mirror of https://github.com/explosion/spaCy.git
140 lines
4.2 KiB
Python
140 lines
4.2 KiB
Python
from __future__ import unicode_literals
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from thinc.api import chain, layerize, clone, concatenate, with_flatten, uniqued
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from thinc.api import noop, with_square_sequences
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from thinc.v2v import Maxout
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from thinc.i2v import HashEmbed, StaticVectors
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from thinc.t2t import ExtractWindow
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from thinc.misc import Residual, LayerNorm, FeatureExtracter
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from ..util import make_layer, register_architecture
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from ._wire import concatenate_lists
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@register_architecture("spacy.Tok2Vec.v1")
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def Tok2Vec(config):
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doc2feats = make_layer(config["@doc2feats"])
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embed = make_layer(config["@embed"])
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encode = make_layer(config["@encode"])
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depth = config["@encode"]["config"]["depth"]
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tok2vec = chain(doc2feats, with_flatten(chain(embed, encode), pad=depth))
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tok2vec.cfg = config
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tok2vec.nO = encode.nO
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tok2vec.embed = embed
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tok2vec.encode = encode
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return tok2vec
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@register_architecture("spacy.Doc2Feats.v1")
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def Doc2Feats(config):
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columns = config["columns"]
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return FeatureExtracter(columns)
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@register_architecture("spacy.MultiHashEmbed.v1")
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def MultiHashEmbed(config):
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cols = config["columns"]
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width = config["width"]
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rows = config["rows"]
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tables = [HashEmbed(width, rows, column=cols.index("NORM"), name="embed_norm")]
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if config["use_subwords"]:
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for feature in ["PREFIX", "SUFFIX", "SHAPE"]:
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tables.append(
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HashEmbed(
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width,
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rows // 2,
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column=cols.index(feature),
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name="embed_%s" % feature.lower(),
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)
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)
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if config.get("@pretrained_vectors"):
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tables.append(make_layer(config["@pretrained_vectors"]))
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mix = make_layer(config["@mix"])
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# This is a pretty ugly hack. Not sure what the best solution should be.
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mix._layers[0].nI = sum(table.nO for table in tables)
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layer = uniqued(chain(concatenate(*tables), mix), column=cols.index("ORTH"))
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layer.cfg = config
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return layer
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@register_architecture("spacy.CharacterEmbed.v1")
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def CharacterEmbed(config):
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width = config["width"]
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chars = config["chars"]
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chr_embed = CharacterEmbed(nM=width, nC=chars)
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other_tables = make_layer(config["@embed_features"])
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mix = make_layer(config["@mix"])
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model = chain(concatenate_lists(chr_embed, other_tables), mix)
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model.cfg = config
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return model
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@register_architecture("spacy.MaxoutWindowEncoder.v1")
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def MaxoutWindowEncoder(config):
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nO = config["width"]
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nW = config["window_size"]
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nP = config["pieces"]
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depth = config["depth"]
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cnn = chain(
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ExtractWindow(nW=nW), LayerNorm(Maxout(nO, nO * ((nW * 2) + 1), pieces=nP))
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)
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model = clone(Residual(cnn), depth)
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model.nO = nO
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return model
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@register_architecture("spacy.PretrainedVectors.v1")
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def PretrainedVectors(config):
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return StaticVectors(config["vectors_name"], config["width"], config["column"])
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@register_architecture("spacy.TorchBiLSTMEncoder.v1")
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def TorchBiLSTMEncoder(config):
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import torch.nn
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from thinc.extra.wrappers import PyTorchWrapperRNN
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width = config["width"]
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depth = config["depth"]
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if depth == 0:
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return layerize(noop())
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return with_square_sequences(
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PyTorchWrapperRNN(torch.nn.LSTM(width, width // 2, depth, bidirectional=True))
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)
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_EXAMPLE_CONFIG = {
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"@doc2feats": {
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"arch": "Doc2Feats",
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"config": {"columns": ["ID", "NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"]},
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},
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"@embed": {
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"arch": "spacy.MultiHashEmbed.v1",
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"config": {
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"width": 96,
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"rows": 2000,
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"columns": ["ID", "NORM", "PREFIX", "SUFFIX", "SHAPE", "ORTH"],
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"use_subwords": True,
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"@pretrained_vectors": {
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"arch": "TransformedStaticVectors",
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"config": {
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"vectors_name": "en_vectors_web_lg.vectors",
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"width": 96,
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"column": 0,
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},
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},
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"@mix": {
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"arch": "LayerNormalizedMaxout",
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"config": {"width": 96, "pieces": 3},
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},
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},
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},
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"@encode": {
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"arch": "MaxoutWindowEncode",
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"config": {"width": 96, "window_size": 1, "depth": 4, "pieces": 3},
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},
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}
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