mirror of
https://github.com/explosion/spaCy.git
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597 lines
24 KiB
Python
597 lines
24 KiB
Python
from typing import Optional, List, Sequence, Dict, Any, Tuple
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from pathlib import Path
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from collections import Counter
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import sys
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import srsly
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from wasabi import Printer, MESSAGES
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from ._app import app, Arg, Opt
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from ..gold import GoldCorpus, Example
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from ..syntax import nonproj
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from ..language import Language
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from ..util import load_model, get_lang_class
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# Minimum number of expected occurrences of NER label in data to train new label
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NEW_LABEL_THRESHOLD = 50
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# Minimum number of expected occurrences of dependency labels
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DEP_LABEL_THRESHOLD = 20
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# Minimum number of expected examples to train a blank model
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BLANK_MODEL_MIN_THRESHOLD = 100
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BLANK_MODEL_THRESHOLD = 2000
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@app.command("debug-data")
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def debug_data_cli(
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# fmt: off
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lang: str = Arg(..., help="Model language"),
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train_path: Path = Arg(..., help="Location of JSON-formatted training data", exists=True),
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dev_path: Path = Arg(..., help="Location of JSON-formatted development data", exists=True),
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tag_map_path: Optional[Path] = Opt(None, "--tag-map-path", "-tm", help="Location of JSON-formatted tag map", exists=True, dir_okay=False),
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base_model: Optional[str] = Opt(None, "--base-model", "-b", help="Name of model to update (optional)"),
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pipeline: str = Opt("tagger,parser,ner", "--pipeline", "-p", help="Comma-separated names of pipeline components to train"),
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ignore_warnings: bool = Opt(False, "--ignore-warnings", "-IW", help="Ignore warnings, only show stats and errors"),
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verbose: bool = Opt(False, "--verbose", "-V", help="Print additional information and explanations"),
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no_format: bool = Opt(False, "--no-format", "-NF", help="Don't pretty-print the results"),
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# fmt: on
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):
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"""
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Analyze, debug and validate your training and development data, get useful
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stats, and find problems like invalid entity annotations, cyclic
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dependencies, low data labels and more.
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"""
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debug_data(
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lang,
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train_path,
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dev_path,
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tag_map_path=tag_map_path,
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base_model=base_model,
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pipeline=[p.strip() for p in pipeline.split(",")],
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ignore_warnings=ignore_warnings,
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verbose=verbose,
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no_format=no_format,
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silent=False,
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)
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def debug_data(
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lang: str,
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train_path: Path,
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dev_path: Path,
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*,
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tag_map_path: Optional[Path] = None,
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base_model: Optional[str] = None,
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pipeline: List[str] = ["tagger", "parser", "ner"],
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ignore_warnings: bool = False,
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verbose: bool = False,
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no_format: bool = True,
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silent: bool = True,
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):
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msg = Printer(
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no_print=silent, pretty=not no_format, ignore_warnings=ignore_warnings
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)
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# Make sure all files and paths exists if they are needed
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if not train_path.exists():
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msg.fail("Training data not found", train_path, exits=1)
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if not dev_path.exists():
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msg.fail("Development data not found", dev_path, exits=1)
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tag_map = {}
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if tag_map_path is not None:
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tag_map = srsly.read_json(tag_map_path)
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# Initialize the model and pipeline
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if base_model:
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nlp = load_model(base_model)
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else:
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lang_cls = get_lang_class(lang)
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nlp = lang_cls()
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# Update tag map with provided mapping
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nlp.vocab.morphology.tag_map.update(tag_map)
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msg.divider("Data format validation")
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# TODO: Validate data format using the JSON schema
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# TODO: update once the new format is ready
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# TODO: move validation to GoldCorpus in order to be able to load from dir
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# Create the gold corpus to be able to better analyze data
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loading_train_error_message = ""
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loading_dev_error_message = ""
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with msg.loading("Loading corpus..."):
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corpus = GoldCorpus(train_path, dev_path)
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try:
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train_dataset = list(corpus.train_dataset(nlp))
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train_dataset_unpreprocessed = list(
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corpus.train_dataset_without_preprocessing(nlp)
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)
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except ValueError as e:
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loading_train_error_message = f"Training data cannot be loaded: {e}"
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try:
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dev_dataset = list(corpus.dev_dataset(nlp))
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except ValueError as e:
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loading_dev_error_message = f"Development data cannot be loaded: {e}"
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if loading_train_error_message or loading_dev_error_message:
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if loading_train_error_message:
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msg.fail(loading_train_error_message)
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if loading_dev_error_message:
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msg.fail(loading_dev_error_message)
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sys.exit(1)
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msg.good("Corpus is loadable")
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# Create all gold data here to avoid iterating over the train_dataset constantly
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gold_train_data = _compile_gold(train_dataset, pipeline, nlp)
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gold_train_unpreprocessed_data = _compile_gold(
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train_dataset_unpreprocessed, pipeline
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)
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gold_dev_data = _compile_gold(dev_dataset, pipeline, nlp)
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train_texts = gold_train_data["texts"]
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dev_texts = gold_dev_data["texts"]
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msg.divider("Training stats")
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msg.text(f"Training pipeline: {', '.join(pipeline)}")
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for pipe in [p for p in pipeline if p not in nlp.factories]:
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msg.fail(f"Pipeline component '{pipe}' not available in factories")
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if base_model:
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msg.text(f"Starting with base model '{base_model}'")
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else:
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msg.text(f"Starting with blank model '{lang}'")
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msg.text(f"{len(train_dataset)} training docs")
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msg.text(f"{len(dev_dataset)} evaluation docs")
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if not len(gold_dev_data):
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msg.fail("No evaluation docs")
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overlap = len(train_texts.intersection(dev_texts))
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if overlap:
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msg.warn(f"{overlap} training examples also in evaluation data")
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else:
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msg.good("No overlap between training and evaluation data")
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if not base_model and len(train_dataset) < BLANK_MODEL_THRESHOLD:
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text = (
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f"Low number of examples to train from a blank model ({len(train_dataset)})"
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)
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if len(train_dataset) < BLANK_MODEL_MIN_THRESHOLD:
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msg.fail(text)
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else:
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msg.warn(text)
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msg.text(
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f"It's recommended to use at least {BLANK_MODEL_THRESHOLD} examples "
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f"(minimum {BLANK_MODEL_MIN_THRESHOLD})",
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show=verbose,
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)
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msg.divider("Vocab & Vectors")
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n_words = gold_train_data["n_words"]
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msg.info(
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f"{n_words} total word(s) in the data ({len(gold_train_data['words'])} unique)"
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)
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if gold_train_data["n_misaligned_words"] > 0:
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n_misaligned = gold_train_data["n_misaligned_words"]
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msg.warn(f"{n_misaligned} misaligned tokens in the training data")
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if gold_dev_data["n_misaligned_words"] > 0:
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n_misaligned = gold_dev_data["n_misaligned_words"]
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msg.warn(f"{n_misaligned} misaligned tokens in the dev data")
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most_common_words = gold_train_data["words"].most_common(10)
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msg.text(
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f"10 most common words: {_format_labels(most_common_words, counts=True)}",
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show=verbose,
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)
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if len(nlp.vocab.vectors):
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msg.info(
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f"{len(nlp.vocab.vectors)} vectors ({nlp.vocab.vectors.n_keys} "
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f"unique keys, {nlp.vocab.vectors_length} dimensions)"
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)
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n_missing_vectors = sum(gold_train_data["words_missing_vectors"].values())
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msg.warn(
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"{} words in training data without vectors ({:0.2f}%)".format(
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n_missing_vectors, n_missing_vectors / gold_train_data["n_words"],
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),
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)
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msg.text(
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"10 most common words without vectors: {}".format(
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_format_labels(
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gold_train_data["words_missing_vectors"].most_common(10),
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counts=True,
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)
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),
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show=verbose,
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)
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else:
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msg.info("No word vectors present in the model")
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if "ner" in pipeline:
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# Get all unique NER labels present in the data
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labels = set(
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label for label in gold_train_data["ner"] if label not in ("O", "-", None)
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)
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label_counts = gold_train_data["ner"]
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model_labels = _get_labels_from_model(nlp, "ner")
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new_labels = [l for l in labels if l not in model_labels]
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existing_labels = [l for l in labels if l in model_labels]
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has_low_data_warning = False
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has_no_neg_warning = False
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has_ws_ents_error = False
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has_punct_ents_warning = False
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msg.divider("Named Entity Recognition")
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msg.info(
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f"{len(new_labels)} new label(s), {len(existing_labels)} existing label(s)"
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)
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missing_values = label_counts["-"]
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msg.text(f"{missing_values} missing value(s) (tokens with '-' label)")
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for label in new_labels:
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if len(label) == 0:
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msg.fail("Empty label found in new labels")
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if new_labels:
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labels_with_counts = [
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(label, count)
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for label, count in label_counts.most_common()
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if label != "-"
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]
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labels_with_counts = _format_labels(labels_with_counts, counts=True)
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msg.text(f"New: {labels_with_counts}", show=verbose)
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if existing_labels:
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msg.text(f"Existing: {_format_labels(existing_labels)}", show=verbose)
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if gold_train_data["ws_ents"]:
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msg.fail(f"{gold_train_data['ws_ents']} invalid whitespace entity spans")
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has_ws_ents_error = True
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if gold_train_data["punct_ents"]:
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msg.warn(f"{gold_train_data['punct_ents']} entity span(s) with punctuation")
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has_punct_ents_warning = True
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for label in new_labels:
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if label_counts[label] <= NEW_LABEL_THRESHOLD:
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msg.warn(
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f"Low number of examples for new label '{label}' ({label_counts[label]})"
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)
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has_low_data_warning = True
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with msg.loading("Analyzing label distribution..."):
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neg_docs = _get_examples_without_label(train_dataset, label)
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if neg_docs == 0:
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msg.warn(f"No examples for texts WITHOUT new label '{label}'")
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has_no_neg_warning = True
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if not has_low_data_warning:
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msg.good("Good amount of examples for all labels")
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if not has_no_neg_warning:
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msg.good("Examples without occurrences available for all labels")
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if not has_ws_ents_error:
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msg.good("No entities consisting of or starting/ending with whitespace")
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if not has_punct_ents_warning:
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msg.good("No entities consisting of or starting/ending with punctuation")
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if has_low_data_warning:
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msg.text(
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f"To train a new entity type, your data should include at "
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f"least {NEW_LABEL_THRESHOLD} instances of the new label",
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show=verbose,
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)
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if has_no_neg_warning:
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msg.text(
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"Training data should always include examples of entities "
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"in context, as well as examples without a given entity "
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"type.",
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show=verbose,
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)
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if has_ws_ents_error:
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msg.text(
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"As of spaCy v2.1.0, entity spans consisting of or starting/ending "
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"with whitespace characters are considered invalid."
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)
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if has_punct_ents_warning:
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msg.text(
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"Entity spans consisting of or starting/ending "
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"with punctuation can not be trained with a noise level > 0."
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)
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if "textcat" in pipeline:
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msg.divider("Text Classification")
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labels = [label for label in gold_train_data["cats"]]
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model_labels = _get_labels_from_model(nlp, "textcat")
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new_labels = [l for l in labels if l not in model_labels]
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existing_labels = [l for l in labels if l in model_labels]
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msg.info(
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f"Text Classification: {len(new_labels)} new label(s), "
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f"{len(existing_labels)} existing label(s)"
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)
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if new_labels:
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labels_with_counts = _format_labels(
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gold_train_data["cats"].most_common(), counts=True
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)
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msg.text(f"New: {labels_with_counts}", show=verbose)
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if existing_labels:
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msg.text(f"Existing: {_format_labels(existing_labels)}", show=verbose)
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if set(gold_train_data["cats"]) != set(gold_dev_data["cats"]):
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msg.fail(
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f"The train and dev labels are not the same. "
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f"Train labels: {_format_labels(gold_train_data['cats'])}. "
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f"Dev labels: {_format_labels(gold_dev_data['cats'])}."
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)
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if gold_train_data["n_cats_multilabel"] > 0:
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msg.info(
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"The train data contains instances without "
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"mutually-exclusive classes. Use '--textcat-multilabel' "
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"when training."
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)
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if gold_dev_data["n_cats_multilabel"] == 0:
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msg.warn(
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"Potential train/dev mismatch: the train data contains "
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"instances without mutually-exclusive classes while the "
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"dev data does not."
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)
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else:
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msg.info(
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"The train data contains only instances with "
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"mutually-exclusive classes."
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)
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if gold_dev_data["n_cats_multilabel"] > 0:
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msg.fail(
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"Train/dev mismatch: the dev data contains instances "
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"without mutually-exclusive classes while the train data "
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"contains only instances with mutually-exclusive classes."
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)
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if "tagger" in pipeline:
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msg.divider("Part-of-speech Tagging")
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labels = [label for label in gold_train_data["tags"]]
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tag_map = nlp.vocab.morphology.tag_map
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msg.info(f"{len(labels)} label(s) in data ({len(tag_map)} label(s) in tag map)")
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labels_with_counts = _format_labels(
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gold_train_data["tags"].most_common(), counts=True
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)
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msg.text(labels_with_counts, show=verbose)
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non_tagmap = [l for l in labels if l not in tag_map]
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if not non_tagmap:
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msg.good(f"All labels present in tag map for language '{nlp.lang}'")
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for label in non_tagmap:
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msg.fail(f"Label '{label}' not found in tag map for language '{nlp.lang}'")
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if "parser" in pipeline:
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has_low_data_warning = False
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msg.divider("Dependency Parsing")
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# profile sentence length
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msg.info(
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f"Found {gold_train_data['n_sents']} sentence(s) with an average "
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f"length of {gold_train_data['n_words'] / gold_train_data['n_sents']:.1f} words."
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)
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# check for documents with multiple sentences
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sents_per_doc = gold_train_data["n_sents"] / len(gold_train_data["texts"])
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if sents_per_doc < 1.1:
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msg.warn(
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f"The training data contains {sents_per_doc:.2f} sentences per "
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f"document. When there are very few documents containing more "
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f"than one sentence, the parser will not learn how to segment "
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f"longer texts into sentences."
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)
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# profile labels
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labels_train = [label for label in gold_train_data["deps"]]
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labels_train_unpreprocessed = [
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label for label in gold_train_unpreprocessed_data["deps"]
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]
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labels_dev = [label for label in gold_dev_data["deps"]]
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if gold_train_unpreprocessed_data["n_nonproj"] > 0:
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n_nonproj = gold_train_unpreprocessed_data["n_nonproj"]
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msg.info(f"Found {n_nonproj} nonprojective train sentence(s)")
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if gold_dev_data["n_nonproj"] > 0:
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n_nonproj = gold_dev_data["n_nonproj"]
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msg.info(f"Found {n_nonproj} nonprojective dev sentence(s)")
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msg.info(f"{labels_train_unpreprocessed} label(s) in train data")
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msg.info(f"{len(labels_train)} label(s) in projectivized train data")
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labels_with_counts = _format_labels(
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gold_train_unpreprocessed_data["deps"].most_common(), counts=True
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)
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msg.text(labels_with_counts, show=verbose)
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# rare labels in train
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for label in gold_train_unpreprocessed_data["deps"]:
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if gold_train_unpreprocessed_data["deps"][label] <= DEP_LABEL_THRESHOLD:
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msg.warn(
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f"Low number of examples for label '{label}' "
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f"({gold_train_unpreprocessed_data['deps'][label]})"
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)
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has_low_data_warning = True
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# rare labels in projectivized train
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rare_projectivized_labels = []
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for label in gold_train_data["deps"]:
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if gold_train_data["deps"][label] <= DEP_LABEL_THRESHOLD and "||" in label:
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rare_projectivized_labels.append(
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f"{label}: {gold_train_data['deps'][label]}"
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)
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if len(rare_projectivized_labels) > 0:
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msg.warn(
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f"Low number of examples for {len(rare_projectivized_labels)} "
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"label(s) in the projectivized dependency trees used for "
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"training. You may want to projectivize labels such as punct "
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"before training in order to improve parser performance."
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)
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msg.warn(
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f"Projectivized labels with low numbers of examples: ",
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", ".join(rare_projectivized_labels),
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show=verbose,
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)
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has_low_data_warning = True
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# labels only in train
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if set(labels_train) - set(labels_dev):
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msg.warn(
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"The following labels were found only in the train data:",
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", ".join(set(labels_train) - set(labels_dev)),
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show=verbose,
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)
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# labels only in dev
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if set(labels_dev) - set(labels_train):
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msg.warn(
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"The following labels were found only in the dev data:",
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", ".join(set(labels_dev) - set(labels_train)),
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show=verbose,
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)
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if has_low_data_warning:
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msg.text(
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f"To train a parser, your data should include at "
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f"least {DEP_LABEL_THRESHOLD} instances of each label.",
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show=verbose,
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)
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# multiple root labels
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if len(gold_train_unpreprocessed_data["roots"]) > 1:
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msg.warn(
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f"Multiple root labels "
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f"({', '.join(gold_train_unpreprocessed_data['roots'])}) "
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f"found in training data. spaCy's parser uses a single root "
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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], pipeline: List[str], nlp: Language
|
|
) -> 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 example in examples:
|
|
gold = example.gold
|
|
doc = example.doc
|
|
valid_words = [x for x in gold.words if x is not None]
|
|
data["words"].update(valid_words)
|
|
data["n_words"] += len(valid_words)
|
|
data["n_misaligned_words"] += len(gold.words) - 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 pipeline:
|
|
for i, label in enumerate(gold.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 pipeline:
|
|
data["cats"].update(gold.cats)
|
|
if list(gold.cats.values()).count(1.0) != 1:
|
|
data["n_cats_multilabel"] += 1
|
|
if "tagger" in pipeline:
|
|
data["tags"].update([x for x in gold.tags if x is not None])
|
|
if "parser" in pipeline:
|
|
data["deps"].update([x for x in gold.labels if x is not None])
|
|
for i, (dep, head) in enumerate(zip(gold.labels, gold.heads)):
|
|
if head == i:
|
|
data["roots"].update([dep])
|
|
data["n_sents"] += 1
|
|
if nonproj.is_nonproj_tree(gold.heads):
|
|
data["n_nonproj"] += 1
|
|
if nonproj.contains_cycle(gold.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 ex in data:
|
|
labels = [
|
|
label.split("-")[1]
|
|
for label in ex.gold.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
|