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https://github.com/explosion/spaCy.git
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e5debc68e4
* Tagger: use unnormalized probabilities for inference Using unnormalized softmax avoids use of the relatively expensive exp function, which can significantly speed up non-transformer models (e.g. I got a speedup of 27% on a German tagging + parsing pipeline). * Add spacy.Tagger.v2 with configurable normalization Normalization of probabilities is disabled by default to improve performance. * Update documentation, models, and tests to spacy.Tagger.v2 * Move Tagger.v1 to spacy-legacy * docs/architectures: run prettier * Unnormalized softmax is now a Softmax_v2 option * Require thinc 8.0.14 and spacy-legacy 3.0.9
136 lines
3.5 KiB
Python
136 lines
3.5 KiB
Python
import re
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import pickle
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import pytest
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from spacy.language import Language
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from spacy.lang.it import Italian
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from spacy.lang.en import English
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from spacy.tokenizer import Tokenizer
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from spacy.training import Example
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from spacy.util import load_config_from_str
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from ..util import make_tempdir
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@pytest.fixture
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def meta_data():
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return {
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"name": "name-in-fixture",
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"version": "version-in-fixture",
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"description": "description-in-fixture",
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"author": "author-in-fixture",
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"email": "email-in-fixture",
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"url": "url-in-fixture",
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"license": "license-in-fixture",
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"vectors": {"width": 0, "vectors": 0, "keys": 0, "name": None},
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}
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@pytest.mark.issue(2482)
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def test_issue2482():
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"""Test we can serialize and deserialize a blank NER or parser model."""
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nlp = Italian()
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nlp.add_pipe("ner")
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b = nlp.to_bytes()
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Italian().from_bytes(b)
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CONFIG_ISSUE_6950 = """
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[nlp]
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lang = "en"
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pipeline = ["tok2vec", "tagger"]
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[components]
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[components.tok2vec]
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factory = "tok2vec"
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[components.tok2vec.model]
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@architectures = "spacy.Tok2Vec.v1"
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[components.tok2vec.model.embed]
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@architectures = "spacy.MultiHashEmbed.v1"
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width = ${components.tok2vec.model.encode:width}
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attrs = ["NORM","PREFIX","SUFFIX","SHAPE"]
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rows = [5000,2500,2500,2500]
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include_static_vectors = false
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[components.tok2vec.model.encode]
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@architectures = "spacy.MaxoutWindowEncoder.v1"
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width = 96
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depth = 4
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window_size = 1
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maxout_pieces = 3
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[components.ner]
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factory = "ner"
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[components.tagger]
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factory = "tagger"
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[components.tagger.model]
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@architectures = "spacy.Tagger.v2"
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nO = null
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[components.tagger.model.tok2vec]
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@architectures = "spacy.Tok2VecListener.v1"
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width = ${components.tok2vec.model.encode:width}
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upstream = "*"
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"""
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@pytest.mark.issue(6950)
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def test_issue6950():
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"""Test that the nlp object with initialized tok2vec with listeners pickles
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correctly (and doesn't have lambdas).
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"""
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nlp = English.from_config(load_config_from_str(CONFIG_ISSUE_6950))
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nlp.initialize(lambda: [Example.from_dict(nlp.make_doc("hello"), {"tags": ["V"]})])
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pickle.dumps(nlp)
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nlp("hello")
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pickle.dumps(nlp)
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def test_serialize_language_meta_disk(meta_data):
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language = Language(meta=meta_data)
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with make_tempdir() as d:
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language.to_disk(d)
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new_language = Language().from_disk(d)
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assert new_language.meta == language.meta
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def test_serialize_with_custom_tokenizer():
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"""Test that serialization with custom tokenizer works without token_match.
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See: https://support.prodi.gy/t/how-to-save-a-custom-tokenizer/661/2
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"""
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prefix_re = re.compile(r"""1/|2/|:[0-9][0-9][A-K]:|:[0-9][0-9]:""")
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suffix_re = re.compile(r"""""")
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infix_re = re.compile(r"""[~]""")
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def custom_tokenizer(nlp):
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return Tokenizer(
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nlp.vocab,
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{},
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prefix_search=prefix_re.search,
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suffix_search=suffix_re.search,
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infix_finditer=infix_re.finditer,
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)
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nlp = Language()
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nlp.tokenizer = custom_tokenizer(nlp)
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with make_tempdir() as d:
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nlp.to_disk(d)
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def test_serialize_language_exclude(meta_data):
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name = "name-in-fixture"
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nlp = Language(meta=meta_data)
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assert nlp.meta["name"] == name
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new_nlp = Language().from_bytes(nlp.to_bytes())
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assert new_nlp.meta["name"] == name
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new_nlp = Language().from_bytes(nlp.to_bytes(), exclude=["meta"])
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assert not new_nlp.meta["name"] == name
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new_nlp = Language().from_bytes(nlp.to_bytes(exclude=["meta"]))
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assert not new_nlp.meta["name"] == name
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