mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-11 12:18:04 +03:00
Merge branch 'master' into spacy.io
This commit is contained in:
commit
d7a94edba6
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@ -4,7 +4,7 @@ preshed>=3.0.2,<3.1.0
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thinc>=7.3.0,<7.4.0
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blis>=0.4.0,<0.5.0
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murmurhash>=0.28.0,<1.1.0
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wasabi>=0.3.0,<1.1.0
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wasabi>=0.4.0,<1.1.0
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srsly>=0.1.0,<1.1.0
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# Third party dependencies
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numpy>=1.15.0
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10
setup.cfg
10
setup.cfg
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@ -40,17 +40,19 @@ setup_requires =
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murmurhash>=0.28.0,<1.1.0
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thinc>=7.3.0,<7.4.0
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install_requires =
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setuptools
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numpy>=1.15.0
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# Our libraries
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murmurhash>=0.28.0,<1.1.0
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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>=7.3.0,<7.4.0
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blis>=0.4.0,<0.5.0
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wasabi>=0.4.0,<1.1.0
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srsly>=0.1.0,<1.1.0
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# Third-party dependencies
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setuptools
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numpy>=1.15.0
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plac>=0.9.6,<1.2.0
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requests>=2.13.0,<3.0.0
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wasabi>=0.3.0,<1.1.0
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srsly>=0.1.0,<1.1.0
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pathlib==1.0.1; python_version < "3.4"
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importlib_metadata>=0.20; python_version < "3.8"
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@ -7,12 +7,10 @@ from __future__ import print_function
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if __name__ == "__main__":
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import plac
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import sys
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from wasabi import Printer
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from wasabi import msg
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from spacy.cli import download, link, info, package, train, pretrain, convert
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from spacy.cli import init_model, profile, evaluate, validate, debug_data
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msg = Printer()
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commands = {
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"download": download,
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"link": link,
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@ -121,6 +121,8 @@ def debug_data(
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msg.text("{} training docs".format(len(train_docs)))
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msg.text("{} evaluation docs".format(len(dev_docs)))
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if not len(dev_docs):
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msg.fail("No evaluation docs")
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overlap = len(train_texts.intersection(dev_texts))
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if overlap:
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msg.warn("{} training examples also in evaluation data".format(overlap))
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@ -6,16 +6,13 @@ import requests
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import os
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import subprocess
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import sys
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from wasabi import Printer
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from wasabi import msg
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from .link import link
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from ..util import get_package_path
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from .. import about
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msg = Printer()
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@plac.annotations(
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model=("Model to download (shortcut or name)", "positional", None, str),
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direct=("Force direct download of name + version", "flag", "d", bool),
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@ -3,7 +3,7 @@ from __future__ import unicode_literals, division, print_function
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import plac
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from timeit import default_timer as timer
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from wasabi import Printer
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from wasabi import msg
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from ..gold import GoldCorpus
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from .. import util
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@ -32,7 +32,6 @@ def evaluate(
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Evaluate a model. To render a sample of parses in a HTML file, set an
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output directory as the displacy_path argument.
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"""
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msg = Printer()
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util.fix_random_seed()
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if gpu_id >= 0:
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util.use_gpu(gpu_id)
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@ -4,7 +4,7 @@ from __future__ import unicode_literals
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import plac
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import platform
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from pathlib import Path
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from wasabi import Printer
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from wasabi import msg
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import srsly
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from ..compat import path2str, basestring_, unicode_
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@ -23,7 +23,6 @@ def info(model=None, markdown=False, silent=False):
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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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msg = Printer()
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if model:
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if util.is_package(model):
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model_path = util.get_package_path(model)
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@ -11,7 +11,7 @@ import tarfile
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import gzip
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import zipfile
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import srsly
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from wasabi import Printer
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from wasabi import msg
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from ..vectors import Vectors
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from ..errors import Errors, Warnings, user_warning
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@ -24,7 +24,6 @@ except ImportError:
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DEFAULT_OOV_PROB = -20
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msg = Printer()
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@plac.annotations(
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@ -3,7 +3,7 @@ from __future__ import unicode_literals
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import plac
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from pathlib import Path
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from wasabi import Printer
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from wasabi import msg
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from ..compat import symlink_to, path2str
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from .. import util
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@ -20,7 +20,6 @@ def link(origin, link_name, force=False, model_path=None):
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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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msg = Printer()
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if util.is_package(origin):
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model_path = util.get_package_path(origin)
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else:
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@ -4,7 +4,7 @@ from __future__ import unicode_literals
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import plac
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import shutil
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from pathlib import Path
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from wasabi import Printer, get_raw_input
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from wasabi import msg, get_raw_input
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import srsly
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from ..compat import path2str
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@ -27,7 +27,6 @@ def package(input_dir, output_dir, meta_path=None, create_meta=False, force=Fals
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set and a meta.json already exists in the output directory, the existing
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values will be used as the defaults in the command-line prompt.
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"""
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msg = Printer()
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input_path = util.ensure_path(input_dir)
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output_path = util.ensure_path(output_dir)
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meta_path = util.ensure_path(meta_path)
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@ -11,7 +11,7 @@ from pathlib import Path
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from thinc.v2v import Affine, Maxout
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from thinc.misc import LayerNorm as LN
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from thinc.neural.util import prefer_gpu
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from wasabi import Printer
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from wasabi import msg
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import srsly
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from ..errors import Errors
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@ -122,7 +122,6 @@ def pretrain(
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for key in config:
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if isinstance(config[key], Path):
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config[key] = str(config[key])
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msg = Printer()
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util.fix_random_seed(seed)
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has_gpu = prefer_gpu()
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@ -9,7 +9,7 @@ import pstats
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import sys
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import itertools
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import thinc.extra.datasets
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from wasabi import Printer
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from wasabi import msg
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from ..util import load_model
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@ -26,7 +26,6 @@ def profile(model, inputs=None, n_texts=10000):
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It can either be provided as a JSONL file, or be read from sys.sytdin.
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If no input file is specified, the IMDB dataset is loaded via Thinc.
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"""
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msg = Printer()
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if inputs is not None:
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inputs = _read_inputs(inputs, msg)
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if inputs is None:
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@ -8,7 +8,7 @@ from thinc.neural._classes.model import Model
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from timeit import default_timer as timer
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import shutil
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import srsly
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from wasabi import Printer
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from wasabi import msg
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import contextlib
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import random
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@ -89,7 +89,6 @@ def train(
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# temp fix to avoid import issues cf https://github.com/explosion/spaCy/issues/4200
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import tqdm
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msg = Printer()
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util.fix_random_seed()
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util.set_env_log(verbose)
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@ -5,7 +5,7 @@ from pathlib import Path
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import sys
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import requests
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import srsly
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from wasabi import Printer
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from wasabi import msg
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from ..compat import path2str
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from ..util import get_data_path
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@ -17,7 +17,6 @@ def validate():
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Validate that the currently installed version of spaCy is compatible
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with the installed models. Should be run after `pip install -U spacy`.
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"""
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msg = Printer()
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with msg.loading("Loading compatibility table..."):
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r = requests.get(about.__compatibility__)
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if r.status_code != 200:
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@ -82,6 +82,7 @@ class Scorer(object):
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self.sbd = PRFScore()
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self.unlabelled = PRFScore()
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self.labelled = PRFScore()
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self.labelled_per_dep = dict()
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self.tags = PRFScore()
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self.ner = PRFScore()
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self.ner_per_ents = dict()
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@ -124,9 +125,18 @@ class Scorer(object):
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@property
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def las(self):
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"""RETURNS (float): Labelled depdendency score."""
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"""RETURNS (float): Labelled dependency score."""
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return self.labelled.fscore * 100
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@property
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def las_per_type(self):
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"""RETURNS (dict): Scores per dependency label.
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"""
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return {
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k: {"p": v.precision * 100, "r": v.recall * 100, "f": v.fscore * 100}
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for k, v in self.labelled_per_dep.items()
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}
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@property
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def ents_p(self):
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"""RETURNS (float): Named entity accuracy (precision)."""
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@ -196,6 +206,7 @@ class Scorer(object):
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return {
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"uas": self.uas,
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"las": self.las,
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"las_per_type": self.las_per_type,
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"ents_p": self.ents_p,
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"ents_r": self.ents_r,
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"ents_f": self.ents_f,
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@ -223,13 +234,20 @@ class Scorer(object):
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doc, tuple(zip(*gold.orig_annot)) + (gold.cats,)
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)
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gold_deps = set()
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gold_deps_per_dep = {}
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gold_tags = set()
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gold_ents = set(tags_to_entities([annot[-1] for annot in gold.orig_annot]))
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for id_, word, tag, head, dep, ner in gold.orig_annot:
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gold_tags.add((id_, tag))
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if dep not in (None, "") and dep.lower() not in punct_labels:
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gold_deps.add((id_, head, dep.lower()))
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if dep.lower() not in self.labelled_per_dep:
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self.labelled_per_dep[dep.lower()] = PRFScore()
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if dep.lower() not in gold_deps_per_dep:
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gold_deps_per_dep[dep.lower()] = set()
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gold_deps_per_dep[dep.lower()].add((id_, head, dep.lower()))
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cand_deps = set()
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cand_deps_per_dep = {}
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cand_tags = set()
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for token in doc:
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if token.orth_.isspace():
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@ -249,6 +267,11 @@ class Scorer(object):
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self.labelled.fp += 1
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else:
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cand_deps.add((gold_i, gold_head, token.dep_.lower()))
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if token.dep_.lower() not in self.labelled_per_dep:
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self.labelled_per_dep[token.dep_.lower()] = PRFScore()
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if token.dep_.lower() not in cand_deps_per_dep:
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cand_deps_per_dep[token.dep_.lower()] = set()
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cand_deps_per_dep[token.dep_.lower()].add((gold_i, gold_head, token.dep_.lower()))
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if "-" not in [token[-1] for token in gold.orig_annot]:
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# Find all NER labels in gold and doc
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ent_labels = set([x[0] for x in gold_ents] + [k.label_ for k in doc.ents])
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@ -280,6 +303,8 @@ class Scorer(object):
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self.ner.score_set(cand_ents, gold_ents)
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self.tags.score_set(cand_tags, gold_tags)
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self.labelled.score_set(cand_deps, gold_deps)
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for dep in self.labelled_per_dep:
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self.labelled_per_dep[dep].score_set(cand_deps_per_dep.get(dep, set()), gold_deps_per_dep.get(dep, set()))
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self.unlabelled.score_set(
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set(item[:2] for item in cand_deps), set(item[:2] for item in gold_deps)
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)
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|
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@ -9,6 +9,14 @@ from spacy.scorer import Scorer, ROCAUCScore
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from spacy.scorer import _roc_auc_score, _roc_curve
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from .util import get_doc
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test_las_apple = [
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[
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"Apple is looking at buying U.K. startup for $ 1 billion",
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{"heads": [2, 2, 2, 2, 3, 6, 4, 4, 10, 10, 7],
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"deps": ['nsubj', 'aux', 'ROOT', 'prep', 'pcomp', 'compound', 'dobj', 'prep', 'quantmod', 'compound', 'pobj']},
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]
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]
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test_ner_cardinal = [
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["100 - 200", {"entities": [[0, 3, "CARDINAL"], [6, 9, "CARDINAL"]]}]
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]
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@ -21,6 +29,53 @@ test_ner_apple = [
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]
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def test_las_per_type(en_vocab):
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# Gold and Doc are identical
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scorer = Scorer()
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for input_, annot in test_las_apple:
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doc = get_doc(
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en_vocab,
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words=input_.split(" "),
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heads=([h - i for i, h in enumerate(annot["heads"])]),
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deps=annot["deps"],
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)
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gold = GoldParse(doc, heads=annot["heads"], deps=annot["deps"])
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scorer.score(doc, gold)
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results = scorer.scores
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assert results["uas"] == 100
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assert results["las"] == 100
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assert results["las_per_type"]["nsubj"]["p"] == 100
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assert results["las_per_type"]["nsubj"]["r"] == 100
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assert results["las_per_type"]["nsubj"]["f"] == 100
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assert results["las_per_type"]["compound"]["p"] == 100
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assert results["las_per_type"]["compound"]["r"] == 100
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assert results["las_per_type"]["compound"]["f"] == 100
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# One dep is incorrect in Doc
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scorer = Scorer()
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for input_, annot in test_las_apple:
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doc = get_doc(
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en_vocab,
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words=input_.split(" "),
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heads=([h - i for i, h in enumerate(annot["heads"])]),
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deps=annot["deps"]
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)
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gold = GoldParse(doc, heads=annot["heads"], deps=annot["deps"])
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doc[0].dep_ = "compound"
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scorer.score(doc, gold)
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results = scorer.scores
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assert results["uas"] == 100
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assert_almost_equal(results["las"], 90.9090909)
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assert results["las_per_type"]["nsubj"]["p"] == 0
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assert results["las_per_type"]["nsubj"]["r"] == 0
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assert results["las_per_type"]["nsubj"]["f"] == 0
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assert_almost_equal(results["las_per_type"]["compound"]["p"], 66.6666666)
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assert results["las_per_type"]["compound"]["r"] == 100
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assert results["las_per_type"]["compound"]["f"] == 80
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def test_ner_per_type(en_vocab):
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# Gold and Doc are identical
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scorer = Scorer()
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|
|
|
@ -1861,6 +1861,30 @@
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"author_links": {
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"github": "microsoft"
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}
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},
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{
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"id": "dframcy",
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"title": "Dframcy",
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"slogan": "Dataframe Integration with spaCy NLP",
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"github": "yash1994/dframcy",
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"description": "DframCy is a light-weight utility module to integrate Pandas Dataframe to spaCy's linguistic annotation and training tasks.",
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"pip": "dframcy",
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"category": ["pipeline", "training"],
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"tags": ["pandas"],
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"code_example": [
|
||||
"import spacy",
|
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"from dframcy import DframCy",
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"",
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"nlp = spacy.load('en_core_web_sm')",
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"dframcy = DframCy(nlp)",
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"doc = dframcy.nlp(u'Apple is looking at buying U.K. startup for $1 billion')",
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"annotation_dataframe = dframcy.to_dataframe(doc)"
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],
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"author": "Yash Patadia",
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"author_links": {
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"twitter": "PatadiaYash",
|
||||
"github": "yash1994"
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||||
}
|
||||
}
|
||||
],
|
||||
|
||||
|
|
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Reference in New Issue
Block a user