* 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>
* Adjust Table API and add docs
* Add attributes and update description [ci skip]
* Use strings.get_string_id instead of hash_string
* Fix table method calls
* Make orth arg in Lemmatizer.lookup optional
Fall back to string, which is now handled by Table.__contains__ out-of-the-box
* Fix method name
* Auto-format
Most of these characters are for languages / writing systems that aren't
supported by spacy, but I don't think it causes problems to include
them. In the UD evals, Hindi and Urdu improve a lot as expected (from
0-10% to 70-80%) and Persian improves a little (90% to 96%). Tamil
improves in combination with #4288.
The punctuation list is converted to a set internally because of its
increased length.
Sentence final punctuation generated with:
```
unichars -gas '[\p{Sentence_Break=STerm}\p{Sentence_Break=ATerm}]' '\p{Terminal_Punctuation}'
```
See: https://stackoverflow.com/a/9508766/461847Fixes#4269.
Add Kannada, Tamil, and Telugu unicode blocks to uncased character
classes so that period is recognized as a suffix during tokenization.
(I'm sure a few symbols in the code blocks should not be ALPHA, but this
is mainly relevant for suffix detection and seems to be an improvement
in practice.)
