mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
bede11b67c
This patch does a few smallish things that tighten up the training workflow a little, and allow memory use during training to be reduced by letting the GoldCorpus stream data properly. Previously, the parser and entity recognizer read and saved labels as lists, with extra labels noted separately. Lists were used becaue ordering is very important, to ensure that the label-to-class mapping is stable. We now manage labels as nested dictionaries, first keyed by the action, and then keyed by the label. Values are frequencies. The trick is, how do we save new labels? We need to make sure we iterate over these in the same order they're added. Otherwise, we'll get different class IDs, and the model's predictions won't make sense. To allow stable sorting, we map the new labels to negative values. If we have two new labels, they'll be noted as having "frequency" -1 and -2. The next new label will then have "frequency" -3. When we sort by (frequency, label), we then get a stable sort. Storing frequencies then allows us to make the next nice improvement. Previously we had to iterate over the whole training set, to pre-process it for the deprojectivisation. This led to storing the whole training set in memory. This was most of the required memory during training. To prevent this, we now store the frequencies as we stream in the data, and deprojectivize as we go. Once we've built the frequencies, we can then apply a frequency cut-off when we decide how many classes to make. Finally, to allow proper data streaming, we also have to have some way of shuffling the iterator. This is awkward if the training files have multiple documents in them. To solve this, the GoldCorpus class now writes the training data to disk in msgpack files, one per document. We can then shuffle the data by shuffling the paths. This is a squash merge, as I made a lot of very small commits. Individual commit messages below. * Simplify label management for TransitionSystem and its subclasses * Fix serialization for new label handling format in parser * Simplify and improve GoldCorpus class. Reduce memory use, write to temp dir * Set actions in transition system * Require thinc 6.11.1.dev4 * Fix error in parser init * Add unicode declaration * Fix unicode declaration * Update textcat test * Try to get model training on less memory * Print json loc for now * Try rapidjson to reduce memory use * Remove rapidjson requirement * Try rapidjson for reduced mem usage * Handle None heads when projectivising * Stream json docs * Fix train script * Handle projectivity in GoldParse * Fix projectivity handling * Add minibatch_by_words util from ud_train * Minibatch by number of words in spacy.cli.train * Move minibatch_by_words util to spacy.util * Fix label handling * More hacking at label management in parser * Fix encoding in msgpack serialization in GoldParse * Adjust batch sizes in parser training * Fix minibatch_by_words * Add merge_subtokens function to pipeline.pyx * Register merge_subtokens factory * Restore use of msgpack tmp directory * Use minibatch-by-words in train * Handle retokenization in scorer * Change back-off approach for missing labels. Use 'dep' label * Update NER for new label management * Set NER tags for over-segmented words * Fix label alignment in gold * Fix label back-off for infrequent labels * Fix int type in labels dict key * Fix int type in labels dict key * Update feature definition for 8 feature set * Update ud-train script for new label stuff * Fix json streamer * Print the line number if conll eval fails * Update children and sentence boundaries after deprojectivisation * Export set_children_from_heads from doc.pxd * Render parses during UD training * Remove print statement * Require thinc 6.11.1.dev6. Try adding wheel as install_requires * Set different dev version, to flush pip cache * Update thinc version * Update GoldCorpus docs * Remove print statements * Fix formatting and links [ci skip]
625 lines
23 KiB
Cython
625 lines
23 KiB
Cython
# cython: profile=True
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# coding: utf8
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from __future__ import unicode_literals, print_function
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import re
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import random
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import cytoolz
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import itertools
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import numpy
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import tempfile
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import shutil
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from pathlib import Path
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import msgpack
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import ujson
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from . import _align
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from .syntax import nonproj
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from .tokens import Doc
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from . import util
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from .util import minibatch, itershuffle
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from .compat import json_dumps
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from libc.stdio cimport FILE, fopen, fclose, fread, fwrite, feof, fseek
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def tags_to_entities(tags):
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entities = []
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start = None
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for i, tag in enumerate(tags):
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if tag is None:
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continue
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if tag.startswith('O'):
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# TODO: We shouldn't be getting these malformed inputs. Fix this.
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if start is not None:
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start = None
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continue
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elif tag == '-':
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continue
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elif tag.startswith('I'):
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assert start is not None, tags[:i]
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continue
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if tag.startswith('U'):
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entities.append((tag[2:], i, i))
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elif tag.startswith('B'):
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start = i
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elif tag.startswith('L'):
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entities.append((tag[2:], start, i))
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start = None
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else:
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raise Exception(tag)
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return entities
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def merge_sents(sents):
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m_deps = [[], [], [], [], [], []]
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m_brackets = []
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i = 0
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for (ids, words, tags, heads, labels, ner), brackets in sents:
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m_deps[0].extend(id_ + i for id_ in ids)
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m_deps[1].extend(words)
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m_deps[2].extend(tags)
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m_deps[3].extend(head + i for head in heads)
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m_deps[4].extend(labels)
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m_deps[5].extend(ner)
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m_brackets.extend((b['first'] + i, b['last'] + i, b['label'])
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for b in brackets)
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i += len(ids)
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return [(m_deps, m_brackets)]
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punct_re = re.compile(r'\W')
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def align(cand_words, gold_words):
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if cand_words == gold_words:
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alignment = numpy.arange(len(cand_words))
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return 0, alignment, alignment, {}, {}
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cand_words = [w.replace(' ', '') for w in cand_words]
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gold_words = [w.replace(' ', '') for w in gold_words]
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cost, i2j, j2i, matrix = _align.align(cand_words, gold_words)
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i2j_multi, j2i_multi = _align.multi_align(i2j, j2i, [len(w) for w in cand_words],
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[len(w) for w in gold_words])
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for i, j in list(i2j_multi.items()):
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if i2j_multi.get(i+1) != j and i2j_multi.get(i-1) != j:
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i2j[i] = j
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i2j_multi.pop(i)
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for j, i in list(j2i_multi.items()):
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if j2i_multi.get(j+1) != i and j2i_multi.get(j-1) != i:
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j2i[j] = i
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j2i_multi.pop(j)
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return cost, i2j, j2i, i2j_multi, j2i_multi
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class GoldCorpus(object):
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"""An annotated corpus, using the JSON file format. Manages
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annotations for tagging, dependency parsing and NER."""
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def __init__(self, train, dev, gold_preproc=False, limit=None):
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"""Create a GoldCorpus.
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train_path (unicode or Path): File or directory of training data.
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dev_path (unicode or Path): File or directory of development data.
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RETURNS (GoldCorpus): The newly created object.
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"""
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self.limit = limit
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if isinstance(train, str) or isinstance(train, Path):
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train = self.read_tuples(self.walk_corpus(train))
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dev = self.read_tuples(self.walk_corpus(dev))
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# Write temp directory with one doc per file, so we can shuffle
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# and stream
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self.tmp_dir = Path(tempfile.mkdtemp())
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self.write_msgpack(self.tmp_dir / 'train', train)
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self.write_msgpack(self.tmp_dir / 'dev', dev)
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def __del__(self):
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shutil.rmtree(self.tmp_dir)
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@staticmethod
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def write_msgpack(directory, doc_tuples):
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if not directory.exists():
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directory.mkdir()
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for i, doc_tuple in enumerate(doc_tuples):
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with open(directory / '{}.msg'.format(i), 'wb') as file_:
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msgpack.dump([doc_tuple], file_, use_bin_type=True, encoding='utf8')
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@staticmethod
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def walk_corpus(path):
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path = util.ensure_path(path)
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if not path.is_dir():
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return [path]
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paths = [path]
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locs = []
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seen = set()
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for path in paths:
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if str(path) in seen:
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continue
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seen.add(str(path))
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if path.parts[-1].startswith('.'):
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continue
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elif path.is_dir():
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paths.extend(path.iterdir())
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elif path.parts[-1].endswith('.json'):
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locs.append(path)
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return locs
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@staticmethod
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def read_tuples(locs, limit=0):
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i = 0
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for loc in locs:
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loc = util.ensure_path(loc)
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if loc.parts[-1].endswith('json'):
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gold_tuples = read_json_file(loc)
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elif loc.parts[-1].endswith('msg'):
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with loc.open('rb') as file_:
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gold_tuples = msgpack.load(file_, encoding='utf8')
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else:
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msg = "Cannot read from file: %s. Supported formats: .json, .msg"
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raise ValueError(msg % loc)
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for item in gold_tuples:
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yield item
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i += len(item[1])
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if limit and i >= limit:
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break
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@property
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def dev_tuples(self):
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locs = (self.tmp_dir / 'dev').iterdir()
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yield from self.read_tuples(locs, limit=self.limit)
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@property
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def train_tuples(self):
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locs = (self.tmp_dir / 'train').iterdir()
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yield from self.read_tuples(locs, limit=self.limit)
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def count_train(self):
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n = 0
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i = 0
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for raw_text, paragraph_tuples in self.train_tuples:
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for sent_tuples, brackets in paragraph_tuples:
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n += len(sent_tuples[1])
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if self.limit and i >= self.limit:
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break
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i += len(paragraph_tuples)
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return n
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def train_docs(self, nlp, gold_preproc=False, max_length=None,
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noise_level=0.0):
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locs = list((self.tmp_dir / 'train').iterdir())
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random.shuffle(locs)
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train_tuples = self.read_tuples(locs, limit=self.limit)
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gold_docs = self.iter_gold_docs(nlp, train_tuples, gold_preproc,
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max_length=max_length,
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noise_level=noise_level,
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make_projective=True)
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yield from gold_docs
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def dev_docs(self, nlp, gold_preproc=False):
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gold_docs = self.iter_gold_docs(nlp, self.dev_tuples,
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gold_preproc=gold_preproc)
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yield from gold_docs
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@classmethod
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def iter_gold_docs(cls, nlp, tuples, gold_preproc, max_length=None,
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noise_level=0.0, make_projective=False):
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for raw_text, paragraph_tuples in tuples:
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if gold_preproc:
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raw_text = None
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else:
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paragraph_tuples = merge_sents(paragraph_tuples)
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docs = cls._make_docs(nlp, raw_text, paragraph_tuples,
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gold_preproc, noise_level=noise_level)
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golds = cls._make_golds(docs, paragraph_tuples, make_projective)
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for doc, gold in zip(docs, golds):
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if (not max_length) or len(doc) < max_length:
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yield doc, gold
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@classmethod
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def _make_docs(cls, nlp, raw_text, paragraph_tuples, gold_preproc,
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noise_level=0.0):
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if raw_text is not None:
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raw_text = add_noise(raw_text, noise_level)
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return [nlp.make_doc(raw_text)]
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else:
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return [Doc(nlp.vocab,
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words=add_noise(sent_tuples[1], noise_level))
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for (sent_tuples, brackets) in paragraph_tuples]
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@classmethod
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def _make_golds(cls, docs, paragraph_tuples, make_projective):
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assert len(docs) == len(paragraph_tuples)
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if len(docs) == 1:
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return [GoldParse.from_annot_tuples(docs[0],
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paragraph_tuples[0][0],
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make_projective=make_projective)]
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else:
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return [GoldParse.from_annot_tuples(doc, sent_tuples,
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make_projective=make_projective)
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for doc, (sent_tuples, brackets)
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in zip(docs, paragraph_tuples)]
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def add_noise(orig, noise_level):
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if random.random() >= noise_level:
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return orig
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elif type(orig) == list:
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corrupted = [_corrupt(word, noise_level) for word in orig]
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corrupted = [w for w in corrupted if w]
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return corrupted
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else:
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return ''.join(_corrupt(c, noise_level) for c in orig)
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def _corrupt(c, noise_level):
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if random.random() >= noise_level:
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return c
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elif c == ' ':
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return '\n'
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elif c == '\n':
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return ' '
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elif c in ['.', "'", "!", "?"]:
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return ''
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else:
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return c.lower()
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def read_json_file(loc, docs_filter=None, limit=None):
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loc = util.ensure_path(loc)
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if loc.is_dir():
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for filename in loc.iterdir():
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yield from read_json_file(loc / filename, limit=limit)
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else:
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for doc in _json_iterate(loc):
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if docs_filter is not None and not docs_filter(doc):
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continue
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paragraphs = []
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for paragraph in doc['paragraphs']:
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sents = []
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for sent in paragraph['sentences']:
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words = []
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ids = []
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tags = []
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heads = []
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labels = []
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ner = []
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for i, token in enumerate(sent['tokens']):
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words.append(token['orth'])
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ids.append(i)
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tags.append(token.get('tag', '-'))
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heads.append(token.get('head', 0) + i)
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labels.append(token.get('dep', ''))
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# Ensure ROOT label is case-insensitive
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if labels[-1].lower() == 'root':
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labels[-1] = 'ROOT'
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ner.append(token.get('ner', '-'))
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sents.append([
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[ids, words, tags, heads, labels, ner],
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sent.get('brackets', [])])
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if sents:
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yield [paragraph.get('raw', None), sents]
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def _json_iterate(loc):
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# We should've made these files jsonl...But since we didn't, parse out
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# the docs one-by-one to reduce memory usage.
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# It's okay to read in the whole file -- just don't parse it into JSON.
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cdef bytes py_raw
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loc = util.ensure_path(loc)
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with loc.open('rb') as file_:
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py_raw = file_.read()
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raw = <char*>py_raw
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cdef int square_depth = 0
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cdef int curly_depth = 0
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cdef int inside_string = 0
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cdef int escape = 0
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cdef int start = -1
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cdef char c
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cdef char quote = ord('"')
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cdef char backslash = ord('\\')
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cdef char open_square = ord('[')
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cdef char close_square = ord(']')
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cdef char open_curly = ord('{')
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cdef char close_curly = ord('}')
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for i in range(len(py_raw)):
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c = raw[i]
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if c == backslash:
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escape = True
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continue
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if escape:
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escape = False
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continue
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if c == quote:
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inside_string = not inside_string
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continue
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if inside_string:
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continue
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if c == open_square:
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square_depth += 1
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elif c == close_square:
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square_depth -= 1
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elif c == open_curly:
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if square_depth == 1 and curly_depth == 0:
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start = i
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curly_depth += 1
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elif c == close_curly:
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curly_depth -= 1
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if square_depth == 1 and curly_depth == 0:
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py_str = py_raw[start : i+1].decode('utf8')
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yield ujson.loads(py_str)
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start = -1
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def iob_to_biluo(tags):
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out = []
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curr_label = None
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tags = list(tags)
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while tags:
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out.extend(_consume_os(tags))
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out.extend(_consume_ent(tags))
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return out
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def _consume_os(tags):
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while tags and tags[0] == 'O':
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yield tags.pop(0)
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def _consume_ent(tags):
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if not tags:
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return []
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target = tags.pop(0).replace('B', 'I')
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length = 1
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while tags and tags[0] == target:
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length += 1
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tags.pop(0)
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label = target[2:]
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if length == 1:
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return ['U-' + label]
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else:
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start = 'B-' + label
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end = 'L-' + label
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middle = ['I-%s' % label for _ in range(1, length - 1)]
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return [start] + middle + [end]
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cdef class GoldParse:
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"""Collection for training annotations."""
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@classmethod
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def from_annot_tuples(cls, doc, annot_tuples, make_projective=False):
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_, words, tags, heads, deps, entities = annot_tuples
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return cls(doc, words=words, tags=tags, heads=heads, deps=deps,
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entities=entities, make_projective=make_projective)
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def __init__(self, doc, annot_tuples=None, words=None, tags=None,
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heads=None, deps=None, entities=None, make_projective=False,
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cats=None):
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"""Create a GoldParse.
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doc (Doc): The document the annotations refer to.
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words (iterable): A sequence of unicode word strings.
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tags (iterable): A sequence of strings, representing tag annotations.
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heads (iterable): A sequence of integers, representing syntactic
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head offsets.
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deps (iterable): A sequence of strings, representing the syntactic
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relation types.
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entities (iterable): A sequence of named entity annotations, either as
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BILUO tag strings, or as `(start_char, end_char, label)` tuples,
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representing the entity positions.
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cats (dict): Labels for text classification. Each key in the dictionary
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may be a string or an int, or a `(start_char, end_char, label)`
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tuple, indicating that the label is applied to only part of the
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document (usually a sentence). Unlike entity annotations, label
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annotations can overlap, i.e. a single word can be covered by
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multiple labelled spans. The TextCategorizer component expects
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true examples of a label to have the value 1.0, and negative
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examples of a label to have the value 0.0. Labels not in the
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dictionary are treated as missing - the gradient for those labels
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will be zero.
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RETURNS (GoldParse): The newly constructed object.
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"""
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if words is None:
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words = [token.text for token in doc]
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if tags is None:
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tags = [None for _ in doc]
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if heads is None:
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heads = [None for token in doc]
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if deps is None:
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deps = [None for _ in doc]
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if entities is None:
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entities = [None for _ in doc]
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elif len(entities) == 0:
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entities = ['O' for _ in doc]
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elif not isinstance(entities[0], basestring):
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# Assume we have entities specified by character offset.
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entities = biluo_tags_from_offsets(doc, entities)
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self.mem = Pool()
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self.loss = 0
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self.length = len(doc)
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# These are filled by the tagger/parser/entity recogniser
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self.c.tags = <int*>self.mem.alloc(len(doc), sizeof(int))
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self.c.heads = <int*>self.mem.alloc(len(doc), sizeof(int))
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self.c.labels = <attr_t*>self.mem.alloc(len(doc), sizeof(attr_t))
|
|
self.c.has_dep = <int*>self.mem.alloc(len(doc), sizeof(int))
|
|
self.c.sent_start = <int*>self.mem.alloc(len(doc), sizeof(int))
|
|
self.c.ner = <Transition*>self.mem.alloc(len(doc), sizeof(Transition))
|
|
|
|
self.cats = {} if cats is None else dict(cats)
|
|
self.words = [None] * len(doc)
|
|
self.tags = [None] * len(doc)
|
|
self.heads = [None] * len(doc)
|
|
self.labels = [None] * len(doc)
|
|
self.ner = [None] * len(doc)
|
|
|
|
# This needs to be done before we align the words
|
|
if make_projective and heads is not None and deps is not None:
|
|
heads, deps = nonproj.projectivize(heads, deps)
|
|
|
|
# Do many-to-one alignment for misaligned tokens.
|
|
# If we over-segment, we'll have one gold word that covers a sequence
|
|
# of predicted words
|
|
# If we under-segment, we'll have one predicted word that covers a
|
|
# sequence of gold words.
|
|
# If we "mis-segment", we'll have a sequence of predicted words covering
|
|
# a sequence of gold words. That's many-to-many -- we don't do that.
|
|
cost, i2j, j2i, i2j_multi, j2i_multi = align([t.orth_ for t in doc], words)
|
|
|
|
self.cand_to_gold = [(j if j >= 0 else None) for j in i2j]
|
|
self.gold_to_cand = [(i if i >= 0 else None) for i in j2i]
|
|
|
|
annot_tuples = (range(len(words)), words, tags, heads, deps, entities)
|
|
self.orig_annot = list(zip(*annot_tuples))
|
|
|
|
for i, gold_i in enumerate(self.cand_to_gold):
|
|
if doc[i].text.isspace():
|
|
self.words[i] = doc[i].text
|
|
self.tags[i] = '_SP'
|
|
self.heads[i] = None
|
|
self.labels[i] = None
|
|
self.ner[i] = 'O'
|
|
if gold_i is None:
|
|
if i in i2j_multi:
|
|
self.words[i] = words[i2j_multi[i]]
|
|
self.tags[i] = tags[i2j_multi[i]]
|
|
is_last = i2j_multi[i] != i2j_multi.get(i+1)
|
|
is_first = i2j_multi[i] != i2j_multi.get(i-1)
|
|
# Set next word in multi-token span as head, until last
|
|
if not is_last:
|
|
self.heads[i] = i+1
|
|
self.labels[i] = 'subtok'
|
|
else:
|
|
self.heads[i] = self.gold_to_cand[heads[i2j_multi[i]]]
|
|
self.labels[i] = deps[i2j_multi[i]]
|
|
# Now set NER...This is annoying because if we've split
|
|
# got an entity word split into two, we need to adjust the
|
|
# BILOU tags. We can't have BB or LL etc.
|
|
# Case 1: O -- easy.
|
|
ner_tag = entities[i2j_multi[i]]
|
|
if ner_tag == 'O':
|
|
self.ner[i] = 'O'
|
|
# Case 2: U. This has to become a B I* L sequence.
|
|
elif ner_tag.startswith('U-'):
|
|
if is_first:
|
|
self.ner[i] = ner_tag.replace('U-', 'B-', 1)
|
|
elif is_last:
|
|
self.ner[i] = ner_tag.replace('U-', 'L-', 1)
|
|
else:
|
|
self.ner[i] = ner_tag.replace('U-', 'I-', 1)
|
|
# Case 3: L. If not last, change to I.
|
|
elif ner_tag.startswith('L-'):
|
|
if is_last:
|
|
self.ner[i] = ner_tag
|
|
else:
|
|
self.ner[i] = ner_tag.replace('L-', 'I-', 1)
|
|
# Case 4: I. Stays correct
|
|
elif ner_tag.startswith('I-'):
|
|
self.ner[i] = ner_tag
|
|
else:
|
|
self.words[i] = words[gold_i]
|
|
self.tags[i] = tags[gold_i]
|
|
if heads[gold_i] is None:
|
|
self.heads[i] = None
|
|
else:
|
|
self.heads[i] = self.gold_to_cand[heads[gold_i]]
|
|
self.labels[i] = deps[gold_i]
|
|
self.ner[i] = entities[gold_i]
|
|
|
|
cycle = nonproj.contains_cycle(self.heads)
|
|
if cycle is not None:
|
|
raise Exception("Cycle found: %s" % cycle)
|
|
|
|
def __len__(self):
|
|
"""Get the number of gold-standard tokens.
|
|
|
|
RETURNS (int): The number of gold-standard tokens.
|
|
"""
|
|
return self.length
|
|
|
|
@property
|
|
def is_projective(self):
|
|
"""Whether the provided syntactic annotations form a projective
|
|
dependency tree.
|
|
"""
|
|
return not nonproj.is_nonproj_tree(self.heads)
|
|
|
|
@property
|
|
def sent_starts(self):
|
|
return [self.c.sent_start[i] for i in range(self.length)]
|
|
|
|
|
|
def biluo_tags_from_offsets(doc, entities, missing='O'):
|
|
"""Encode labelled spans into per-token tags, using the
|
|
Begin/In/Last/Unit/Out scheme (BILUO).
|
|
|
|
doc (Doc): The document that the entity offsets refer to. The output tags
|
|
will refer to the token boundaries within the document.
|
|
entities (iterable): A sequence of `(start, end, label)` triples. `start`
|
|
and `end` should be character-offset integers denoting the slice into
|
|
the original string.
|
|
RETURNS (list): A list of unicode strings, describing the tags. Each tag
|
|
string will be of the form either "", "O" or "{action}-{label}", where
|
|
action is one of "B", "I", "L", "U". The string "-" is used where the
|
|
entity offsets don't align with the tokenization in the `Doc` object.
|
|
The training algorithm will view these as missing values. "O" denotes a
|
|
non-entity token. "B" denotes the beginning of a multi-token entity,
|
|
"I" the inside of an entity of three or more tokens, and "L" the end
|
|
of an entity of two or more tokens. "U" denotes a single-token entity.
|
|
|
|
EXAMPLE:
|
|
>>> text = 'I like London.'
|
|
>>> entities = [(len('I like '), len('I like London'), 'LOC')]
|
|
>>> doc = nlp.tokenizer(text)
|
|
>>> tags = biluo_tags_from_offsets(doc, entities)
|
|
>>> assert tags == ['O', 'O', 'U-LOC', 'O']
|
|
"""
|
|
starts = {token.idx: token.i for token in doc}
|
|
ends = {token.idx+len(token): token.i for token in doc}
|
|
biluo = ['-' for _ in doc]
|
|
# Handle entity cases
|
|
for start_char, end_char, label in entities:
|
|
start_token = starts.get(start_char)
|
|
end_token = ends.get(end_char)
|
|
# Only interested if the tokenization is correct
|
|
if start_token is not None and end_token is not None:
|
|
if start_token == end_token:
|
|
biluo[start_token] = 'U-%s' % label
|
|
else:
|
|
biluo[start_token] = 'B-%s' % label
|
|
for i in range(start_token+1, end_token):
|
|
biluo[i] = 'I-%s' % label
|
|
biluo[end_token] = 'L-%s' % label
|
|
# Now distinguish the O cases from ones where we miss the tokenization
|
|
entity_chars = set()
|
|
for start_char, end_char, label in entities:
|
|
for i in range(start_char, end_char):
|
|
entity_chars.add(i)
|
|
for token in doc:
|
|
for i in range(token.idx, token.idx+len(token)):
|
|
if i in entity_chars:
|
|
break
|
|
else:
|
|
biluo[token.i] = missing
|
|
return biluo
|
|
|
|
|
|
def offsets_from_biluo_tags(doc, tags):
|
|
"""Encode per-token tags following the BILUO scheme into entity offsets.
|
|
|
|
doc (Doc): The document that the BILUO tags refer to.
|
|
entities (iterable): A sequence of BILUO tags with each tag describing one
|
|
token. Each tags string will be of the form of either "", "O" or
|
|
"{action}-{label}", where action is one of "B", "I", "L", "U".
|
|
RETURNS (list): A sequence of `(start, end, label)` triples. `start` and
|
|
`end` will be character-offset integers denoting the slice into the
|
|
original string.
|
|
"""
|
|
token_offsets = tags_to_entities(tags)
|
|
offsets = []
|
|
for label, start_idx, end_idx in token_offsets:
|
|
span = doc[start_idx : end_idx + 1]
|
|
offsets.append((span.start_char, span.end_char, label))
|
|
return offsets
|
|
|
|
|
|
def is_punct_label(label):
|
|
return label == 'P' or label.lower() == 'punct'
|