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* 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
39 lines
864 B
Plaintext
39 lines
864 B
Plaintext
# Our libraries
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spacy-legacy>=3.0.9,<3.1.0
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spacy-loggers>=1.0.0,<2.0.0
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cymem>=2.0.2,<2.1.0
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preshed>=3.0.2,<3.1.0
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thinc>=8.0.14,<8.1.0
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blis>=0.4.0,<0.8.0
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ml_datasets>=0.2.0,<0.3.0
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murmurhash>=0.28.0,<1.1.0
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wasabi>=0.8.1,<1.1.0
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srsly>=2.4.1,<3.0.0
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catalogue>=2.0.6,<2.1.0
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typer>=0.3.0,<0.5.0
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pathy>=0.3.5
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# Third party dependencies
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numpy>=1.15.0
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requests>=2.13.0,<3.0.0
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tqdm>=4.38.0,<5.0.0
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pydantic>=1.7.4,!=1.8,!=1.8.1,<1.9.0
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jinja2
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langcodes>=3.2.0,<4.0.0
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# Official Python utilities
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setuptools
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packaging>=20.0
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typing_extensions>=3.7.4.1,<4.0.0.0; python_version < "3.8"
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# Development dependencies
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pre-commit>=2.13.0
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cython>=0.25,<3.0
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pytest>=5.2.0,<7.1.0
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pytest-timeout>=1.3.0,<2.0.0
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mock>=2.0.0,<3.0.0
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flake8>=3.8.0,<3.10.0
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hypothesis>=3.27.0,<7.0.0
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mypy==0.910
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types-dataclasses>=0.1.3; python_version < "3.7"
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types-mock>=0.1.1
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types-requests
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black>=22.0,<23.0
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