* remove empty gold.pyx
* add alignment unit test (to be used in docs)
* ensure that Alignment is only used on equal texts
* additional test using example.alignment
* formatting
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* moving syntax folder to _parser_internals
* moving nn_parser and transition_system
* move nn_parser and transition_system out of internals folder
* moving nn_parser code into transition_system file
* rename transition_system to transition_parser
* moving parser_model and _state to ml
* move _state back to internals
* The Parser now inherits from Pipe!
* small code fixes
* removing unnecessary imports
* remove link_vectors_to_models
* transition_system to internals folder
* little bit more cleanup
* newlines
* add "greedy" option for match pattern
* distinction between greedy FIRST or LONGEST
* check for proper values, throw custom warning otherwise
* unxfail one more test
* add comment in docstring
* add test that LONGEST also prefers first match if equal length
* use c arrays for more efficient processing
* rename 'greediness' to 'greedy'
Add and update `score` methods, provided `scores`, and default weights
`default_score_weights` for pipeline components.
* `scores` provides all top-level keys returned by `score` (merely informative, similar to `assigns`).
* `default_score_weights` provides the default weights for a default config.
* The keys from `default_score_weights` determine which values will be
shown in the `spacy train` output, so keys with weight `0.0` will be
displayed but not counted toward the overall score.
* Provide top-level score as `attr_score`
* Provide a description of the score as `attr_score_desc`
* Provide all potential scores keys, setting unused keys to `None`
* Update CLI evaluate accordingly
* Refactor the Scorer to improve flexibility
Refactor the `Scorer` to improve flexibility for arbitrary pipeline
components.
* Individual pipeline components provide their own `evaluate` methods
that score a list of `Example`s and return a dictionary of scores
* `Scorer` is initialized either:
* with a provided pipeline containing components to be scored
* with a default pipeline containing the built-in statistical
components (senter, tagger, morphologizer, parser, ner)
* `Scorer.score` evaluates a list of `Example`s and returns a dictionary
of scores referring to the scores provided by the components in the
pipeline
Significant differences:
* `tags_acc` is renamed to `tag_acc` to be consistent with `token_acc`
and the new `morph_acc`, `pos_acc`, and `lemma_acc`
* Scoring is no longer cumulative: `Scorer.score` scores a list of
examples rather than a single example and does not retain any state
about previously scored examples
* PRF values in the returned scores are no longer multiplied by 100
* Add kwargs to Morphologizer.evaluate
* Create generalized scoring methods in Scorer
* Generalized static scoring methods are added to `Scorer`
* Methods require an attribute (either on Token or Doc) that is
used to key the returned scores
Naming differences:
* `uas`, `las`, and `las_per_type` in the scores dict are renamed to
`dep_uas`, `dep_las`, and `dep_las_per_type`
Scoring differences:
* `Doc.sents` is now scored as spans rather than on sentence-initial
token positions so that `Doc.sents` and `Doc.ents` can be scored with
the same method (this lowers scores since a single incorrect sentence
start results in two incorrect spans)
* Simplify / extend hasattr check for eval method
* Add hasattr check to tokenizer scoring
* Simplify to hasattr check for component scoring
* Reset Example alignment if docs are set
Reset the Example alignment if either doc is set in case the
tokenization has changed.
* Add PRF tokenization scoring for tokens as spans
Add PRF scores for tokens as character spans. The scores are:
* token_acc: # correct tokens / # gold tokens
* token_p/r/f: PRF for (token.idx, token.idx + len(token))
* Add docstring to Scorer.score_tokenization
* Rename component.evaluate() to component.score()
* Update Scorer API docs
* Update scoring for positive_label in textcat
* Fix TextCategorizer.score kwargs
* Update Language.evaluate docs
* Update score names in default config
* Update POS tests to reflect current behavior (it is not entirely clear
whether the AUX/VERB mapping is indeed the desired behavior?)
* Switch to `from_config` initialization in subtoken test
* `MorphAnalysis.get` returns only the field values
* Move `_normalize_props` inside `Morphology` as
`Morphology.normalize_attrs` and simplify
* Simplify POS field detection/conversion
* Convert all non-POS features to strings
* `Morphology` returns an empty string for a missing morph to align
with the FEATS string returned for an existing morph
* Remove unused `list_to_feats`
* Update with WIP
* Update with WIP
* Update with pipeline serialization
* Update types and pipe factories
* Add deep merge, tidy up and add tests
* Fix pipe creation from config
* Don't validate default configs on load
* Update spacy/language.py
Co-authored-by: Ines Montani <ines@ines.io>
* Adjust factory/component meta error
* Clean up factory args and remove defaults
* Add test for failing empty dict defaults
* Update pipeline handling and methods
* provide KB as registry function instead of as object
* small change in test to make functionality more clear
* update example script for EL configuration
* Fix typo
* Simplify test
* Simplify test
* splitting pipes.pyx into separate files
* moving default configs to each component file
* fix batch_size type
* removing default values from component constructors where possible (TODO: test 4725)
* skip instead of xfail
* Add test for config -> nlp with multiple instances
* pipeline.pipes -> pipeline.pipe
* Tidy up, document, remove kwargs
* small cleanup/generalization for Tok2VecListener
* use DEFAULT_UPSTREAM field
* revert to avoid circular imports
* Fix tests
* Replace deprecated arg
* Make model dirs require config
* fix pickling of keyword-only arguments in constructor
* WIP: clean up and integrate full config
* Add helper to handle function args more reliably
Now also includes keyword-only args
* Fix config composition and serialization
* Improve config debugging and add visual diff
* Remove unused defaults and fix type
* Remove pipeline and factories from meta
* Update spacy/default_config.cfg
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update spacy/default_config.cfg
* small UX edits
* avoid printing stack trace for debug CLI commands
* Add support for language-specific factories
* specify the section of the config which holds the model to debug
* WIP: add Language.from_config
* Update with language data refactor WIP
* Auto-format
* Add backwards-compat handling for Language.factories
* Update morphologizer.pyx
* Fix morphologizer
* Update and simplify lemmatizers
* Fix Japanese tests
* Port over tagger changes
* Fix Chinese and tests
* Update to latest Thinc
* WIP: xfail first Russian lemmatizer test
* Fix component-specific overrides
* fix nO for output layers in debug_model
* Fix default value
* Fix tests and don't pass objects in config
* Fix deep merging
* Fix lemma lookup data registry
Only load the lookups if an entry is available in the registry (and if spacy-lookups-data is installed)
* Add types
* Add Vocab.from_config
* Fix typo
* Fix tests
* Make config copying more elegant
* Fix pipe analysis
* Fix lemmatizers and is_base_form
* WIP: move language defaults to config
* Fix morphology type
* Fix vocab
* Remove comment
* Update to latest Thinc
* Add morph rules to config
* Tidy up
* Remove set_morphology option from tagger factory
* Hack use_gpu
* Move [pipeline] to top-level block and make [nlp.pipeline] list
Allows separating component blocks from component order – otherwise, ordering the config would mean a changed component order, which is bad. Also allows initial config to define more components and not use all of them
* Fix use_gpu and resume in CLI
* Auto-format
* Remove resume from config
* Fix formatting and error
* [pipeline] -> [components]
* Fix types
* Fix tagger test: requires set_morphology?
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* step_through tests: skip instead of xfail
* test_empty_doc should be fixed with new Thinc version
* remove outdated test (there are other misaligned tests now)
* xfail reason
* fix test according to french exceptions
* clarified some skipped tests
* skip ukranian test instead of xfail
* skip instead of xfail
* skip + reason instead of xfail
* removed obsolete tests referring to removed "set_frozen" functionality
* fix test 999
* remove unused AlignmentError
* remove xfail where possible, skip otherwise
* increment thinc release for empty_doc test
* Refactor Chinese tokenizer configuration
Refactor `ChineseTokenizer` configuration so that it uses a single
`segmenter` setting to choose between character segmentation, jieba, and
pkuseg.
* replace `use_jieba`, `use_pkuseg`, `require_pkuseg` with the setting
`segmenter` with the supported values: `char`, `jieba`, `pkuseg`
* make the default segmenter plain character segmentation `char` (no
additional libraries required)
* Fix Chinese serialization test to use char default
* Warn if attempting to customize other segmenter
Add a warning if `Chinese.pkuseg_update_user_dict` is called when
another segmenter is selected.
* Improve tag map initialization and updating
Generalize tag map initialization and updating so that the tag map can
be loaded correctly prior to loading a `Corpus` with `spacy debug-data`
and `spacy train`.
* normalize provided tag map as necessary
* use the same method for initializing and updating the tag map
* Replace rather than update tag map
Replace rather than update tag map when loading a custom tag map.
Updating the tag map is problematic due to the sorted list of tag names
and the fact that the tag map will contain lingering/unwanted tags from
the default tag map.
* Update CLI scripts
* Reinitialize cache after loading new tag map
Reinitialize the cache with the right size after loading a new tag map.
* update `Morphologizer.begin_training` for use with `Example`
* make init and begin_training more consistent
* add `Morphology.normalize_features` to normalize outside of
`Morphology.add`
* make sure `get_loss` doesn't create unknown labels when the POS and
morph alignments differ
Serialize `morph_rules` with the tagger alongside the `tag_map`.
Use `Morphology.load_tag_map` and `Morphology.load_morph_exceptions` to
load these settings rather than reinitializing the morphology each time
they are changed.
