mirror of
https://github.com/explosion/spaCy.git
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140 lines
3.6 KiB
Python
140 lines
3.6 KiB
Python
from thinc.api import Config
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import spacy
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from spacy import util
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from spacy.lang.en import English
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from spacy.util import registry
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from ..util import make_tempdir
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from ...ml.models import build_Tok2Vec_model, build_tb_parser_model
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nlp_config_string = """
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[nlp]
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lang = "en"
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[nlp.pipeline.tok2vec]
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factory = "tok2vec"
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[nlp.pipeline.tok2vec.model]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 342
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depth = 4
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window_size = 1
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embed_size = 2000
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maxout_pieces = 3
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subword_features = true
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dropout = null
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[nlp.pipeline.tagger]
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factory = "tagger"
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[nlp.pipeline.tagger.model]
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@architectures = "spacy.Tagger.v1"
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[nlp.pipeline.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecTensors.v1"
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width = ${nlp.pipeline.tok2vec.model:width}
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"""
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parser_config_string = """
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[model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 99
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hidden_width = 66
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maxout_pieces = 2
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[model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 333
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depth = 4
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embed_size = 5555
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window_size = 1
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maxout_pieces = 7
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subword_features = false
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dropout = null
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"""
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@registry.architectures.register("my_test_parser")
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def my_parser():
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tok2vec = build_Tok2Vec_model(
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width=321,
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embed_size=5432,
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pretrained_vectors=None,
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window_size=3,
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maxout_pieces=4,
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subword_features=True,
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char_embed=True,
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nM=64,
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nC=8,
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conv_depth=2,
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bilstm_depth=0,
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dropout=None,
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)
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parser = build_tb_parser_model(
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tok2vec=tok2vec, nr_feature_tokens=7, hidden_width=65, maxout_pieces=5
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)
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return parser
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def test_serialize_nlp():
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""" Create a custom nlp pipeline from config and ensure it serializes it correctly """
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nlp_config = Config().from_str(nlp_config_string)
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nlp = util.load_model_from_config(nlp_config["nlp"])
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nlp.begin_training()
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assert "tok2vec" in nlp.pipe_names
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assert "tagger" in nlp.pipe_names
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assert "parser" not in nlp.pipe_names
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assert nlp.get_pipe("tagger").model.get_ref("tok2vec").get_dim("nO") == 342
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with make_tempdir() as d:
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nlp.to_disk(d)
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nlp2 = spacy.load(d)
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assert "tok2vec" in nlp2.pipe_names
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assert "tagger" in nlp2.pipe_names
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assert "parser" not in nlp2.pipe_names
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assert nlp2.get_pipe("tagger").model.get_ref("tok2vec").get_dim("nO") == 342
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def test_serialize_custom_nlp():
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""" Create a custom nlp pipeline and ensure it serializes it correctly"""
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nlp = English()
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parser_cfg = dict()
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parser_cfg["model"] = {"@architectures": "my_test_parser"}
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parser = nlp.create_pipe("parser", parser_cfg)
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nlp.add_pipe(parser)
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nlp.begin_training()
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with make_tempdir() as d:
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nlp.to_disk(d)
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nlp2 = spacy.load(d)
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model = nlp2.get_pipe("parser").model
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tok2vec = model.get_ref("tok2vec")
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upper = model.get_ref("upper")
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# check that we have the correct settings, not the default ones
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assert upper.get_dim("nI") == 65
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def test_serialize_parser():
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""" Create a non-default parser config to check nlp serializes it correctly """
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nlp = English()
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model_config = Config().from_str(parser_config_string)
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parser = nlp.create_pipe("parser", config=model_config)
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parser.add_label("nsubj")
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nlp.add_pipe(parser)
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nlp.begin_training()
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with make_tempdir() as d:
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nlp.to_disk(d)
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nlp2 = spacy.load(d)
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model = nlp2.get_pipe("parser").model
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tok2vec = model.get_ref("tok2vec")
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upper = model.get_ref("upper")
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# check that we have the correct settings, not the default ones
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assert upper.get_dim("nI") == 66
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