mirror of
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b5d999e510
* Add doc.cats to spacy.gold at the paragraph level
Support `doc.cats` as `"cats": [{"label": string, "value": number}]` in
the spacy JSON training format at the paragraph level.
* `spacy.gold.docs_to_json()` writes `docs.cats`
* `GoldCorpus` reads in cats in each `GoldParse`
* Update instances of gold_tuples to handle cats
Update iteration over gold_tuples / gold_parses to handle addition of
cats at the paragraph level.
* Add textcat to train CLI
* Add textcat options to train CLI
* Add textcat labels in `TextCategorizer.begin_training()`
* Add textcat evaluation to `Scorer`:
* For binary exclusive classes with provided label: F1 for label
* For 2+ exclusive classes: F1 macro average
* For multilabel (not exclusive): ROC AUC macro average (currently
relying on sklearn)
* Provide user info on textcat evaluation settings, potential
incompatibilities
* Provide pipeline to Scorer in `Language.evaluate` for textcat config
* Customize train CLI output to include only metrics relevant to current
pipeline
* Add textcat evaluation to evaluate CLI
* Fix handling of unset arguments and config params
Fix handling of unset arguments and model confiug parameters in Scorer
initialization.
* Temporarily add sklearn requirement
* Remove sklearn version number
* Improve Scorer handling of models without textcats
* Fixing Scorer handling of models without textcats
* Update Scorer output for python 2.7
* Modify inf in Scorer for python 2.7
* Auto-format
Also make small adjustments to make auto-formatting with black easier and produce nicer results
* Move error message to Errors
* Update documentation
* Add cats to annotation JSON format [ci skip]
* Fix tpl flag and docs [ci skip]
* Switch to internal roc_auc_score
Switch to internal `roc_auc_score()` adapted from scikit-learn.
* Add AUCROCScore tests and improve errors/warnings
* Add tests for AUCROCScore and roc_auc_score
* Add missing error for only positive/negative values
* Remove unnecessary warnings and errors
* Make reduced roc_auc_score functions private
Because most of the checks and warnings have been stripped for the
internal functions and access is only intended through `ROCAUCScore`,
make the functions for roc_auc_score adapted from scikit-learn private.
* Check that data corresponds with multilabel flag
Check that the training instances correspond with the multilabel flag,
adding the multilabel flag if required.
* Add textcat score to early stopping check
* Add more checks to debug-data for textcat
* Add example training data for textcat
* Add more checks to textcat train CLI
* Check configuration when extending base model
* Fix typos
* Update textcat example data
* Provide licensing details and licenses for data
* Remove two labels with no positive instances from jigsaw-toxic-comment
data.
Co-authored-by: Ines Montani <ines@ines.io>
54 lines
1.5 KiB
Python
54 lines
1.5 KiB
Python
from pathlib import Path
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import plac
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import spacy
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from spacy.gold import docs_to_json
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import srsly
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import sys
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@plac.annotations(
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model=("Model name. Defaults to 'en'.", "option", "m", str),
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input_file=("Input file (jsonl)", "positional", None, Path),
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output_dir=("Output directory", "positional", None, Path),
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n_texts=("Number of texts to convert", "option", "t", int),
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)
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def convert(model='en', input_file=None, output_dir=None, n_texts=0):
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# Load model with tokenizer + sentencizer only
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nlp = spacy.load(model)
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nlp.disable_pipes(*nlp.pipe_names)
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sentencizer = nlp.create_pipe("sentencizer")
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nlp.add_pipe(sentencizer, first=True)
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texts = []
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cats = []
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count = 0
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if not input_file.exists():
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print("Input file not found:", input_file)
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sys.exit(1)
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else:
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with open(input_file) as fileh:
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for line in fileh:
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data = srsly.json_loads(line)
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texts.append(data["text"])
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cats.append(data["cats"])
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if output_dir is not None:
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output_dir = Path(output_dir)
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if not output_dir.exists():
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output_dir.mkdir()
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else:
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output_dir = Path(".")
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docs = []
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for i, doc in enumerate(nlp.pipe(texts)):
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doc.cats = cats[i]
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docs.append(doc)
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if n_texts > 0 and count == n_texts:
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break
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count += 1
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srsly.write_json(output_dir / input_file.with_suffix(".json"), [docs_to_json(docs)])
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if __name__ == "__main__":
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plac.call(convert)
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