* 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`
Provide more customized normalization table warnings when training a new
model. Only suggest installing `spacy-lookups-data` if it's not already
installed and it includes a table for this language (currently checked
in a hard-coded list).
* 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.
* Improve tag map initialization and updating
Generalize tag map initialization and updating so that a provided tag
map can be loaded correctly in the CLI.
* normalize provided tag map as necessary
* use the same method for initializing and overwriting the tag map
* 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.
Update `Morphology` to load exceptions in `Morphology.__init__` and
`Morphology.load_morph_exceptions` from the format used in `MORPH_RULES`
rather than the internal format with tuple keys.
* Rename to `Morphology.exc` to `Morphology._exc` for internal use with
tuple keys
* Add `Morphology.exc` as a property that converts the internal `_exc`
back to `MORPH_RULES` format, primarily for serialization
Remove corpus-specific tag maps from the language data for languages
without custom tokenizers. For languages with custom word segmenters
that also provide tags (Japanese and Korean), the tag maps for the
custom tokenizers are kept as the default.
The default tag maps for languages without custom tokenizers are now the
default tag map from `lang/tag_map/py`, UPOS -> UPOS.
* Add morph to morphology in Doc.from_array
Add morphological analyses to morphology table in `Doc.from_array`.
* Use separate vocab in DocBin roundtrip test
* adding debug-model to print the internals for debugging purposes
* expend debug-model script with 4 stages: before, init, train, predict
* avoid enforcing to have a seed in the train script
* small fixes
* Update project CLI hashes, directories, skipping
* Improve clone success message
* Remove unused context args
* Move project-specific utils to project utils
The hashing/checksum functions may not end up being general-purpose functions and are more designed for the projects, so they shouldn't live in spacy.util
* Improve run help and add workflows
* Add note re: directory checksum speed
* Fix cloning from subdirectories and output messages
* Remove hard-coded dirs
* add keyword separator for update functions and drop unused "state"
* few more Example tests and various small fixes
* consistently return losses after update call
* eliminate unused tensors field across pipe components
* fix name
* fix arg name
* Add initial reproducibility tests
* failing test for default_text_classifier (WIP)
* track trouble to underlying tok2vec layer
* add regression test for Issue 5551
* tests go green with https://github.com/explosion/thinc/pull/359
* update test
* adding fixed seeds to HashEmbed layers, seems to fix the reproducility issue
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Make project command a submodule
* Update with WIP
* Add helper for joining commands
* Update docstrins, formatting and types
* Update assets and add support for copying local files
* Fix type
* Update success messages
* Fix get_loss for None alignments in senter
When converting the `sent_start` values back to `SentenceRecognizer`
labels, handle `None` alignments.
* Handle SENT_START as -1
Handle SENT_START as -1 (or -1 converted to uint64) by treating any
values other than 1 the same as 0 in `SentenceRecognizer.get_loss`.
* Update get_loss for senter
Update `SentenceRecognizer.get_loss` to keep it similar to `Tagger`.
* Update get_loss for morphologizer
Update `Morphologizer.get_loss` to keep it similar to `Tagger`.
* remove _convert_examples
* fix test_gold, raise TypeError if tuples are used instead of Example's
* throwing proper errors when the wrong type of objects are passed
* fix deprectated format in tests
* fix deprectated format in parser tests
* fix tests for NEL, morph, senter, tagger, textcat
* update regression tests with new Example format
* use make_doc
* more fixes to nlp.update calls
* few more small fixes for rehearse and evaluate
* only import ml_datasets if really necessary
* Use cosine loss in Cloze multitask
* Fix char_embed for gpu
* Call resume_training for base model in train CLI
* Fix bilstm_depth default in pretrain command
* Implement character-based pretraining objective
* Use chars loss in ClozeMultitask
* Add method to decode predicted characters
* Fix number characters
* Rescale gradients for mlm
* Fix char embed+vectors in ml
* Fix pipes
* Fix pretrain args
* Move get_characters_loss
* Fix import
* Fix import
* Mention characters loss option in pretrain
* Remove broken 'self attention' option in pretrain
* Revert "Remove broken 'self attention' option in pretrain"
This reverts commit 56b820f6af.
* Document 'characters' objective of pretrain
* Tell convert CLI to store user data for Doc
* Remove assert
* Add has_unknwon_spaces flag on Doc
* Do not tokenize docs with unknown spaces in Corpus
* Handle conversion of unknown spaces in Example
* Fixes
* Fixes
* Draft has_known_spaces support in DocBin
* Add test for serialize has_unknown_spaces
* Fix DocBin serialization when has_unknown_spaces
* Use serialization in test
* Add static method to Doc to allow merging of multiple docs.
* Add error description for the error that occurs if docs with different
vocabs (from different languages) are merged in Doc.from_docs().
* Add test for Doc.from_docs() implementation.
* Fix using numpy's concatenate in Doc.from_docs.
* Replace typing's type annotations in from_docs.
* Simply remove type annotations in from_docs.
* Add documentation for Doc.from_docs to api.
* Simplify from_docs, its test and the api doc for codebase consistency.
* Fix merging of Doc objects that end with whitespaces (Achieved by simply not setting the SPACY attribute on whitespace tokens). Remove two unnecessary imports of attributes.
* Add merging of user data from Doc objects in from_docs. Add user data test case to corresponding test. Add applicable warning messages.
* Fix incorrect setting of tokens idx by using concatenated spaces (again). Add test case to corresponding test.
* Add MORPH to attrs
* Update warnings calls
* Remove out-dated error from merge
* Rename space_delimiter to ensure_whitespace
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add version number to DocBin
Add a version number to DocBin for future use.
* Add POS to all attributes in DocBin
* Add morph string to strings in DocBin
* Update DocBin API
* Add string for ENT_KB_ID in DocBin
* fixes in ud_train, UX for morphs
* update pyproject with new version of thinc
* fixes in debug_data script
* cleanup of old unused error messages
* remove obsolete TempErrors
* move error messages to errors.py
* add ENT_KB_ID to default DocBin serialization
* few fixes to simple_ner
* fix tags
* Convert custom user_data to token extension format
Convert the user_data values so that they can be loaded as custom token
extensions for `inflection`, `reading_form`, `sub_tokens`, and `lemma`.
* Reset Underscore state in ja tokenizer tests
Move `Lemmatizer.is_base_form` to the language settings so that each
language can provide a language-specific method as
`LanguageDefaults.is_base_form`.
The existing English-specific `Lemmatizer.is_base_form` is moved to
`EnglishDefaults`.
* Update errors
* Remove beam for now (maybe)
Remove beam_utils
Update setup.py
Remove beam
* Remove GoldParse
WIP on removing goldparse
Get ArcEager compiling after GoldParse excise
Update setup.py
Get spacy.syntax compiling after removing GoldParse
Rename NewExample -> Example and clean up
Clean html files
Start updating tests
Update Morphologizer
* fix error numbers
* fix merge conflict
* informative error when calling to_array with wrong field
* fix error catching
* fixing language and scoring tests
* start testing get_aligned
* additional tests for new get_aligned function
* Draft create_gold_state for arc_eager oracle
* Fix import
* Fix import
* Remove TokenAnnotation code from nonproj
* fixing NER one-to-many alignment
* Fix many-to-one IOB codes
* fix test for misaligned
* attempt to fix cases with weird spaces
* fix spaces
* test_gold_biluo_different_tokenization works
* allow None as BILUO annotation
* fixed some tests + WIP roundtrip unit test
* add spaces to json output format
* minibatch utiltiy can deal with strings, docs or examples
* fix augment (needs further testing)
* various fixes in scripts - needs to be further tested
* fix test_cli
* cleanup
* correct silly typo
* add support for MORPH in to/from_array, fix morphologizer overfitting test
* fix tagger
* fix entity linker
* ensure test keeps working with non-linked entities
* pipe() takes docs, not examples
* small bug fix
* textcat bugfix
* throw informative error when running the components with the wrong type of objects
* fix parser tests to work with example (most still failing)
* fix BiluoPushDown parsing entities
* small fixes
* bugfix tok2vec
* fix renames and simple_ner labels
* various small fixes
* prevent writing dummy values like deps because that could interfer with sent_start values
* fix the fix
* implement split_sent with aligned SENT_START attribute
* test for split sentences with various alignment issues, works
* Return ArcEagerGoldParse from ArcEager
* Update parser and NER gold stuff
* Draft new GoldCorpus class
* add links to to_dict
* clean up
* fix test checking for variants
* Fix oracles
* Start updating converters
* Move converters under spacy.gold
* Move things around
* Fix naming
* Fix name
* Update converter to produce DocBin
* Update converters
* Allow DocBin to take list of Doc objects.
* Make spacy convert output docbin
* Fix import
* Fix docbin
* Fix compile in ArcEager
* Fix import
* Serialize all attrs by default
* Update converter
* Remove jsonl converter
* Add json2docs converter
* Draft Corpus class for DocBin
* Work on train script
* Update Corpus
* Update DocBin
* Allocate Doc before starting to add words
* Make doc.from_array several times faster
* Update train.py
* Fix Corpus
* Fix parser model
* Start debugging arc_eager oracle
* Update header
* Fix parser declaration
* Xfail some tests
* Skip tests that cause crashes
* Skip test causing segfault
* Remove GoldCorpus
* Update imports
* Update after removing GoldCorpus
* Fix module name of corpus
* Fix mimport
* Work on parser oracle
* Update arc_eager oracle
* Restore ArcEager.get_cost function
* Update transition system
* Update test_arc_eager_oracle
* Remove beam test
* Update test
* Unskip
* Unskip tests
* add links to to_dict
* clean up
* fix test checking for variants
* Allow DocBin to take list of Doc objects.
* Fix compile in ArcEager
* Serialize all attrs by default
Move converters under spacy.gold
Move things around
Fix naming
Fix name
Update converter to produce DocBin
Update converters
Make spacy convert output docbin
Fix import
Fix docbin
Fix import
Update converter
Remove jsonl converter
Add json2docs converter
* Allocate Doc before starting to add words
* Make doc.from_array several times faster
* Start updating converters
* Work on train script
* Draft Corpus class for DocBin
Update Corpus
Fix Corpus
* Update DocBin
Add missing strings when serializing
* Update train.py
* Fix parser model
* Start debugging arc_eager oracle
* Update header
* Fix parser declaration
* Xfail some tests
Skip tests that cause crashes
Skip test causing segfault
* Remove GoldCorpus
Update imports
Update after removing GoldCorpus
Fix module name of corpus
Fix mimport
* Work on parser oracle
Update arc_eager oracle
Restore ArcEager.get_cost function
Update transition system
* Update tests
Remove beam test
Update test
Unskip
Unskip tests
* Add get_aligned_parse method in Example
Fix Example.get_aligned_parse
* Add kwargs to Corpus.dev_dataset to match train_dataset
* Update nonproj
* Use get_aligned_parse in ArcEager
* Add another arc-eager oracle test
* Remove Example.doc property
Remove Example.doc
Remove Example.doc
Remove Example.doc
Remove Example.doc
* Update ArcEager oracle
Fix Break oracle
* Debugging
* Fix Corpus
* Fix eg.doc
* Format
* small fixes
* limit arg for Corpus
* fix test_roundtrip_docs_to_docbin
* fix test_make_orth_variants
* fix add_label test
* Update tests
* avoid writing temp dir in json2docs, fixing 4402 test
* Update test
* Add missing costs to NER oracle
* Update test
* Work on Example.get_aligned_ner method
* Clean up debugging
* Xfail tests
* Remove prints
* Remove print
* Xfail some tests
* Replace unseen labels for parser
* Update test
* Update test
* Xfail test
* Fix Corpus
* fix imports
* fix docs_to_json
* various small fixes
* cleanup
* Support gold_preproc in Corpus
* Support gold_preproc
* Pass gold_preproc setting into corpus
* Remove debugging
* Fix gold_preproc
* Fix json2docs converter
* Fix convert command
* Fix flake8
* Fix import
* fix output_dir (converted to Path by typer)
* fix var
* bugfix: update states after creating golds to avoid out of bounds indexing
* Improve efficiency of ArEager oracle
* pull merge_sent into iob2docs to avoid Doc creation for each line
* fix asserts
* bugfix excl Span.end in iob2docs
* Support max_length in Corpus
* Fix arc_eager oracle
* Filter out uannotated sentences in NER
* Remove debugging in parser
* Simplify NER alignment
* Fix conversion of NER data
* Fix NER init_gold_batch
* Tweak efficiency of precomputable affine
* Update onto-json default
* Update gold test for NER
* Fix parser test
* Update test
* Add NER data test
* Fix convert for single file
* Fix test
* Hack scorer to avoid evaluating non-nered data
* Fix handling of NER data in Example
* Output unlabelled spans from O biluo tags in iob_utils
* Fix unset variable
* Return kept examples from init_gold_batch
* Return examples from init_gold_batch
* Dont return Example from init_gold_batch
* Set spaces on gold doc after conversion
* Add test
* Fix spaces reading
* Improve NER alignment
* Improve handling of missing values in NER
* Restore the 'cutting' in parser training
* Add assertion
* Print epochs
* Restore random cuts in parser/ner training
* Implement Doc.copy
* Implement Example.copy
* Copy examples at the start of Language.update
* Don't unset example docs
* Tweak parser model slightly
* attempt to fix _guess_spaces
* _add_entities_to_doc first, so that links don't get overwritten
* fixing get_aligned_ner for one-to-many
* fix indexing into x_text
* small fix biluo_tags_from_offsets
* Add onto-ner config
* Simplify NER alignment
* Fix NER scoring for partially annotated documents
* fix indexing into x_text
* fix test_cli failing tests by ignoring spans in doc.ents with empty label
* Fix limit
* Improve NER alignment
* Fix count_train
* Remove print statement
* fix tests, we're not having nothing but None
* fix clumsy fingers
* Fix tests
* Fix doc.ents
* Remove empty docs in Corpus and improve limit
* Update config
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
* Skip special tag _SP in check for new tag map
In `Tagger.begin_training()` check for new tags aside from `_SP` in the
new tag map initialized from the provided gold tuples when determining
whether to reinitialize the morphology with the new tag map.
* Simplify _SP check
* user_dict fields: adding inflections, reading_forms, sub_tokens
deleting: unidic_tags
improve code readability around the token alignment procedure
* add test cases, replace fugashi with sudachipy in conftest
* move bunsetu.py to spaCy Universe as a pipeline component BunsetuRecognizer
* tag is space -> both surface and tag are spaces
* consider len(text)==0
* Fix warning message for lemmatization tables
* Add a warning when the `lexeme_norm` table is empty. (Given the
relatively lang-specific loading for `Lookups`, it seemed like too much
overhead to dynamically extract the list of languages, so for now it's
hard-coded.)
* Added Examples for Tamil Sentences
#### Description
This PR add example sentences for the Tamil language which were missing as per issue #1107
#### Type of Change
This is an enhancement.
* Accepting spaCy Contributor Agreement
* Signed on my behalf as an individual
* Fix warning message for lemmatization tables
* Add a warning when the `lexeme_norm` table is empty. (Given the
relatively lang-specific loading for `Lookups`, it seemed like too much
overhead to dynamically extract the list of languages, so for now it's
hard-coded.)
* Added Examples for Tamil Sentences
#### Description
This PR add example sentences for the Tamil language which were missing as per issue #1107
#### Type of Change
This is an enhancement.
* Accepting spaCy Contributor Agreement
* Signed on my behalf as an individual
* added setting for neighbour sentence in NEL
* added spaCy contributor agreement
* added multi sentence also for training
* made the try-except block smaller
* added setting for neighbour sentence in NEL
* added spaCy contributor agreement
* added multi sentence also for training
* made the try-except block smaller
* verbose and tag_map options
* adding init_tok2vec option and only changing the tok2vec that is specified
* adding omit_extra_lookups and verifying textcat config
* wip
* pretrain bugfix
* add replace and resume options
* train_textcat fix
* raw text functionality
* improve UX when KeyError or when input data can't be parsed
* avoid unnecessary access to goldparse in TextCat pipe
* save performance information in nlp.meta
* add noise_level to config
* move nn_parser's defaults to config file
* multitask in config - doesn't work yet
* scorer offering both F and AUC options, need to be specified in config
* add textcat verification code from old train script
* small fixes to config files
* clean up
* set default config for ner/parser to allow create_pipe to work as before
* two more test fixes
* small fixes
* cleanup
* fix NER pickling + additional unit test
* create_pipe as before
* Use `config` dict for tokenizer settings
* Add serialization of split mode setting
* Add tests for tokenizer split modes and serialization of split mode
setting
Based on #5561
* Add more rules to deal with Japanese UD mappings
Japanese UD rules sometimes give different UD tags to tokens with the
same underlying POS tag. The UD spec indicates these cases should be
disambiguated using the output of a tool called "comainu", but rules are
enough to get the right result.
These rules are taken from Ginza at time of writing, see #3756.
* Add new tags from GSD
This is a few rare tags that aren't in Unidic but are in the GSD data.
* Add basic Japanese sentencization
This code is taken from Ginza again.
* Add sentenceizer quote handling
Could probably add more paired characters but this will do for now. Also
includes some tests.
* Replace fugashi with SudachiPy
* Modify tag format to match GSD annotations
Some of the tests still need to be updated, but I want to get this up
for testing training.
* Deal with case with closing punct without opening
* refactor resolve_pos()
* change tag field separator from "," to "-"
* add TAG_ORTH_MAP
* add TAG_BIGRAM_MAP
* revise rules for 連体詞
* revise rules for 連体詞
* improve POS about 2%
* add syntax_iterator.py (not mature yet)
* improve syntax_iterators.py
* improve syntax_iterators.py
* add phrases including nouns and drop NPs consist of STOP_WORDS
* First take at noun chunks
This works in many situations but still has issues in others.
If the start of a subtree has no noun, then nested phrases can be
generated.
また行きたい、そんな気持ちにさせてくれるお店です。
[そんな気持ち, また行きたい、そんな気持ちにさせてくれるお店]
For some reason て gets included sometimes. Not sure why.
ゲンに連れ添って円盤生物を調査するパートナーとなる。
[て円盤生物, ...]
Some phrases that look like they should be split are grouped together;
not entirely sure that's wrong. This whole thing becomes one chunk:
道の駅遠山郷北側からかぐら大橋南詰現道交点までの1.060kmのみ開通済み
* Use new generic get_words_and_spaces
The new get_words_and_spaces function is simpler than what was used in
Japanese, so it's good to be able to switch to it. However, there was an
issue. The new function works just on text, so POS info could get out of
sync. Fixing this required a small change to the way dtokens (tokens
with POS and lemma info) were generated.
Specifically, multiple extraneous spaces now become a single token, so
when generating dtokens multiple space tokens should be created in a
row.
* Fix noun_chunks, should be working now
* Fix some tests, add naughty strings tests
Some of the existing tests changed because the tokenization mode of
Sudachi changed to the more fine-grained A mode.
Sudachi also has issues with some strings, so this adds a test against
the naughty strings.
* Remove empty Sudachi tokens
Not doing this creates zero-length tokens and causes errors in the
internal spaCy processing.
* Add yield_bunsetu back in as a separate piece of code
Co-authored-by: Hiroshi Matsuda <40782025+hiroshi-matsuda-rit@users.noreply.github.com>
Co-authored-by: hiroshi <hiroshi_matsuda@megagon.ai>
Port relevant changes from #5361:
* Initialize lower flag explicitly
* Handle whitespace words from GoldParse correctly when creating raw
text with orth variants
Updates from #5362 and fix from #5387:
* `train`:
* if training on GPU, only run evaluation/timing on CPU in the first
iteration
* if training is aborted, exit with a non-0 exit status
This reverts commit 9393253b66.
The model shouldn't need to see all examples, and actually in v3 there's
no equivalent step. All examples are provided to the component, for the
component to do stuff like figuring out the labels. The model just needs
to do stuff like shape inference.
If `_SP` is already in the tag map, use the mapping from `_SP` instead
of `SP` so that `SP` can be a valid non-space tag. (Chinese has a
non-space tag `SP` which was overriding the mapping of `_SP` to
`SPACE`.)
Restructure Polish lemmatizer not to depend on lookups data in
`__init__` since the lemmatizer is initialized before the lookups data
is loaded from a saved model. The lookups tables are accessed first in
`__call__` instead once the data is available.
During `nlp.update`, components can be passed a boolean set_annotations
to indicate whether they should assign annotations to the `Doc`. This
needs to be called if downstream components expect to use the
annotations during training, e.g. if we wanted to use tagger features in
the parser.
Components can specify their assignments and requirements, so we can
figure out which components have these inter-dependencies. After
figuring this out, we can guess whether to pass set_annotations=True.
We could also call set_annotations=True always, or even just have this
as the only behaviour. The downside of this is that it would require the
`Doc` objects to be created afresh to avoid problematic modifications.
One approach would be to make a fresh copy of the `Doc` objects within
`nlp.update()`, so that we can write to the objects without any
problems. If we do that, we can drop this logic and also drop the
`set_annotations` mechanism. I would be fine with that approach,
although it runs the risk of introducing some performance overhead, and
we'll have to take care to copy all extension attributes etc.
