mirror of
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115 lines
3.9 KiB
Python
115 lines
3.9 KiB
Python
# coding: utf8
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from __future__ import print_function
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# NB! This breaks in plac on Python 2!!
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#from __future__ import unicode_literals
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import plac
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from spacy.cli import download as cli_download
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from spacy.cli import link as cli_link
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from spacy.cli import info as cli_info
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from spacy.cli import package as cli_package
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from spacy.cli import train as cli_train
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from spacy.cli import train_config as cli_train_config
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class CLI(object):
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"""Command-line interface for spaCy"""
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commands = ('download', 'link', 'info', 'package', 'train', 'train_config')
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@plac.annotations(
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model=("model to download (shortcut or model name)", "positional", None, str),
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direct=("force direct download. Needs model name with version and won't "
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"perform compatibility check", "flag", "d", bool)
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)
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def download(self, model=None, direct=False):
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"""
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Download compatible model from default download path using pip. Model
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can be shortcut, model name or, if --direct flag is set, full model name
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with version.
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"""
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cli_download(model, direct)
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@plac.annotations(
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origin=("package name or local path to model", "positional", None, str),
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link_name=("name of shortuct link to create", "positional", None, str),
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force=("force overwriting of existing link", "flag", "f", bool)
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)
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def link(self, origin, link_name, force=False):
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"""
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Create a symlink for models within the spacy/data directory. Accepts
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either the name of a pip package, or the local path to the model data
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directory. Linking models allows loading them via spacy.load(link_name).
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"""
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cli_link(origin, link_name, force)
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@plac.annotations(
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model=("optional: shortcut link of model", "positional", None, str),
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markdown=("generate Markdown for GitHub issues", "flag", "md", str)
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)
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def info(self, model=None, markdown=False):
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"""
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Print info about spaCy installation. If a model shortcut link is
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speficied as an argument, print model information. Flag --markdown
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prints details in Markdown for easy copy-pasting to GitHub issues.
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"""
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cli_info(model, markdown)
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@plac.annotations(
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input_dir=("directory with model data", "positional", None, str),
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output_dir=("output parent directory", "positional", None, str),
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force=("force overwriting of existing folder in output directory", "flag", "f", bool)
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)
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def package(self, input_dir, output_dir, force=False):
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"""
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Generate Python package for model data, including meta and required
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installation files. A new directory will be created in the specified
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output directory, and model data will be copied over.
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"""
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cli_package(input_dir, output_dir, force)
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@plac.annotations(
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lang=("language", "positional", None, str),
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output_dir=("output directory", "positional", None, str),
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train_data=("training data", "positional", None, str),
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dev_data=("development data", "positional", None, str),
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n_iter=("number of iterations", "flag", "n", int),
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tagger=("train tagger", "flag", "t", bool),
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parser=("train parser", "flag", "p", bool),
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ner=("train NER", "flag", "n", bool)
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)
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def train(self, lang, output_dir, train_data, dev_data, n_iter=15, tagger=True,
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parser=True, ner=True):
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"""Train a model."""
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cli_train(output_dir, train_data, dev_data, tagger, parser, ner)
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@plac.annotations(
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config=("config", "positional", None, str),
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)
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def train_config(self, config):
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"""Train a model from config file."""
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cli_train_config(config)
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def __missing__(self, name):
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print("\n Command %r does not exist\n" % name)
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if __name__ == '__main__':
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import plac
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import sys
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cli = CLI()
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sys.argv[0] = 'spacy'
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plac.Interpreter.call(CLI)
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