required to be the same,
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to allow residual connections. This value will be determined by the
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width of the inputs. Recommended values are between 64 and 300.
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window_size (int): The number of words to concatenate around each token
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to construct the convolution. Recommended value is 1.
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depth (int): The number of convolutional layers. Recommended value is 4.
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"""
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if depth == 0:
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return noop()
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return with_padded(PyTorchLSTM(width, width, bi=True, depth=depth, dropout=dropout))
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