* Refactor Docs.is_ flags
* Add derived `Doc.has_annotation` method
* `Doc.has_annotation(attr)` returns `True` for partial annotation
* `Doc.has_annotation(attr, require_complete=True)` returns `True` for
complete annotation
* Add deprecation warnings to `is_tagged`, `is_parsed`, `is_sentenced`
and `is_nered`
* Add `Doc._get_array_attrs()`, which returns a full list of `Doc` attrs
for use with `Doc.to_array`, `Doc.to_bytes` and `Doc.from_docs`. The
list is the `DocBin` attributes list plus `SPACY` and `LENGTH`.
Notes on `Doc.has_annotation`:
* `HEAD` is converted to `DEP` because heads don't have an unset state
* Accept `IS_SENT_START` as a synonym of `SENT_START`
Additional changes:
* Add `NORM`, `ENT_ID` and `SENT_START` to default attributes for
`DocBin`
* In `Doc.from_array()` the presence of `DEP` causes `HEAD` to override
`SENT_START`
* In `Doc.from_array()` using `attrs` other than
`Doc._get_array_attrs()` (i.e., a user's custom list rather than our
default internal list) with both `HEAD` and `SENT_START` shows a warning
that `HEAD` will override `SENT_START`
* `set_children_from_heads` does not require dependency labels to set
sentence boundaries and sets `sent_start` for all non-sentence starts to
`-1`
* Fix call to set_children_form_heads
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
For languages without provided models and with lemmatizer rules in
`spacy-lookups-data`, make the rule-based lemmatizer the default:
Bengali, Persian, Norwegian, Swedish
* Update stop_words.py
Hebrew STOP WORDS
* Update stop_words.py
* contributor
* contributor
* add some common domain extentions
support human number 1K/1M....
* support human number 1K/1M....
* hebrew number tokenize
1K/1M implement in EN
* test human tokenize fix
* test
* heb like num
revert human number change
* heb like num
* Create lex_attrs.py
Hello,
I am missing a CZECH language in SpaCy. So I would like to help to push it a little. This file is base on others lex_attrs.py files just with translation to Czech.
* Update __init__.py
Updated for use with new Czech Lex_attrs file
* Update stop_words.py
* Create test_text.py
Co-authored-by: Vladimír Holubec <vholubec@arcdata.cz>
* Add Lemmatizer and simplify related components
* Add `Lemmatizer` pipe with `lookup` and `rule` modes using the
`Lookups` tables.
* Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma)
* Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer,
or morph rules)
* Remove lemmatizer from `Vocab`
* Adjust many many tests
Differences:
* No default lookup lemmas
* No special treatment of TAG in `from_array` and similar required
* Easier to modify labels in a `Tagger`
* No extra strings added from morphology / tag map
* Fix test
* Initial fix for Lemmatizer config/serialization
* Adjust init test to be more generic
* Adjust init test to force empty Lookups
* Add simple cache to rule-based lemmatizer
* Convert language-specific lemmatizers
Convert language-specific lemmatizers to component lemmatizers. Remove
previous lemmatizer class.
* Fix French and Polish lemmatizers
* Remove outdated UPOS conversions
* Update Russian lemmatizer init in tests
* Add minimal init/run tests for custom lemmatizers
* Add option to overwrite existing lemmas
* Update mode setting, lookup loading, and caching
* Make `mode` an immutable property
* Only enforce strict `load_lookups` for known supported modes
* Move caching into individual `_lemmatize` methods
* Implement strict when lang is not found in lookups
* Fix tables/lookups in make_lemmatizer
* Reallow provided lookups and allow for stricter checks
* Add lookups asset to all Lemmatizer pipe tests
* Rename lookups in lemmatizer init test
* Clean up merge
* Refactor lookup table loading
* Add helper from `load_lemmatizer_lookups` that loads required and
optional lookups tables based on settings provided by a config.
Additional slight refactor of lookups:
* Add `Lookups.set_table` to set a table from a provided `Table`
* Reorder class definitions to be able to specify type as `Table`
* Move registry assets into test methods
* Refactor lookups tables config
Use class methods within `Lemmatizer` to provide the config for
particular modes and to load the lookups from a config.
* Add pipe and score to lemmatizer
* Simplify Tagger.score
* Add missing import
* Clean up imports and auto-format
* Remove unused kwarg
* Tidy up and auto-format
* Update docstrings for Lemmatizer
Update docstrings for Lemmatizer.
Additionally modify `is_base_form` API to take `Token` instead of
individual features.
* Update docstrings
* Remove tag map values from Tagger.add_label
* Update API docs
* Fix relative link in Lemmatizer API docs
* 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>
* 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.
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.
* 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`.
* 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
* 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
* 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>
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.
Update Polish tokenizer for UD_Polish-PDB, which is a relatively major
change from the existing tokenizer. Unused exceptions files and
conflicting test cases removed.
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Reduce stored lexemes data, move feats to lookups
* Move non-derivable lexemes features (`norm / cluster / prob`) to
`spacy-lookups-data` as lookups
* Get/set `norm` in both lookups and `LexemeC`, serialize in lookups
* Remove `cluster` and `prob` from `LexemesC`, get/set/serialize in
lookups only
* Remove serialization of lexemes data as `vocab/lexemes.bin`
* Remove `SerializedLexemeC`
* Remove `Lexeme.to_bytes/from_bytes`
* Modify normalization exception loading:
* Always create `Vocab.lookups` table `lexeme_norm` for
normalization exceptions
* Load base exceptions from `lang.norm_exceptions`, but load
language-specific exceptions from lookups
* Set `lex_attr_getter[NORM]` including new lookups table in
`BaseDefaults.create_vocab()` and when deserializing `Vocab`
* Remove all cached lexemes when deserializing vocab to override
existing normalizations with the new normalizations (as a replacement
for the previous step that replaced all lexemes data with the
deserialized data)
* Skip English normalization test
Skip English normalization test because the data is now in
`spacy-lookups-data`.
* Remove norm exceptions
Moved to spacy-lookups-data.
* Move norm exceptions test to spacy-lookups-data
* Load extra lookups from spacy-lookups-data lazily
Load extra lookups (currently for cluster and prob) lazily from the
entry point `lg_extra` as `Vocab.lookups_extra`.
* Skip creating lexeme cache on load
To improve model loading times, do not create the full lexeme cache when
loading. The lexemes will be created on demand when processing.
* Identify numeric values in Lexeme.set_attrs()
With the removal of a special case for `PROB`, also identify `float` to
avoid trying to convert it with the `StringStore`.
* Skip lexeme cache init in from_bytes
* Unskip and update lookups tests for python3.6+
* Update vocab pickle to include lookups_extra
* Update vocab serialization tests
Check strings rather than lexemes since lexemes aren't initialized
automatically, account for addition of "_SP".
* Re-skip lookups test because of python3.5
* Skip PROB/float values in Lexeme.set_attrs
* Convert is_oov from lexeme flag to lex in vectors
Instead of storing `is_oov` as a lexeme flag, `is_oov` reports whether
the lexeme has a vector.
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Limiting noun_chunks for specific langauges
* Limiting noun_chunks for specific languages
Contributor Agreement
* Addressing review comments
* Removed unused fixtures and imports
* Add fa_tokenizer in test suite
* Use fa_tokenizer in test
* Undo extraneous reformatting
Co-authored-by: adrianeboyd <adrianeboyd@gmail.com>
Remove `TAG` value from Danish and Swedish tokenizer exceptions because
it may not be included in a tag map (and these settings are problematic
as tokenizer exceptions anyway).
Instead of treating `'d` in contractions like `I'd` as `would` in all
cases in the tokenizer exceptions, leave the tagging and lemmatization
up to later components.
To fix the slow tokenizer URL (#4374) and allow `token_match` to take
priority over prefixes and suffixes by default, introduce a new
tokenizer option for a token match pattern that's applied after prefixes
and suffixes but before infixes.
Modify jieba install message to instruct the user to use
`ChineseDefaults.use_jieba = False` so that it's possible to load
pkuseg-only models without jieba installed.
* Add pkuseg and serialization support for Chinese
Add support for pkuseg alongside jieba
* Specify model through `Language` meta:
* split on characters (if no word segmentation packages are installed)
```
Chinese(meta={"tokenizer": {"config": {"use_jieba": False, "use_pkuseg": False}}})
```
* jieba (remains the default tokenizer if installed)
```
Chinese()
Chinese(meta={"tokenizer": {"config": {"use_jieba": True}}}) # explicit
```
* pkuseg
```
Chinese(meta={"tokenizer": {"config": {"pkuseg_model": "default", "use_jieba": False, "use_pkuseg": True}}})
```
* The new tokenizer setting `require_pkuseg` is used to override
`use_jieba` default, which is intended for models that provide a pkuseg
model:
```
nlp_pkuseg = Chinese(meta={"tokenizer": {"config": {"pkuseg_model": "default", "require_pkuseg": True}}})
nlp = Chinese() # has `use_jieba` as `True` by default
nlp.from_bytes(nlp_pkuseg.to_bytes()) # `require_pkuseg` overrides `use_jieba` when calling the tokenizer
```
Add support for serialization of tokenizer settings and pkuseg model, if
loaded
* Add sorting for `Language.to_bytes()` serialization of `Language.meta`
so that the (emptied, but still present) tokenizer metadata is in a
consistent position in the serialized data
Extend tests to cover all three tokenizer configurations and
serialization
* Fix from_disk and tests without jieba or pkuseg
* Load cfg first and only show error if `use_pkuseg`
* Fix blank/default initialization in serialization tests
* Explicitly initialize jieba's cache on init
* Add serialization for pkuseg pre/postprocessors
* Reformat pkuseg install message
* Use inline flags in token_match patterns
Use inline flags in `token_match` patterns so that serializing does not
lose the flag information.
* Modify inline flag
* Modify inline flag
