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def detailed_tokens(tokenizer, text):
"""Format Mecab output into a nice data structure, based on Janome.""" |
node = tokenizer.parseToNode(text)
node = node.next # first node is beginning of sentence and empty, skip it
words = []
while node.posid != 0:
surface = node.surface
base = surface # a default value. Updated if available later.
parts = node.feature.split(",")
pos = ","... |
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def symlink_to(orig, dest):
"""Create a symlink. Used for model shortcut links. orig (unicode / Path):
The origin path. dest (unicode / Path):
The destination ... |
if is_windows:
import subprocess
subprocess.check_call(
["mklink", "/d", path2str(orig), path2str(dest)], shell=True
)
else:
orig.symlink_to(dest) |
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def symlink_remove(link):
"""Remove a symlink. Used for model shortcut links. link (unicode / Path):
The path to the symlink. """ |
# https://stackoverflow.com/q/26554135/6400719
if os.path.isdir(path2str(link)) and is_windows:
# this should only be on Py2.7 and windows
os.rmdir(path2str(link))
else:
os.unlink(path2str(link)) |
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def is_config(python2=None, python3=None, windows=None, linux=None, osx=None):
"""Check if a specific configuration of Python version and operating system matche... |
return (
python2 in (None, is_python2)
and python3 in (None, is_python3)
and windows in (None, is_windows)
and linux in (None, is_linux)
and osx in (None, is_osx)
) |
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def import_file(name, loc):
"""Import module from a file. Used to load models from a directory. name (unicode):
Name of module to load. loc (unicode / Path):
P... |
loc = path2str(loc)
if is_python_pre_3_5:
import imp
return imp.load_source(name, loc)
else:
import importlib.util
spec = importlib.util.spec_from_file_location(name, str(loc))
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
... |
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def get_lang_class(lang):
"""Import and load a Language class. lang (unicode):
Two-letter language code, e.g. 'en'. RETURNS (Language):
Language class. """ |
global LANGUAGES
# Check if an entry point is exposed for the language code
entry_point = get_entry_point("spacy_languages", lang)
if entry_point is not None:
LANGUAGES[lang] = entry_point
return entry_point
if lang not in LANGUAGES:
try:
module = importlib.impor... |
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def load_model(name, **overrides):
"""Load a model from a shortcut link, package or data path. name (unicode):
Package name, shortcut link or model path. **over... |
data_path = get_data_path()
if not data_path or not data_path.exists():
raise IOError(Errors.E049.format(path=path2str(data_path)))
if isinstance(name, basestring_): # in data dir / shortcut
if name in set([d.name for d in data_path.iterdir()]):
return load_model_from_link(name... |
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def load_model_from_link(name, **overrides):
"""Load a model from a shortcut link, or directory in spaCy data path.""" |
path = get_data_path() / name / "__init__.py"
try:
cls = import_file(name, path)
except AttributeError:
raise IOError(Errors.E051.format(name=name))
return cls.load(**overrides) |
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def load_model_from_package(name, **overrides):
"""Load a model from an installed package.""" |
cls = importlib.import_module(name)
return cls.load(**overrides) |
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def get_model_meta(path):
"""Get model meta.json from a directory path and validate its contents. path (unicode or Path):
Path to model directory. RETURNS (dict... |
model_path = ensure_path(path)
if not model_path.exists():
raise IOError(Errors.E052.format(path=path2str(model_path)))
meta_path = model_path / "meta.json"
if not meta_path.is_file():
raise IOError(Errors.E053.format(path=meta_path))
meta = srsly.read_json(meta_path)
for settin... |
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def get_package_path(name):
"""Get the path to an installed package. name (unicode):
Package name. RETURNS (Path):
Path to installed package. """ |
name = name.lower() # use lowercase version to be safe
# Here we're importing the module just to find it. This is worryingly
# indirect, but it's otherwise very difficult to find the package.
pkg = importlib.import_module(name)
return Path(pkg.__file__).parent |
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def get_entry_point(key, value):
"""Check if registered entry point is available for a given name and load it. Otherwise, return None. key (unicode):
Entry poin... |
for entry_point in pkg_resources.iter_entry_points(key):
if entry_point.name == value:
return entry_point.load() |
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def compile_suffix_regex(entries):
"""Compile a sequence of suffix rules into a regex object. entries (tuple):
The suffix rules, e.g. spacy.lang.punctuation.TOK... |
expression = "|".join([piece + "$" for piece in entries if piece.strip()])
return re.compile(expression) |
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def compile_infix_regex(entries):
"""Compile a sequence of infix rules into a regex object. entries (tuple):
The infix rules, e.g. spacy.lang.punctuation.TOKENI... |
expression = "|".join([piece for piece in entries if piece.strip()])
return re.compile(expression) |
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def expand_exc(excs, search, replace):
"""Find string in tokenizer exceptions, duplicate entry and replace string. For example, to add additional versions with t... |
def _fix_token(token, search, replace):
fixed = dict(token)
fixed[ORTH] = fixed[ORTH].replace(search, replace)
return fixed
new_excs = dict(excs)
for token_string, tokens in excs.items():
if search in token_string:
new_key = token_string.replace(search, replace... |
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def minibatch(items, size=8):
"""Iterate over batches of items. `size` may be an iterator, so that batch-size can vary on each step. """ |
if isinstance(size, int):
size_ = itertools.repeat(size)
else:
size_ = size
items = iter(items)
while True:
batch_size = next(size_)
batch = list(itertools.islice(items, int(batch_size)))
if len(batch) == 0:
break
yield list(batch) |
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def minibatch_by_words(items, size, tuples=True, count_words=len):
"""Create minibatches of a given number of words.""" |
if isinstance(size, int):
size_ = itertools.repeat(size)
else:
size_ = size
items = iter(items)
while True:
batch_size = next(size_)
batch = []
while batch_size >= 0:
try:
if tuples:
doc, gold = next(items)
... |
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def labels(self):
"""All labels present in the match patterns. RETURNS (set):
The string labels. DOCS: https://spacy.io/api/entityruler#labels """ |
all_labels = set(self.token_patterns.keys())
all_labels.update(self.phrase_patterns.keys())
return tuple(all_labels) |
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def patterns(self):
"""Get all patterns that were added to the entity ruler. RETURNS (list):
The original patterns, one dictionary per pattern. DOCS: https://sp... |
all_patterns = []
for label, patterns in self.token_patterns.items():
for pattern in patterns:
all_patterns.append({"label": label, "pattern": pattern})
for label, patterns in self.phrase_patterns.items():
for pattern in patterns:
all_patt... |
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def from_bytes(self, patterns_bytes, **kwargs):
"""Load the entity ruler from a bytestring. patterns_bytes (bytes):
The bytestring to load. **kwargs: Other conf... |
patterns = srsly.msgpack_loads(patterns_bytes)
self.add_patterns(patterns)
return self |
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def golds_to_gold_tuples(docs, golds):
"""Get out the annoying 'tuples' format used by begin_training, given the GoldParse objects.""" |
tuples = []
for doc, gold in zip(docs, golds):
text = doc.text
ids, words, tags, heads, labels, iob = zip(*gold.orig_annot)
sents = [((ids, words, tags, heads, labels, iob), [])]
tuples.append((text, sents))
return tuples |
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def merge_bytes(binder_strings):
"""Concatenate multiple serialized binders into one byte string.""" |
output = None
for byte_string in binder_strings:
binder = Binder().from_bytes(byte_string)
if output is None:
output = binder
else:
output.merge(binder)
return output.to_bytes() |
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def add(self, doc):
"""Add a doc's annotations to the binder for serialization.""" |
array = doc.to_array(self.attrs)
if len(array.shape) == 1:
array = array.reshape((array.shape[0], 1))
self.tokens.append(array)
spaces = doc.to_array(SPACY)
assert array.shape[0] == spaces.shape[0]
spaces = spaces.reshape((spaces.shape[0], 1))
self.sp... |
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def get_docs(self, vocab):
"""Recover Doc objects from the annotations, using the given vocab.""" |
for string in self.strings:
vocab[string]
orth_col = self.attrs.index(ORTH)
for tokens, spaces in zip(self.tokens, self.spaces):
words = [vocab.strings[orth] for orth in tokens[:, orth_col]]
doc = Doc(vocab, words=words, spaces=spaces)
doc = doc.f... |
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def merge(self, other):
"""Extend the annotations of this binder with the annotations from another.""" |
assert self.attrs == other.attrs
self.tokens.extend(other.tokens)
self.spaces.extend(other.spaces)
self.strings.update(other.strings) |
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def to_bytes(self):
"""Serialize the binder's annotations into a byte string.""" |
for tokens in self.tokens:
assert len(tokens.shape) == 2, tokens.shape
lengths = [len(tokens) for tokens in self.tokens]
msg = {
"attrs": self.attrs,
"tokens": numpy.vstack(self.tokens).tobytes("C"),
"spaces": numpy.vstack(self.spaces).tobytes("C"... |
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def from_bytes(self, string):
"""Deserialize the binder's annotations from a byte string.""" |
msg = srsly.msgpack_loads(gzip.decompress(string))
self.attrs = msg["attrs"]
self.strings = set(msg["strings"])
lengths = numpy.fromstring(msg["lengths"], dtype="int32")
flat_spaces = numpy.fromstring(msg["spaces"], dtype=bool)
flat_tokens = numpy.fromstring(msg["tokens"... |
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def is_base_form(self, univ_pos, morphology=None):
""" Check whether we're dealing with an uninflected paradigm, so we can avoid lemmatization entirely. """ |
morphology = {} if morphology is None else morphology
others = [key for key in morphology
if key not in (POS, 'Number', 'POS', 'VerbForm', 'Tense')]
if univ_pos == 'noun' and morphology.get('Number') == 'sing':
return True
elif univ_pos == 'verb' and morpho... |
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def main(model=None, new_model_name="animal", output_dir=None, n_iter=30):
"""Set up the pipeline and entity recognizer, and train the new entity.""" |
random.seed(0)
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en") # create blank Language class
print("Created blank 'en' model")
# Add entity recognizer to model if it's not in the... |
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def conll_ner2json(input_data, **kwargs):
""" Convert files in the CoNLL-2003 NER format into JSON format for use with train cli. """ |
delimit_docs = "-DOCSTART- -X- O O"
output_docs = []
for doc in input_data.strip().split(delimit_docs):
doc = doc.strip()
if not doc:
continue
output_doc = []
for sent in doc.split("\n\n"):
sent = sent.strip()
if not sent:
... |
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def main(lang="en", output_dir=None, n_iter=25):
"""Create a new model, set up the pipeline and train the tagger. In order to train the tagger with a custom tag ... |
nlp = spacy.blank(lang)
# add the tagger to the pipeline
# nlp.create_pipe works for built-ins that are registered with spaCy
tagger = nlp.create_pipe("tagger")
# Add the tags. This needs to be done before you start training.
for tag, values in TAG_MAP.items():
tagger.add_label(tag, val... |
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def init_model( lang, output_dir, freqs_loc=None, clusters_loc=None, jsonl_loc=None, vectors_loc=None, prune_vectors=-1, ):
""" Create a new model from raw data,... |
if jsonl_loc is not None:
if freqs_loc is not None or clusters_loc is not None:
settings = ["-j"]
if freqs_loc:
settings.append("-f")
if clusters_loc:
settings.append("-c")
msg.warn(
"Incompatible arguments",
... |
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def main(model=None, output_dir=None, n_iter=100):
"""Load the model, set up the pipeline and train the entity recognizer.""" |
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en") # create blank Language class
print("Created blank 'en' model")
# create the built-in pipeline components and add them to the pipelin... |
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def make_update(model, docs, optimizer, drop=0.0, objective="L2"):
"""Perform an update over a single batch of documents. docs (iterable):
A batch of `Doc` obje... |
predictions, backprop = model.begin_update(docs, drop=drop)
loss, gradients = get_vectors_loss(model.ops, docs, predictions, objective)
backprop(gradients, sgd=optimizer)
# Don't want to return a cupy object here
# The gradients are modified in-place by the BERT MLM,
# so we get an accurate los... |
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def get_vectors_loss(ops, docs, prediction, objective="L2"):
"""Compute a mean-squared error loss between the documents' vectors and the prediction. Note that th... |
# The simplest way to implement this would be to vstack the
# token.vector values, but that's a bit inefficient, especially on GPU.
# Instead we fetch the index into the vectors table for each of our tokens,
# and look them up all at once. This prevents data copying.
ids = ops.flatten([doc.to_array... |
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def _smart_round(figure, width=10, max_decimal=4):
"""Round large numbers as integers, smaller numbers as decimals.""" |
n_digits = len(str(int(figure)))
n_decimal = width - (n_digits + 1)
if n_decimal <= 1:
return str(int(figure))
else:
n_decimal = min(n_decimal, max_decimal)
format_str = "%." + str(n_decimal) + "f"
return format_str % figure |
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def noun_chunks(obj):
""" Detect base noun phrases. Works on both Doc and Span. """ |
# It follows the logic of the noun chunks finder of English language,
# adjusted to some Greek language special characteristics.
# obj tag corrects some DEP tagger mistakes.
# Further improvement of the models will eliminate the need for this tag.
labels = ["nsubj", "obj", "iobj", "appos", "ROOT", ... |
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def get_ext_args(**kwargs):
"""Validate and convert arguments. Reused in Doc, Token and Span.""" |
default = kwargs.get("default")
getter = kwargs.get("getter")
setter = kwargs.get("setter")
method = kwargs.get("method")
if getter is None and setter is not None:
raise ValueError(Errors.E089)
valid_opts = ("default" in kwargs, method is not None, getter is not None)
nr_defined = s... |
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def is_new_osx():
"""Check whether we're on OSX >= 10.10""" |
name = distutils.util.get_platform()
if sys.platform != "darwin":
return False
elif name.startswith("macosx-10"):
minor_version = int(name.split("-")[1].split(".")[1])
if minor_version >= 7:
return True
else:
return False
else:
return Fals... |
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def get_position_label(i, words, tags, heads, labels, ents):
"""Return labels indicating the position of the word in the document. """ |
if len(words) < 20:
return "short-doc"
elif i == 0:
return "first-word"
elif i < 10:
return "early-word"
elif i < 20:
return "mid-word"
elif i == len(words) - 1:
return "last-word"
else:
return "late-word" |
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def load_model(modelname, add_sentencizer=False):
""" Load a specific spaCy model """ |
loading_start = time.time()
nlp = spacy.load(modelname)
if add_sentencizer:
nlp.add_pipe(nlp.create_pipe('sentencizer'))
loading_end = time.time()
loading_time = loading_end - loading_start
if add_sentencizer:
return nlp, loading_time, modelname + '_sentencizer'
return nlp, ... |
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def load_default_model_sentencizer(lang):
""" Load a generic spaCy model and add the sentencizer for sentence tokenization""" |
loading_start = time.time()
lang_class = get_lang_class(lang)
nlp = lang_class()
nlp.add_pipe(nlp.create_pipe('sentencizer'))
loading_end = time.time()
loading_time = loading_end - loading_start
return nlp, loading_time, lang + "_default_" + 'sentencizer' |
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def get_freq_tuples(my_list, print_total_threshold):
""" Turn a list of errors into frequency-sorted tuples thresholded by a certain total number """ |
d = {}
for token in my_list:
d.setdefault(token, 0)
d[token] += 1
return sorted(d.items(), key=operator.itemgetter(1), reverse=True)[:print_total_threshold] |
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def _contains_blinded_text(stats_xml):
""" Heuristic to determine whether the treebank has blinded texts or not """ |
tree = ET.parse(stats_xml)
root = tree.getroot()
total_tokens = int(root.find('size/total/tokens').text)
unique_lemmas = int(root.find('lemmas').get('unique'))
# assume the corpus is largely blinded when there are less than 1% unique tokens
return (unique_lemmas / total_tokens) < 0.01 |
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def fetch_all_treebanks(ud_dir, languages, corpus, best_per_language):
"""" Fetch the txt files for all treebanks for a given set of languages """ |
all_treebanks = dict()
treebank_size = dict()
for l in languages:
all_treebanks[l] = []
treebank_size[l] = 0
for treebank_dir in ud_dir.iterdir():
if treebank_dir.is_dir():
for txt_path in treebank_dir.iterdir():
if txt_path.name.endswith('-ud-' + co... |
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def run_all_evals(models, treebanks, out_file, check_parse, print_freq_tasks):
"""" Run an evaluation for each language with its specified models and treebanks "... |
print_header = True
for tb_lang, treebank_list in treebanks.items():
print()
print("Language", tb_lang)
for text_path in treebank_list:
print(" Evaluating on", text_path)
gold_path = text_path.parent / (text_path.stem + '.conllu')
print(" Gold data... |
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def main(out_path, ud_dir, check_parse=False, langs=ALL_LANGUAGES, exclude_trained_models=False, exclude_multi=False, hide_freq=False, corpus='train', best_per_la... |
languages = [lang.strip() for lang in langs.split(",")]
print_freq_tasks = []
if not hide_freq:
print_freq_tasks = ['Tokens']
# fetching all relevant treebank from the directory
treebanks = fetch_all_treebanks(ud_dir, languages, corpus, best_per_language)
print()
print("Loading a... |
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def noun_chunks(obj):
""" Detect base noun phrases from a dependency parse. Works on both Doc and Span. """ |
# this iterator extracts spans headed by NOUNs starting from the left-most
# syntactic dependent until the NOUN itself for close apposition and
# measurement construction, the span is sometimes extended to the right of
# the NOUN. Example: "eine Tasse Tee" (a cup (of) tea) returns "eine Tasse Tee"
... |
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def with_cpu(ops, model):
"""Wrap a model that should run on CPU, transferring inputs and outputs as necessary.""" |
model.to_cpu()
def with_cpu_forward(inputs, drop=0.0):
cpu_outputs, backprop = model.begin_update(_to_cpu(inputs), drop=drop)
gpu_outputs = _to_device(ops, cpu_outputs)
def with_cpu_backprop(d_outputs, sgd=None):
cpu_d_outputs = _to_cpu(d_outputs)
return backpr... |
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def masked_language_model(vocab, model, mask_prob=0.15):
"""Convert a model into a BERT-style masked language model""" |
random_words = _RandomWords(vocab)
def mlm_forward(docs, drop=0.0):
mask, docs = _apply_mask(docs, random_words, mask_prob=mask_prob)
mask = model.ops.asarray(mask).reshape((mask.shape[0], 1))
output, backprop = model.begin_update(docs, drop=drop)
def mlm_backward(d_output, s... |
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def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs):
"""Allocate model, using width from tensorizer in pipeline. gold_tuples (iterable):
Gold... |
if self.model is True:
self.model = self.Model(pipeline[0].model.nO)
link_vectors_to_models(self.vocab)
if sgd is None:
sgd = self.create_optimizer()
return sgd |
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def render_svg(self, render_id, words, arcs):
"""Render SVG. render_id (int):
Unique ID, typically index of document. words (list):
Individual words and their ... |
self.levels = self.get_levels(arcs)
self.highest_level = len(self.levels)
self.offset_y = self.distance / 2 * self.highest_level + self.arrow_stroke
self.width = self.offset_x + len(words) * self.distance
self.height = self.offset_y + 3 * self.word_spacing
self.id = rend... |
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def render_word(self, text, tag, i):
"""Render individual word. text (unicode):
Word text. tag (unicode):
Part-of-speech tag. i (int):
Unique ID, typically wo... |
y = self.offset_y + self.word_spacing
x = self.offset_x + i * self.distance
if self.direction == "rtl":
x = self.width - x
html_text = escape_html(text)
return TPL_DEP_WORDS.format(text=html_text, tag=tag, x=x, y=y) |
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def render_arrow(self, label, start, end, direction, i):
"""Render individual arrow. label (unicode):
Dependency label. start (int):
Index of start word. end (... |
level = self.levels.index(end - start) + 1
x_start = self.offset_x + start * self.distance + self.arrow_spacing
if self.direction == "rtl":
x_start = self.width - x_start
y = self.offset_y
x_end = (
self.offset_x
+ (end - start) * self.distanc... |
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def get_arc(self, x_start, y, y_curve, x_end):
"""Render individual arc. x_start (int):
X-coordinate of arrow start point. y (int):
Y-coordinate of arrow start... |
template = "M{x},{y} C{x},{c} {e},{c} {e},{y}"
if self.compact:
template = "M{x},{y} {x},{c} {e},{c} {e},{y}"
return template.format(x=x_start, y=y, c=y_curve, e=x_end) |
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def get_arrowhead(self, direction, x, y, end):
"""Render individual arrow head. direction (unicode):
Arrow direction, 'left' or 'right'. x (int):
X-coordinate ... |
if direction == "left":
pos1, pos2, pos3 = (x, x - self.arrow_width + 2, x + self.arrow_width - 2)
else:
pos1, pos2, pos3 = (
end,
end + self.arrow_width - 2,
end - self.arrow_width + 2,
)
arrowhead = (
... |
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def get_levels(self, arcs):
"""Calculate available arc height "levels". Used to calculate arrow heights dynamically and without wasting space. args (list):
Indi... |
levels = set(map(lambda arc: arc["end"] - arc["start"], arcs))
return sorted(list(levels)) |
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def render_ents(self, text, spans, title):
"""Render entities in text. text (unicode):
Original text. spans (list):
Individual entity spans and their start, en... |
markup = ""
offset = 0
for span in spans:
label = span["label"]
start = span["start"]
end = span["end"]
entity = escape_html(text[start:end])
fragments = text[offset:start].split("\n")
for i, fragment in enumerate(fragments... |
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def merge_noun_chunks(doc):
"""Merge noun chunks into a single token. doc (Doc):
The Doc object. RETURNS (Doc):
The Doc object with merged noun chunks. DOCS: h... |
if not doc.is_parsed:
return doc
with doc.retokenize() as retokenizer:
for np in doc.noun_chunks:
attrs = {"tag": np.root.tag, "dep": np.root.dep}
retokenizer.merge(np, attrs=attrs)
return doc |
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def merge_entities(doc):
"""Merge entities into a single token. doc (Doc):
The Doc object. RETURNS (Doc):
The Doc object with merged entities. DOCS: https://sp... |
with doc.retokenize() as retokenizer:
for ent in doc.ents:
attrs = {"tag": ent.root.tag, "dep": ent.root.dep, "ent_type": ent.label}
retokenizer.merge(ent, attrs=attrs)
return doc |
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def merge_subtokens(doc, label="subtok"):
"""Merge subtokens into a single token. doc (Doc):
The Doc object. label (unicode):
The subtoken dependency label. RE... |
merger = Matcher(doc.vocab)
merger.add("SUBTOK", None, [{"DEP": label, "op": "+"}])
matches = merger(doc)
spans = [doc[start : end + 1] for _, start, end in matches]
with doc.retokenize() as retokenizer:
for span in spans:
retokenizer.merge(span)
return doc |
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def _score_for_model(meta):
""" Returns mean score between tasks in pipeline that can be used for early stopping. """ |
mean_acc = list()
pipes = meta["pipeline"]
acc = meta["accuracy"]
if "tagger" in pipes:
mean_acc.append(acc["tags_acc"])
if "parser" in pipes:
mean_acc.append((acc["uas"] + acc["las"]) / 2)
if "ner" in pipes:
mean_acc.append((acc["ents_p"] + acc["ents_r"] + acc["ents_f"]... |
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def _load_pretrained_tok2vec(nlp, loc):
"""Load pre-trained weights for the 'token-to-vector' part of the component models, which is typically a CNN. See 'spacy ... |
with loc.open("rb") as file_:
weights_data = file_.read()
loaded = []
for name, component in nlp.pipeline:
if hasattr(component, "model") and hasattr(component.model, "tok2vec"):
component.tok2vec.from_bytes(weights_data)
loaded.append(name)
return loaded |
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def conllu2json(input_data, n_sents=10, use_morphology=False, lang=None):
""" Convert conllu files into JSON format for use with train cli. use_morphology parame... |
# by @dvsrepo, via #11 explosion/spacy-dev-resources
# by @katarkor
docs = []
sentences = []
conll_tuples = read_conllx(input_data, use_morphology=use_morphology)
checked_for_ner = False
has_ner_tags = False
for i, (raw_text, tokens) in enumerate(conll_tuples):
sentence, bracket... |
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def is_ner(tag):
""" Check the 10th column of the first token to determine if the file contains NER tags """ |
tag_match = re.match("([A-Z_]+)-([A-Z_]+)", tag)
if tag_match:
return True
elif tag == "O":
return True
else:
return False |
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def main(model=None, output_dir=None, n_iter=15):
"""Load the model, set up the pipeline and train the parser.""" |
if model is not None:
nlp = spacy.load(model) # load existing spaCy model
print("Loaded model '%s'" % model)
else:
nlp = spacy.blank("en") # create blank Language class
print("Created blank 'en' model")
# We'll use the built-in dependency parser class, but we want to crea... |
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def get_pipe(self, name):
"""Get a pipeline component for a given component name. name (unicode):
Name of pipeline component to get. RETURNS (callable):
The pi... |
for pipe_name, component in self.pipeline:
if pipe_name == name:
return component
raise KeyError(Errors.E001.format(name=name, opts=self.pipe_names)) |
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def replace_pipe(self, name, component):
"""Replace a component in the pipeline. name (unicode):
Name of the component to replace. component (callable):
Pipeli... |
if name not in self.pipe_names:
raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names))
self.pipeline[self.pipe_names.index(name)] = (name, component) |
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def rename_pipe(self, old_name, new_name):
"""Rename a pipeline component. old_name (unicode):
Name of the component to rename. new_name (unicode):
New name of... |
if old_name not in self.pipe_names:
raise ValueError(Errors.E001.format(name=old_name, opts=self.pipe_names))
if new_name in self.pipe_names:
raise ValueError(Errors.E007.format(name=new_name, opts=self.pipe_names))
i = self.pipe_names.index(old_name)
self.pipeli... |
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def remove_pipe(self, name):
"""Remove a component from the pipeline. name (unicode):
Name of the component to remove. RETURNS (tuple):
A `(name, component)` t... |
if name not in self.pipe_names:
raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names))
return self.pipeline.pop(self.pipe_names.index(name)) |
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def rehearse(self, docs, sgd=None, losses=None, config=None):
"""Make a "rehearsal" update to the models in the pipeline, to prevent forgetting. Rehearsal update... |
# TODO: document
if len(docs) == 0:
return
if sgd is None:
if self._optimizer is None:
self._optimizer = create_default_optimizer(Model.ops)
sgd = self._optimizer
docs = list(docs)
for i, doc in enumerate(docs):
if ... |
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def preprocess_gold(self, docs_golds):
"""Can be called before training to pre-process gold data. By default, it handles nonprojectivity and adds missing tags to... |
for name, proc in self.pipeline:
if hasattr(proc, "preprocess_gold"):
docs_golds = proc.preprocess_gold(docs_golds)
for doc, gold in docs_golds:
yield doc, gold |
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def begin_training(self, get_gold_tuples=None, sgd=None, component_cfg=None, **cfg):
"""Allocate models, pre-process training data and acquire a trainer and opti... |
if get_gold_tuples is None:
get_gold_tuples = lambda: []
# Populate vocab
else:
for _, annots_brackets in get_gold_tuples():
for annots, _ in annots_brackets:
for word in annots[1]:
_ = self.vocab[word] # noqa:... |
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def resume_training(self, sgd=None, **cfg):
"""Continue training a pre-trained model. Create and return an optimizer, and initialize "rehearsal" for any pipeline... |
if cfg.get("device", -1) >= 0:
util.use_gpu(cfg["device"])
if self.vocab.vectors.data.shape[1] >= 1:
self.vocab.vectors.data = Model.ops.asarray(self.vocab.vectors.data)
link_vectors_to_models(self.vocab)
if self.vocab.vectors.data.shape[1]:
c... |
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def use_params(self, params, **cfg):
"""Replace weights of models in the pipeline with those provided in the params dictionary. Can be used as a contextmanager, ... |
contexts = [
pipe.use_params(params)
for name, pipe in self.pipeline
if hasattr(pipe, "use_params")
]
# TODO: Having trouble with contextlib
# Workaround: these aren't actually context managers atm.
for context in contexts:
try:
... |
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def pipe( self, texts, as_tuples=False, n_threads=-1, batch_size=1000, disable=[], cleanup=False, component_cfg=None, ):
"""Process texts as a stream, and yield ... |
if n_threads != -1:
deprecation_warning(Warnings.W016)
if as_tuples:
text_context1, text_context2 = itertools.tee(texts)
texts = (tc[0] for tc in text_context1)
contexts = (tc[1] for tc in text_context2)
docs = self.pipe(
texts... |
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def to_disk(self, path, exclude=tuple(), disable=None):
"""Save the current state to a directory. If a model is loaded, this will include the model. path (unicod... |
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
path = util.ensure_path(path)
serializers = OrderedDict()
serializers["tokenizer"] = lambda p: self.tokenizer.to_disk(p, exclude=["vocab"])
serializers["meta.json"] = lambda p: p... |
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def from_disk(self, path, exclude=tuple(), disable=None):
"""Loads state from a directory. Modifies the object in place and returns it. If the saved `Language` o... |
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
path = util.ensure_path(path)
deserializers = OrderedDict()
deserializers["meta.json"] = lambda p: self.meta.update(srsly.read_json(p))
deserializers["vocab"] = lambda p: self.vo... |
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def to_bytes(self, exclude=tuple(), disable=None, **kwargs):
"""Serialize the current state to a binary string. exclude (list):
Names of components or serializa... |
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
serializers = OrderedDict()
serializers["vocab"] = lambda: self.vocab.to_bytes()
serializers["tokenizer"] = lambda: self.tokenizer.to_bytes(exclude=["vocab"])
serializers["meta.j... |
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def from_bytes(self, bytes_data, exclude=tuple(), disable=None, **kwargs):
"""Load state from a binary string. bytes_data (bytes):
The data to load from. exclud... |
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
deserializers = OrderedDict()
deserializers["meta.json"] = lambda b: self.meta.update(srsly.json_loads(b))
deserializers["vocab"] = lambda b: self.vocab.from_bytes(b) and _fix_pretrained... |
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def restore(self):
"""Restore the pipeline to its state when DisabledPipes was created.""" |
current, self.nlp.pipeline = self.nlp.pipeline, self.original_pipeline
unexpected = [name for name, pipe in current if not self.nlp.has_pipe(name)]
if unexpected:
# Don't change the pipeline if we're raising an error.
self.nlp.pipeline = current
raise ValueEr... |
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def get_loaded_rules(rules_paths):
"""Yields all available rules. :type rules_paths: [Path] :rtype: Iterable[Rule] """ |
for path in rules_paths:
if path.name != '__init__.py':
rule = Rule.from_path(path)
if rule.is_enabled:
yield rule |
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def get_rules_import_paths():
"""Yields all rules import paths. :rtype: Iterable[Path] """ |
# Bundled rules:
yield Path(__file__).parent.joinpath('rules')
# Rules defined by user:
yield settings.user_dir.joinpath('rules')
# Packages with third-party rules:
for path in sys.path:
for contrib_module in Path(path).glob('thefuck_contrib_*'):
contrib_rules = contrib_modu... |
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def get_rules():
"""Returns all enabled rules. :rtype: [Rule] """ |
paths = [rule_path for path in get_rules_import_paths()
for rule_path in sorted(path.glob('*.py'))]
return sorted(get_loaded_rules(paths),
key=lambda rule: rule.priority) |
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def organize_commands(corrected_commands):
"""Yields sorted commands without duplicates. :type corrected_commands: Iterable[thefuck.types.CorrectedCommand] :rtyp... |
try:
first_command = next(corrected_commands)
yield first_command
except StopIteration:
return
without_duplicates = {
command for command in sorted(
corrected_commands, key=lambda command: command.priority)
if command != first_command}
sorted_comman... |
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def get_corrected_commands(command):
"""Returns generator with sorted and unique corrected commands. :type command: thefuck.types.Command :rtype: Iterable[thefuc... |
corrected_commands = (
corrected for rule in get_rules()
if rule.is_match(command)
for corrected in rule.get_corrected_commands(command))
return organize_commands(corrected_commands) |
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def fix_command(known_args):
"""Fixes previous command. Used when `thefuck` called without arguments.""" |
settings.init(known_args)
with logs.debug_time('Total'):
logs.debug(u'Run with settings: {}'.format(pformat(settings)))
raw_command = _get_raw_command(known_args)
try:
command = types.Command.from_raw_script(raw_command)
except EmptyCommand:
logs.debug('... |
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def get_output(script):
"""Gets command output from shell logger.""" |
with logs.debug_time(u'Read output from external shell logger'):
commands = _get_last_n(const.SHELL_LOGGER_LIMIT)
for command in commands:
if command['command'] == script:
lines = _get_output_lines(command['output'])
output = '\n'.join(lines).strip()
... |
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def _get_history_lines(self):
"""Returns list of history entries.""" |
history_file_name = self._get_history_file_name()
if os.path.isfile(history_file_name):
with io.open(history_file_name, 'r',
encoding='utf-8', errors='ignore') as history_file:
lines = history_file.readlines()
if settings.history_lim... |
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def split_command(self, command):
"""Split the command using shell-like syntax.""" |
encoded = self.encode_utf8(command)
try:
splitted = [s.replace("??", "\\ ") for s in shlex.split(encoded.replace('\\ ', '??'))]
except ValueError:
splitted = encoded.split(' ')
return self.decode_utf8(splitted) |
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def quote(self, s):
"""Return a shell-escaped version of the string s.""" |
if six.PY2:
from pipes import quote
else:
from shlex import quote
return quote(s) |
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def _put_to_history(self, command_script):
"""Puts command script to shell history.""" |
history_file_name = self._get_history_file_name()
if os.path.isfile(history_file_name):
with open(history_file_name, 'a') as history:
entry = self._get_history_line(command_script)
if six.PY2:
history.write(entry.encode('utf-8'))
... |
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def _get_brew_commands(brew_path_prefix):
"""To get brew default commands on local environment""" |
brew_cmd_path = brew_path_prefix + BREW_CMD_PATH
return [name[:-3] for name in os.listdir(brew_cmd_path)
if name.endswith(('.rb', '.sh'))] |
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def git_support(fn, command):
"""Resolves git aliases and supports testing for both git and hub.""" |
# supports GitHub's `hub` command
# which is recommended to be used with `alias git=hub`
# but at this point, shell aliases have already been resolved
if not is_app(command, 'git', 'hub'):
return False
# perform git aliases expansion
if 'trace: alias expansion:' in command.output:
... |
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def read_actions():
"""Yields actions for pressed keys.""" |
while True:
key = get_key()
# Handle arrows, j/k (qwerty), and n/e (colemak)
if key in (const.KEY_UP, const.KEY_CTRL_N, 'k', 'e'):
yield const.ACTION_PREVIOUS
elif key in (const.KEY_DOWN, const.KEY_CTRL_P, 'j', 'n'):
yield const.ACTION_NEXT
elif key ... |
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def shell_logger(output):
"""Logs shell output to the `output`. Works like unix script command with `-f` flag. """ |
if not os.environ.get('SHELL'):
logs.warn("Shell logger doesn't support your platform.")
sys.exit(1)
fd = os.open(output, os.O_CREAT | os.O_TRUNC | os.O_RDWR)
os.write(fd, b'\x00' * const.LOG_SIZE_IN_BYTES)
buffer = mmap.mmap(fd, const.LOG_SIZE_IN_BYTES, mmap.MAP_SHARED, mmap.PROT_WRIT... |
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def get_output(script, expanded):
"""Get output of the script. :param script: Console script. :type script: str :param expanded: Console script with expanded ali... |
if shell_logger.is_available():
return shell_logger.get_output(script)
if settings.instant_mode:
return read_log.get_output(script)
else:
return rerun.get_output(script, expanded) |
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def _add_arguments(self):
"""Adds arguments to parser.""" |
self._parser.add_argument(
'-v', '--version',
action='store_true',
help="show program's version number and exit")
self._parser.add_argument(
'-a', '--alias',
nargs='?',
const=get_alias(),
help='[custom-alias-name] print... |
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def _add_conflicting_arguments(self):
"""It's too dangerous to use `-y` and `-r` together.""" |
group = self._parser.add_mutually_exclusive_group()
group.add_argument(
'-y', '--yes', '--yeah',
action='store_true',
help='execute fixed command without confirmation')
group.add_argument(
'-r', '--repeat',
action='store_true',
... |
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def get_scripts():
"""Get custom npm scripts.""" |
proc = Popen(['npm', 'run-script'], stdout=PIPE)
should_yeild = False
for line in proc.stdout.readlines():
line = line.decode()
if 'available via `npm run-script`:' in line:
should_yeild = True
continue
if should_yeild and re.match(r'^ [^ ]+', line):
... |
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