mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-14 05:37:03 +03:00
583 lines
23 KiB
Python
583 lines
23 KiB
Python
from typing import List, Sequence, Dict, Any, Tuple, Optional
|
|
from pathlib import Path
|
|
from collections import Counter
|
|
import sys
|
|
import srsly
|
|
from wasabi import Printer, MESSAGES, msg
|
|
import typer
|
|
|
|
from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
|
|
from ._util import import_code, debug_cli
|
|
from ..training import Example
|
|
from ..training.initialize import get_sourced_components
|
|
from ..schemas import ConfigSchemaTraining
|
|
from ..pipeline._parser_internals import nonproj
|
|
from ..language import Language
|
|
from ..util import registry, resolve_dot_names
|
|
from .. import util
|
|
|
|
|
|
# Minimum number of expected occurrences of NER label in data to train new label
|
|
NEW_LABEL_THRESHOLD = 50
|
|
# Minimum number of expected occurrences of dependency labels
|
|
DEP_LABEL_THRESHOLD = 20
|
|
# Minimum number of expected examples to train a new pipeline
|
|
BLANK_MODEL_MIN_THRESHOLD = 100
|
|
BLANK_MODEL_THRESHOLD = 2000
|
|
|
|
|
|
@debug_cli.command(
|
|
"data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
|
|
)
|
|
@app.command(
|
|
"debug-data",
|
|
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
|
|
hidden=True, # hide this from main CLI help but still allow it to work with warning
|
|
)
|
|
def debug_data_cli(
|
|
# fmt: off
|
|
ctx: typer.Context, # This is only used to read additional arguments
|
|
config_path: Path = Arg(..., help="Path to config file", exists=True),
|
|
code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
|
|
ignore_warnings: bool = Opt(False, "--ignore-warnings", "-IW", help="Ignore warnings, only show stats and errors"),
|
|
verbose: bool = Opt(False, "--verbose", "-V", help="Print additional information and explanations"),
|
|
no_format: bool = Opt(False, "--no-format", "-NF", help="Don't pretty-print the results"),
|
|
# fmt: on
|
|
):
|
|
"""
|
|
Analyze, debug and validate your training and development data. Outputs
|
|
useful stats, and can help you find problems like invalid entity annotations,
|
|
cyclic dependencies, low data labels and more.
|
|
|
|
DOCS: https://nightly.spacy.io/api/cli#debug-data
|
|
"""
|
|
if ctx.command.name == "debug-data":
|
|
msg.warn(
|
|
"The debug-data command is now available via the 'debug data' "
|
|
"subcommand (without the hyphen). You can run python -m spacy debug "
|
|
"--help for an overview of the other available debugging commands."
|
|
)
|
|
overrides = parse_config_overrides(ctx.args)
|
|
import_code(code_path)
|
|
debug_data(
|
|
config_path,
|
|
config_overrides=overrides,
|
|
ignore_warnings=ignore_warnings,
|
|
verbose=verbose,
|
|
no_format=no_format,
|
|
silent=False,
|
|
)
|
|
|
|
|
|
def debug_data(
|
|
config_path: Path,
|
|
*,
|
|
config_overrides: Dict[str, Any] = {},
|
|
ignore_warnings: bool = False,
|
|
verbose: bool = False,
|
|
no_format: bool = True,
|
|
silent: bool = True,
|
|
):
|
|
msg = Printer(
|
|
no_print=silent, pretty=not no_format, ignore_warnings=ignore_warnings
|
|
)
|
|
# Make sure all files and paths exists if they are needed
|
|
with show_validation_error(config_path):
|
|
cfg = util.load_config(config_path, overrides=config_overrides)
|
|
nlp = util.load_model_from_config(cfg)
|
|
config = nlp.config.interpolate()
|
|
T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
|
|
# Use original config here, not resolved version
|
|
sourced_components = get_sourced_components(cfg)
|
|
frozen_components = T["frozen_components"]
|
|
resume_components = [p for p in sourced_components if p not in frozen_components]
|
|
pipeline = nlp.pipe_names
|
|
factory_names = [nlp.get_pipe_meta(pipe).factory for pipe in nlp.pipe_names]
|
|
msg.divider("Data file validation")
|
|
|
|
# Create the gold corpus to be able to better analyze data
|
|
dot_names = [T["train_corpus"], T["dev_corpus"]]
|
|
train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
|
|
train_dataset = list(train_corpus(nlp))
|
|
dev_dataset = list(dev_corpus(nlp))
|
|
msg.good("Corpus is loadable")
|
|
|
|
nlp.initialize(lambda: train_dataset)
|
|
msg.good("Pipeline can be initialized with data")
|
|
|
|
# Create all gold data here to avoid iterating over the train_dataset constantly
|
|
gold_train_data = _compile_gold(train_dataset, factory_names, nlp, make_proj=True)
|
|
gold_train_unpreprocessed_data = _compile_gold(
|
|
train_dataset, factory_names, nlp, make_proj=False
|
|
)
|
|
gold_dev_data = _compile_gold(dev_dataset, factory_names, nlp, make_proj=True)
|
|
|
|
train_texts = gold_train_data["texts"]
|
|
dev_texts = gold_dev_data["texts"]
|
|
frozen_components = T["frozen_components"]
|
|
|
|
msg.divider("Training stats")
|
|
msg.text(f"Language: {nlp.lang}")
|
|
msg.text(f"Training pipeline: {', '.join(pipeline)}")
|
|
if resume_components:
|
|
msg.text(f"Components from other pipelines: {', '.join(resume_components)}")
|
|
if frozen_components:
|
|
msg.text(f"Frozen components: {', '.join(frozen_components)}")
|
|
msg.text(f"{len(train_dataset)} training docs")
|
|
msg.text(f"{len(dev_dataset)} evaluation docs")
|
|
|
|
if not len(gold_dev_data):
|
|
msg.fail("No evaluation docs")
|
|
overlap = len(train_texts.intersection(dev_texts))
|
|
if overlap:
|
|
msg.warn(f"{overlap} training examples also in evaluation data")
|
|
else:
|
|
msg.good("No overlap between training and evaluation data")
|
|
# TODO: make this feedback more fine-grained and report on updated
|
|
# components vs. blank components
|
|
if not resume_components and len(train_dataset) < BLANK_MODEL_THRESHOLD:
|
|
text = f"Low number of examples to train a new pipeline ({len(train_dataset)})"
|
|
if len(train_dataset) < BLANK_MODEL_MIN_THRESHOLD:
|
|
msg.fail(text)
|
|
else:
|
|
msg.warn(text)
|
|
msg.text(
|
|
f"It's recommended to use at least {BLANK_MODEL_THRESHOLD} examples "
|
|
f"(minimum {BLANK_MODEL_MIN_THRESHOLD})",
|
|
show=verbose,
|
|
)
|
|
|
|
msg.divider("Vocab & Vectors")
|
|
n_words = gold_train_data["n_words"]
|
|
msg.info(
|
|
f"{n_words} total word(s) in the data ({len(gold_train_data['words'])} unique)"
|
|
)
|
|
if gold_train_data["n_misaligned_words"] > 0:
|
|
n_misaligned = gold_train_data["n_misaligned_words"]
|
|
msg.warn(f"{n_misaligned} misaligned tokens in the training data")
|
|
if gold_dev_data["n_misaligned_words"] > 0:
|
|
n_misaligned = gold_dev_data["n_misaligned_words"]
|
|
msg.warn(f"{n_misaligned} misaligned tokens in the dev data")
|
|
most_common_words = gold_train_data["words"].most_common(10)
|
|
msg.text(
|
|
f"10 most common words: {_format_labels(most_common_words, counts=True)}",
|
|
show=verbose,
|
|
)
|
|
if len(nlp.vocab.vectors):
|
|
msg.info(
|
|
f"{len(nlp.vocab.vectors)} vectors ({nlp.vocab.vectors.n_keys} "
|
|
f"unique keys, {nlp.vocab.vectors_length} dimensions)"
|
|
)
|
|
n_missing_vectors = sum(gold_train_data["words_missing_vectors"].values())
|
|
msg.warn(
|
|
"{} words in training data without vectors ({:0.2f}%)".format(
|
|
n_missing_vectors, n_missing_vectors / gold_train_data["n_words"],
|
|
),
|
|
)
|
|
msg.text(
|
|
"10 most common words without vectors: {}".format(
|
|
_format_labels(
|
|
gold_train_data["words_missing_vectors"].most_common(10),
|
|
counts=True,
|
|
)
|
|
),
|
|
show=verbose,
|
|
)
|
|
else:
|
|
msg.info("No word vectors present in the package")
|
|
|
|
if "ner" in factory_names:
|
|
# Get all unique NER labels present in the data
|
|
labels = set(
|
|
label for label in gold_train_data["ner"] if label not in ("O", "-", None)
|
|
)
|
|
label_counts = gold_train_data["ner"]
|
|
model_labels = _get_labels_from_model(nlp, "ner")
|
|
new_labels = [l for l in labels if l not in model_labels]
|
|
existing_labels = [l for l in labels if l in model_labels]
|
|
has_low_data_warning = False
|
|
has_no_neg_warning = False
|
|
has_ws_ents_error = False
|
|
has_punct_ents_warning = False
|
|
|
|
msg.divider("Named Entity Recognition")
|
|
msg.info(
|
|
f"{len(new_labels)} new label(s), {len(existing_labels)} existing label(s)"
|
|
)
|
|
missing_values = label_counts["-"]
|
|
msg.text(f"{missing_values} missing value(s) (tokens with '-' label)")
|
|
for label in new_labels:
|
|
if len(label) == 0:
|
|
msg.fail("Empty label found in new labels")
|
|
if new_labels:
|
|
labels_with_counts = [
|
|
(label, count)
|
|
for label, count in label_counts.most_common()
|
|
if label != "-"
|
|
]
|
|
labels_with_counts = _format_labels(labels_with_counts, counts=True)
|
|
msg.text(f"New: {labels_with_counts}", show=verbose)
|
|
if existing_labels:
|
|
msg.text(f"Existing: {_format_labels(existing_labels)}", show=verbose)
|
|
if gold_train_data["ws_ents"]:
|
|
msg.fail(f"{gold_train_data['ws_ents']} invalid whitespace entity spans")
|
|
has_ws_ents_error = True
|
|
|
|
if gold_train_data["punct_ents"]:
|
|
msg.warn(f"{gold_train_data['punct_ents']} entity span(s) with punctuation")
|
|
has_punct_ents_warning = True
|
|
|
|
for label in new_labels:
|
|
if label_counts[label] <= NEW_LABEL_THRESHOLD:
|
|
msg.warn(
|
|
f"Low number of examples for new label '{label}' ({label_counts[label]})"
|
|
)
|
|
has_low_data_warning = True
|
|
|
|
with msg.loading("Analyzing label distribution..."):
|
|
neg_docs = _get_examples_without_label(train_dataset, label)
|
|
if neg_docs == 0:
|
|
msg.warn(f"No examples for texts WITHOUT new label '{label}'")
|
|
has_no_neg_warning = True
|
|
|
|
if not has_low_data_warning:
|
|
msg.good("Good amount of examples for all labels")
|
|
if not has_no_neg_warning:
|
|
msg.good("Examples without occurrences available for all labels")
|
|
if not has_ws_ents_error:
|
|
msg.good("No entities consisting of or starting/ending with whitespace")
|
|
if not has_punct_ents_warning:
|
|
msg.good("No entities consisting of or starting/ending with punctuation")
|
|
|
|
if has_low_data_warning:
|
|
msg.text(
|
|
f"To train a new entity type, your data should include at "
|
|
f"least {NEW_LABEL_THRESHOLD} instances of the new label",
|
|
show=verbose,
|
|
)
|
|
if has_no_neg_warning:
|
|
msg.text(
|
|
"Training data should always include examples of entities "
|
|
"in context, as well as examples without a given entity "
|
|
"type.",
|
|
show=verbose,
|
|
)
|
|
if has_ws_ents_error:
|
|
msg.text(
|
|
"As of spaCy v2.1.0, entity spans consisting of or starting/ending "
|
|
"with whitespace characters are considered invalid."
|
|
)
|
|
|
|
if has_punct_ents_warning:
|
|
msg.text(
|
|
"Entity spans consisting of or starting/ending "
|
|
"with punctuation can not be trained with a noise level > 0."
|
|
)
|
|
|
|
if "textcat" in factory_names:
|
|
msg.divider("Text Classification")
|
|
labels = [label for label in gold_train_data["cats"]]
|
|
model_labels = _get_labels_from_model(nlp, "textcat")
|
|
new_labels = [l for l in labels if l not in model_labels]
|
|
existing_labels = [l for l in labels if l in model_labels]
|
|
msg.info(
|
|
f"Text Classification: {len(new_labels)} new label(s), "
|
|
f"{len(existing_labels)} existing label(s)"
|
|
)
|
|
if new_labels:
|
|
labels_with_counts = _format_labels(
|
|
gold_train_data["cats"].most_common(), counts=True
|
|
)
|
|
msg.text(f"New: {labels_with_counts}", show=verbose)
|
|
if existing_labels:
|
|
msg.text(f"Existing: {_format_labels(existing_labels)}", show=verbose)
|
|
if set(gold_train_data["cats"]) != set(gold_dev_data["cats"]):
|
|
msg.fail(
|
|
f"The train and dev labels are not the same. "
|
|
f"Train labels: {_format_labels(gold_train_data['cats'])}. "
|
|
f"Dev labels: {_format_labels(gold_dev_data['cats'])}."
|
|
)
|
|
if gold_train_data["n_cats_multilabel"] > 0:
|
|
msg.info(
|
|
"The train data contains instances without "
|
|
"mutually-exclusive classes. Use '--textcat-multilabel' "
|
|
"when training."
|
|
)
|
|
if gold_dev_data["n_cats_multilabel"] == 0:
|
|
msg.warn(
|
|
"Potential train/dev mismatch: the train data contains "
|
|
"instances without mutually-exclusive classes while the "
|
|
"dev data does not."
|
|
)
|
|
else:
|
|
msg.info(
|
|
"The train data contains only instances with "
|
|
"mutually-exclusive classes."
|
|
)
|
|
if gold_dev_data["n_cats_multilabel"] > 0:
|
|
msg.fail(
|
|
"Train/dev mismatch: the dev data contains instances "
|
|
"without mutually-exclusive classes while the train data "
|
|
"contains only instances with mutually-exclusive classes."
|
|
)
|
|
|
|
if "tagger" in factory_names:
|
|
msg.divider("Part-of-speech Tagging")
|
|
labels = [label for label in gold_train_data["tags"]]
|
|
# TODO: does this need to be updated?
|
|
msg.info(f"{len(labels)} label(s) in data")
|
|
labels_with_counts = _format_labels(
|
|
gold_train_data["tags"].most_common(), counts=True
|
|
)
|
|
msg.text(labels_with_counts, show=verbose)
|
|
|
|
if "parser" in factory_names:
|
|
has_low_data_warning = False
|
|
msg.divider("Dependency Parsing")
|
|
|
|
# profile sentence length
|
|
msg.info(
|
|
f"Found {gold_train_data['n_sents']} sentence(s) with an average "
|
|
f"length of {gold_train_data['n_words'] / gold_train_data['n_sents']:.1f} words."
|
|
)
|
|
|
|
# check for documents with multiple sentences
|
|
sents_per_doc = gold_train_data["n_sents"] / len(gold_train_data["texts"])
|
|
if sents_per_doc < 1.1:
|
|
msg.warn(
|
|
f"The training data contains {sents_per_doc:.2f} sentences per "
|
|
f"document. When there are very few documents containing more "
|
|
f"than one sentence, the parser will not learn how to segment "
|
|
f"longer texts into sentences."
|
|
)
|
|
|
|
# profile labels
|
|
labels_train = [label for label in gold_train_data["deps"]]
|
|
labels_train_unpreprocessed = [
|
|
label for label in gold_train_unpreprocessed_data["deps"]
|
|
]
|
|
labels_dev = [label for label in gold_dev_data["deps"]]
|
|
|
|
if gold_train_unpreprocessed_data["n_nonproj"] > 0:
|
|
n_nonproj = gold_train_unpreprocessed_data["n_nonproj"]
|
|
msg.info(f"Found {n_nonproj} nonprojective train sentence(s)")
|
|
if gold_dev_data["n_nonproj"] > 0:
|
|
n_nonproj = gold_dev_data["n_nonproj"]
|
|
msg.info(f"Found {n_nonproj} nonprojective dev sentence(s)")
|
|
msg.info(f"{len(labels_train_unpreprocessed)} label(s) in train data")
|
|
msg.info(f"{len(labels_train)} label(s) in projectivized train data")
|
|
labels_with_counts = _format_labels(
|
|
gold_train_unpreprocessed_data["deps"].most_common(), counts=True
|
|
)
|
|
msg.text(labels_with_counts, show=verbose)
|
|
|
|
# rare labels in train
|
|
for label in gold_train_unpreprocessed_data["deps"]:
|
|
if gold_train_unpreprocessed_data["deps"][label] <= DEP_LABEL_THRESHOLD:
|
|
msg.warn(
|
|
f"Low number of examples for label '{label}' "
|
|
f"({gold_train_unpreprocessed_data['deps'][label]})"
|
|
)
|
|
has_low_data_warning = True
|
|
|
|
# rare labels in projectivized train
|
|
rare_projectivized_labels = []
|
|
for label in gold_train_data["deps"]:
|
|
if gold_train_data["deps"][label] <= DEP_LABEL_THRESHOLD and "||" in label:
|
|
rare_projectivized_labels.append(
|
|
f"{label}: {gold_train_data['deps'][label]}"
|
|
)
|
|
|
|
if len(rare_projectivized_labels) > 0:
|
|
msg.warn(
|
|
f"Low number of examples for {len(rare_projectivized_labels)} "
|
|
"label(s) in the projectivized dependency trees used for "
|
|
"training. You may want to projectivize labels such as punct "
|
|
"before training in order to improve parser performance."
|
|
)
|
|
msg.warn(
|
|
f"Projectivized labels with low numbers of examples: ",
|
|
", ".join(rare_projectivized_labels),
|
|
show=verbose,
|
|
)
|
|
has_low_data_warning = True
|
|
|
|
# labels only in train
|
|
if set(labels_train) - set(labels_dev):
|
|
msg.warn(
|
|
"The following labels were found only in the train data:",
|
|
", ".join(set(labels_train) - set(labels_dev)),
|
|
show=verbose,
|
|
)
|
|
|
|
# labels only in dev
|
|
if set(labels_dev) - set(labels_train):
|
|
msg.warn(
|
|
"The following labels were found only in the dev data:",
|
|
", ".join(set(labels_dev) - set(labels_train)),
|
|
show=verbose,
|
|
)
|
|
|
|
if has_low_data_warning:
|
|
msg.text(
|
|
f"To train a parser, your data should include at "
|
|
f"least {DEP_LABEL_THRESHOLD} instances of each label.",
|
|
show=verbose,
|
|
)
|
|
|
|
# multiple root labels
|
|
if len(gold_train_unpreprocessed_data["roots"]) > 1:
|
|
msg.warn(
|
|
f"Multiple root labels "
|
|
f"({', '.join(gold_train_unpreprocessed_data['roots'])}) "
|
|
f"found in training data. spaCy's parser uses a single root "
|
|
f"label ROOT so this distinction will not be available."
|
|
)
|
|
|
|
# these should not happen, but just in case
|
|
if gold_train_data["n_nonproj"] > 0:
|
|
msg.fail(
|
|
f"Found {gold_train_data['n_nonproj']} nonprojective "
|
|
f"projectivized train sentence(s)"
|
|
)
|
|
if gold_train_data["n_cycles"] > 0:
|
|
msg.fail(
|
|
f"Found {gold_train_data['n_cycles']} projectivized train sentence(s) with cycles"
|
|
)
|
|
|
|
msg.divider("Summary")
|
|
good_counts = msg.counts[MESSAGES.GOOD]
|
|
warn_counts = msg.counts[MESSAGES.WARN]
|
|
fail_counts = msg.counts[MESSAGES.FAIL]
|
|
if good_counts:
|
|
msg.good(f"{good_counts} {'check' if good_counts == 1 else 'checks'} passed")
|
|
if warn_counts:
|
|
msg.warn(f"{warn_counts} {'warning' if warn_counts == 1 else 'warnings'}")
|
|
if fail_counts:
|
|
msg.fail(f"{fail_counts} {'error' if fail_counts == 1 else 'errors'}")
|
|
sys.exit(1)
|
|
|
|
|
|
def _load_file(file_path: Path, msg: Printer) -> None:
|
|
file_name = file_path.parts[-1]
|
|
if file_path.suffix == ".json":
|
|
with msg.loading(f"Loading {file_name}..."):
|
|
data = srsly.read_json(file_path)
|
|
msg.good(f"Loaded {file_name}")
|
|
return data
|
|
elif file_path.suffix == ".jsonl":
|
|
with msg.loading(f"Loading {file_name}..."):
|
|
data = srsly.read_jsonl(file_path)
|
|
msg.good(f"Loaded {file_name}")
|
|
return data
|
|
msg.fail(
|
|
f"Can't load file extension {file_path.suffix}",
|
|
"Expected .json or .jsonl",
|
|
exits=1,
|
|
)
|
|
|
|
|
|
def _compile_gold(
|
|
examples: Sequence[Example],
|
|
factory_names: List[str],
|
|
nlp: Language,
|
|
make_proj: bool,
|
|
) -> Dict[str, Any]:
|
|
data = {
|
|
"ner": Counter(),
|
|
"cats": Counter(),
|
|
"tags": Counter(),
|
|
"deps": Counter(),
|
|
"words": Counter(),
|
|
"roots": Counter(),
|
|
"ws_ents": 0,
|
|
"punct_ents": 0,
|
|
"n_words": 0,
|
|
"n_misaligned_words": 0,
|
|
"words_missing_vectors": Counter(),
|
|
"n_sents": 0,
|
|
"n_nonproj": 0,
|
|
"n_cycles": 0,
|
|
"n_cats_multilabel": 0,
|
|
"texts": set(),
|
|
}
|
|
for eg in examples:
|
|
gold = eg.reference
|
|
doc = eg.predicted
|
|
valid_words = [x for x in gold if x is not None]
|
|
data["words"].update(valid_words)
|
|
data["n_words"] += len(valid_words)
|
|
data["n_misaligned_words"] += len(gold) - len(valid_words)
|
|
data["texts"].add(doc.text)
|
|
if len(nlp.vocab.vectors):
|
|
for word in valid_words:
|
|
if nlp.vocab.strings[word] not in nlp.vocab.vectors:
|
|
data["words_missing_vectors"].update([word])
|
|
if "ner" in factory_names:
|
|
for i, label in enumerate(eg.get_aligned_ner()):
|
|
if label is None:
|
|
continue
|
|
if label.startswith(("B-", "U-", "L-")) and doc[i].is_space:
|
|
# "Illegal" whitespace entity
|
|
data["ws_ents"] += 1
|
|
if label.startswith(("B-", "U-", "L-")) and doc[i].text in [
|
|
".",
|
|
"'",
|
|
"!",
|
|
"?",
|
|
",",
|
|
]:
|
|
# punctuation entity: could be replaced by whitespace when training with noise,
|
|
# so add a warning to alert the user to this unexpected side effect.
|
|
data["punct_ents"] += 1
|
|
if label.startswith(("B-", "U-")):
|
|
combined_label = label.split("-")[1]
|
|
data["ner"][combined_label] += 1
|
|
elif label == "-":
|
|
data["ner"]["-"] += 1
|
|
if "textcat" in factory_names:
|
|
data["cats"].update(gold.cats)
|
|
if list(gold.cats.values()).count(1.0) != 1:
|
|
data["n_cats_multilabel"] += 1
|
|
if "tagger" in factory_names:
|
|
tags = eg.get_aligned("TAG", as_string=True)
|
|
data["tags"].update([x for x in tags if x is not None])
|
|
if "parser" in factory_names:
|
|
aligned_heads, aligned_deps = eg.get_aligned_parse(projectivize=make_proj)
|
|
data["deps"].update([x for x in aligned_deps if x is not None])
|
|
for i, (dep, head) in enumerate(zip(aligned_deps, aligned_heads)):
|
|
if head == i:
|
|
data["roots"].update([dep])
|
|
data["n_sents"] += 1
|
|
if nonproj.is_nonproj_tree(aligned_heads):
|
|
data["n_nonproj"] += 1
|
|
if nonproj.contains_cycle(aligned_heads):
|
|
data["n_cycles"] += 1
|
|
return data
|
|
|
|
|
|
def _format_labels(labels: List[Tuple[str, int]], counts: bool = False) -> str:
|
|
if counts:
|
|
return ", ".join([f"'{l}' ({c})" for l, c in labels])
|
|
return ", ".join([f"'{l}'" for l in labels])
|
|
|
|
|
|
def _get_examples_without_label(data: Sequence[Example], label: str) -> int:
|
|
count = 0
|
|
for eg in data:
|
|
labels = [
|
|
label.split("-")[1]
|
|
for label in eg.get_aligned_ner()
|
|
if label not in ("O", "-", None)
|
|
]
|
|
if label not in labels:
|
|
count += 1
|
|
return count
|
|
|
|
|
|
def _get_labels_from_model(nlp: Language, pipe_name: str) -> Sequence[str]:
|
|
if pipe_name not in nlp.pipe_names:
|
|
return set()
|
|
pipe = nlp.get_pipe(pipe_name)
|
|
return pipe.labels
|