* 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.
* Tidy up train-from-config a bit
* Fix accidentally quadratic perf in TokenAnnotation.brackets
When we're reading in the gold data, we had a nested loop where
we looped over the brackets for each token, looking for brackets
that start on that word. This is accidentally quadratic, because
we have one bracket per word (for the POS tags). So we had
an O(N**2) behaviour here that ended up being pretty slow.
To solve this I'm indexing the brackets by their starting word
on the TokenAnnotations object, and having a property to provide
the previous view.
* Fixes
* setting KB in the EL constructor, similar to how the model is passed on
* removing wikipedia example files - moved to projects
* throw an error when nlp.update is called with 2 positional arguments
* rewriting the config logic in create pipe to accomodate for other objects (e.g. KB) in the config
* update config files with new parameters
* avoid training pipeline components that don't have a model (like sentencizer)
* various small fixes + UX improvements
* small fixes
* set thinc to 8.0.0a9 everywhere
* remove outdated comment
* Fix most_similar for vectors with unused rows
Address issues related to the unused rows in the vector table and
`most_similar`:
* Update `most_similar()` to search only through rows that are in use
according to `key2row`.
* Raise an error when `most_similar(n=n)` is larger than the number of
vectors in the table.
* Set and restore `_unset` correctly when vectors are added or
deserialized so that new vectors are added in the correct row.
* Set data and keys to the same length in `Vocab.prune_vectors()` to
avoid spurious entries in `key2row`.
* Fix regression test using `most_similar`
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Add warning for misaligned character offset spans
* Resolve conflict
* Filter warnings in example scripts
Filter warnings in example scripts to show warnings once, in particular
warnings about misaligned entities.
Co-authored-by: Ines Montani <ines@ines.io>
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>
* make disable_pipes deprecated in favour of the new toggle_pipes
* rewrite disable_pipes statements
* update documentation
* remove bin/wiki_entity_linking folder
* one more fix
* remove deprecated link to documentation
* few more doc fixes
* add note about name change to the docs
* restore original disable_pipes
* small fixes
* fix typo
* fix error number to W096
* rename to select_pipes
* also make changes to the documentation
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Draft layer for BILUO actions
* Fixes to biluo layer
* WIP on BILUO layer
* Add tests for BILUO layer
* Format
* Fix transitions
* Update test
* Link in the simple_ner
* Update BILUO tagger
* Update __init__
* Import simple_ner
* Update test
* Import
* Add files
* Add config
* Fix label passing for BILUO and tagger
* Fix label handling for simple_ner component
* Update simple NER test
* Update config
* Hack train script
* Update BILUO layer
* Fix SimpleNER component
* Update train_from_config
* Add biluo_to_iob helper
* Add IOB layer
* Add IOBTagger model
* Update biluo layer
* Update SimpleNER tagger
* Update BILUO
* Read random seed in train-from-config
* Update use of normal_init
* Fix normalization of gradient in SimpleNER
* Update IOBTagger
* Remove print
* Tweak masking in BILUO
* Add dropout in SimpleNER
* Update thinc
* Tidy up simple_ner
* Fix biluo model
* Unhack train-from-config
* Update setup.cfg and requirements
* Add tb_framework.py for parser model
* Try to avoid memory leak in BILUO
* Move ParserModel into spacy.ml, avoid need for subclass.
* Use updated parser model
* Remove incorrect call to model.initializre in PrecomputableAffine
* Update parser model
* Avoid divide by zero in tagger
* Add extra dropout layer in tagger
* Refine minibatch_by_words function to avoid oom
* Fix parser model after refactor
* Try to avoid div-by-zero in SimpleNER
* Fix infinite loop in minibatch_by_words
* Use SequenceCategoricalCrossentropy in Tagger
* Fix parser model when hidden layer
* Remove extra dropout from tagger
* Add extra nan check in tagger
* Fix thinc version
* Update tests and imports
* Fix test
* Update test
* Update tests
* Fix tests
* Fix test
Co-authored-by: Ines Montani <ines@ines.io>
* 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>
Check that row is within bounds for the vector data array when adding a
vector.
Don't add vectors with rank OOV_RANK in `init-model` (change is due to
shift from OOV as 0 to OOV as OOV_RANK).
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.
* Initialize lower flag explicitly
* Handle whitespace words from GoldParse correctly when creating raw
text with orth variants
* Return the text with original casing if anything goes wrong
* `debug-data`: determine coverage of provided vectors
* `evaluate`: support `blank:lg` model to make it possible to just evaluate
tokenization
* `init-model`: add option to truncate vectors to N most frequent vectors
from word2vec file
* `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
* simplify creation of KB by skipping dim reduction
* small fixes to train EL example script
* add KB creation and NEL training example scripts to example section
* update descriptions of example scripts in the documentation
* moving wiki_entity_linking folder from bin to projects
* remove test for wiki NEL functionality that is being moved
Reconstruction of the original PR #4697 by @MiniLau.
Removes unused `SENT_END` symbol and `IS_SENT_END` from `Matcher` schema
because the Matcher is only going to be able to support `IS_SENT_START`.
