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.
* 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* remove outdated comment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).
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.
* 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`.
Improve GoldParse NER alignment by including all cases where the start
and end of the NER span can be aligned, regardless of internal
tokenization differences.
To do this, convert BILUO tags to character offsets, check start/end
alignment with `doc.char_span()`, and assign the BILUO tags for the
aligned spans. Alignment for `O/-` tags is handled through the
one-to-one and multi alignments.
* 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
* Matcher support for Span, as well as Doc #5056
* Removes an import unused
* Signed contributors agreement
* Code optimization and better test
* Add error message for bad Matcher call argument
* Fix merging
* Use max(uint64) for OOV lexeme rank
* Add test for default OOV rank
* Revert back to thinc==7.4.0
Requiring the updated version of thinc was unnecessary.
* Define OOV_RANK in one place
Define OOV_RANK in one place in `util`.
* Fix formatting [ci skip]
* Switch to external definitions of max(uint64)
Switch to external defintions of max(uint64) and confirm that they are
equal.
* Add Doc init from list of words and text
Add an option to initialize a `Doc` from a text and list of words where
the words may or may not include all whitespace tokens. If the text and
words are mismatched, raise an error.
* Fix error code
* Remove all whitespace before aligning words/text
* Move words/text init to util function
* Update error message
* Rename to get_words_and_spaces
* Fix formatting
* Add pos and morph scoring to Scorer
Add pos, morph, and morph_per_type to `Scorer`. Report pos and morph
accuracy in `spacy evaluate`.
* Update morphologizer for v3
* switch to tagger-based morphologizer
* use `spacy.HashCharEmbedCNN` for morphologizer defaults
* add `Doc.is_morphed` flag
* Add morphologizer to train CLI
* Add basic morphologizer pipeline tests
* Add simple morphologizer training example
* Remove subword_features from CharEmbed models
Remove `subword_features` argument from `spacy.HashCharEmbedCNN.v1` and
`spacy.HashCharEmbedBiLSTM.v1` since in these cases `subword_features`
is always `False`.
* Rename setting in morphologizer example
Use `with_pos_tags` instead of `without_pos_tags`.
* Fix kwargs for spacy.HashCharEmbedBiLSTM.v1
* Remove defaults for spacy.HashCharEmbedBiLSTM.v1
Remove default `nM/nC` for `spacy.HashCharEmbedBiLSTM.v1`.
* Set random seed for textcat overfitting test
* bring back default build_text_classifier method
* remove _set_dims_ hack in favor of proper dim inference
* add tok2vec initialize to unit test
* small fixes
* add unit test for various textcat config settings
* logistic output layer does not have nO
* fix window_size setting
* proper fix
* fix W initialization
* Update textcat training example
* Use ml_datasets
* Convert training data to `Example` format
* Use `n_texts` to set proportionate dev size
* fix _init renaming on latest thinc
* avoid setting a non-existing dim
* update to thinc==8.0.0a2
* add BOW and CNN defaults for easy testing
* various experiments with train_textcat script, fix softmax activation in textcat bow
* allow textcat train script to work on other datasets as well
* have dataset as a parameter
* train textcat from config, with example config
* add config for training textcat
* formatting
* fix exclusive_classes
* fixing BOW for GPU
* bump thinc to 8.0.0a3 (not published yet so CI will fail)
* add in link_vectors_to_models which got deleted
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Check whether doc is instantiated
When creating docs to pair with gold parses, modify test to check
whether a doc is unset rather than whether it contains tokens.
* Restore test of evaluate on an empty doc
* Set a minimal gold.orig for the scorer
Without a minimal gold.orig the scorer can't evaluate empty docs. This
is the v3 equivalent of #4925.
* Modify Vector.resize to work with cupy
Modify `Vectors.resize` to work with cupy. Modify behavior when resizing
to a different vector dimension so that individual vectors are truncated
or extended with zeros instead of having the original values filled into
the new shape without regard for the original axes.
* Update spacy/tests/vocab_vectors/test_vectors.py
Co-Authored-By: Matthew Honnibal <honnibal+gh@gmail.com>
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
UD_Danish-DDT has (as far as I can tell) hallucinated periods after
abbreviations, so the changes are an artifact of the corpus and not due
to anything meaningful about Danish tokenization.
* Revert changes to priority of `token_match` so that it has priority
over all other tokenizer patterns
* Add lookahead and potentially slow lookbehind back to the default URL
pattern
* Expand character classes in URL pattern to improve matching around
lookaheads and lookbehinds related to #4882
* Revert changes to Hungarian tokenizer
* Revert (xfail) several URL tests to their status before #4374
* Update `tokenizer.explain()` and docs accordingly
* avoid changing original config
* fix elif structure, batch with just int crashes otherwise
* tok2vec example with doc2feats, encode and embed architectures
* further clean up MultiHashEmbed
* further generalize Tok2Vec to work with extract-embed-encode parts
* avoid initializing the charembed layer with Docs (for now ?)
* small fixes for bilstm config (still does not run)
* rename to core layer
* move new configs
* walk model to set nI instead of using core ref
* fix senter overfitting test to be more similar to the training data (avoid flakey behaviour)
* merge_entities sets the vector in the vocab for the merged token
* add unit test
* import unicode_literals
* move code to _merge function
* only set vector if vocab has non-zero vectors
* Update sentence recognizer
* rename `sentrec` to `senter`
* use `spacy.HashEmbedCNN.v1` by default
* update to follow `Tagger` modifications
* remove component methods that can be inherited from `Tagger`
* add simple initialization and overfitting pipeline tests
* Update serialization test for senter
* Improve token head verification
Improve the verification for valid token heads when heads are set:
* in `Token.head`: heads come from the same document
* in `Doc.from_array()`: head indices are within the bounds of the
document
* Improve error message
* fix grad_clip naming
* cleaning up pretrained_vectors out of cfg
* further refactoring Model init's
* move Model building out of pipes
* further refactor to require a model config when creating a pipe
* small fixes
* making cfg in nn_parser more consistent
* fixing nr_class for parser
* fixing nn_parser's nO
* fix printing of loss
* architectures in own file per type, consistent naming
* convenience methods default_tagger_config and default_tok2vec_config
* let create_pipe access default config if available for that component
* default_parser_config
* move defaults to separate folder
* allow reading nlp from package or dir with argument 'name'
* architecture spacy.VocabVectors.v1 to read static vectors from file
* cleanup
* default configs for nel, textcat, morphologizer, tensorizer
* fix imports
* fixing unit tests
* fixes and clean up
* fixing defaults, nO, fix unit tests
* restore parser IO
* fix IO
* 'fix' serialization test
* add *.cfg to manifest
* fix example configs with additional arguments
* replace Morpohologizer with Tagger
* add IO bit when testing overfitting of tagger (currently failing)
* fix IO - don't initialize when reading from disk
* expand overfitting tests to also check IO goes OK
* remove dropout from HashEmbed to fix Tagger performance
* add defaults for sentrec
* update thinc
* always pass a Model instance to a Pipe
* fix piped_added statement
* remove obsolete W029
* remove obsolete errors
* restore byte checking tests (work again)
* clean up test
* further test cleanup
* convert from config to Model in create_pipe
* bring back error when component is not initialized
* cleanup
* remove calls for nlp2.begin_training
* use thinc.api in imports
* allow setting charembed's nM and nC
* fix for hardcoded nM/nC + unit test
* formatting fixes
* trigger build
* add lemma option to displacy 'dep' visualiser
* more compact list comprehension
* add option to doc
* fix test and add lemmas to util.get_doc
* fix capital
* remove lemma from get_doc
* cleanup
* Sync Span __eq__ and __hash__
Use the same tuple for `__eq__` and `__hash__`, including all attributes
except `vector` and `vector_norm`.
* Update entity comparison in tests
Update `assert_docs_equal()` test util to compare `Span` properties for
ents rather than `Span` objects.
Modify flag settings so that `DEP` is not sufficient to set `is_parsed`
and only run `set_children_from_heads()` if `HEAD` is provided.
Then the combination `[SENT_START, DEP]` will set deps and not clobber
sent starts with a lot of one-word sentences.
* don't split on a colon. Colon is used to attach suffixes for abbreviations
* tokenize on any of LIST_HYPHENS (except a single hyphen), not just on --
* simplify infix rules by merging similar rules
* Add convert CLI option to merge CoNLL-U subtokens
Add `-T` option to convert CLI that merges CoNLL-U subtokens into one
token in the converted data. Each CoNLL-U sentence is read into a `Doc`
and the `Retokenizer` is used to merge subtokens with features as
follows:
* `orth` is the merged token orth (should correspond to raw text and `#
text`)
* `tag` is all subtoken tags concatenated with `_`, e.g. `ADP_DET`
* `pos` is the POS of the syntactic root of the span (as determined by
the Retokenizer)
* `morph` is all morphological features merged
* `lemma` is all subtoken lemmas concatenated with ` `, e.g. `de o`
* with `-m` all morphological features are combined with the tag using
the separator `__`, e.g.
`ADP_DET__Definite=Def|Gender=Masc|Number=Sing|PronType=Art`
* `dep` is the dependency relation for the syntactic root of the span
(as determined by the Retokenizer)
Concatenated tags will be mapped to the UD POS of the syntactic root
(e.g., `ADP`) and the morphological features will be the combined
features.
In many cases, the original UD subtokens can be reconstructed from the
available features given a language-specific lookup table, e.g.,
Portuguese `do / ADP_DET /
Definite=Def|Gender=Masc|Number=Sing|PronType=Art` is `de / ADP`, `o /
DET / Definite=Def|Gender=Masc|Number=Sing|PronType=Art` or lookup rules
for forms containing open class words like Spanish `hablarlo / VERB_PRON
/
Case=Acc|Gender=Masc|Number=Sing|Person=3|PrepCase=Npr|PronType=Prs|VerbForm=Inf`.
* Clean up imports
* Add load_from_config function
* Add train_from_config script
* Merge configs and expose via spacy.config
* Fix script
* Suggest create_evaluation_callback
* Hard-code for NER
* Fix errors
* Register command
* Add TODO
* Update train-from-config todos
* Fix imports
* Allow delayed setting of parser model nr_class
* Get train-from-config working
* Tidy up and fix scores and printing
* Hide traceback if cancelled
* Fix weighted score formatting
* Fix score formatting
* Make output_path optional
* Add Tok2Vec component
* Tidy up and add tok2vec_tensors
* Add option to copy docs in nlp.update
* Copy docs in nlp.update
* Adjust nlp.update() for set_annotations
* Don't shuffle pipes in nlp.update, decruft
* Support set_annotations arg in component update
* Support set_annotations in parser update
* Add get_gradients method
* Add get_gradients to parser
* Update errors.py
* Fix problems caused by merge
* Add _link_components method in nlp
* Add concept of 'listeners' and ControlledModel
* Support optional attributes arg in ControlledModel
* Try having tok2vec component in pipeline
* Fix tok2vec component
* Fix config
* Fix tok2vec
* Update for Example
* Update for Example
* Update config
* Add eg2doc util
* Update and add schemas/types
* Update schemas
* Fix nlp.update
* Fix tagger
* Remove hacks from train-from-config
* Remove hard-coded config str
* Calculate loss in tok2vec component
* Tidy up and use function signatures instead of models
* Support union types for registry models
* Minor cleaning in Language.update
* Make ControlledModel specifically Tok2VecListener
* Fix train_from_config
* Fix tok2vec
* Tidy up
* Add function for bilstm tok2vec
* Fix type
* Fix syntax
* Fix pytorch optimizer
* Add example configs
* Update for thinc describe changes
* Update for Thinc changes
* Update for dropout/sgd changes
* Update for dropout/sgd changes
* Unhack gradient update
* Work on refactoring _ml
* Remove _ml.py module
* WIP upgrade cli scripts for thinc
* Move some _ml stuff to util
* Import link_vectors from util
* Update train_from_config
* Import from util
* Import from util
* Temporarily add ml.component_models module
* Move ml methods
* Move typedefs
* Update load vectors
* Update gitignore
* Move imports
* Add PrecomputableAffine
* Fix imports
* Fix imports
* Fix imports
* Fix missing imports
* Update CLI scripts
* Update spacy.language
* Add stubs for building the models
* Update model definition
* Update create_default_optimizer
* Fix import
* Fix comment
* Update imports in tests
* Update imports in spacy.cli
* Fix import
* fix obsolete thinc imports
* update srsly pin
* from thinc to ml_datasets for example data such as imdb
* update ml_datasets pin
* using STATE.vectors
* small fix
* fix Sentencizer.pipe
* black formatting
* rename Affine to Linear as in thinc
* set validate explicitely to True
* rename with_square_sequences to with_list2padded
* rename with_flatten to with_list2array
* chaining layernorm
* small fixes
* revert Optimizer import
* build_nel_encoder with new thinc style
* fixes using model's get and set methods
* Tok2Vec in component models, various fixes
* fix up legacy tok2vec code
* add model initialize calls
* add in build_tagger_model
* small fixes
* setting model dims
* fixes for ParserModel
* various small fixes
* initialize thinc Models
* fixes
* consistent naming of window_size
* fixes, removing set_dropout
* work around Iterable issue
* remove legacy tok2vec
* util fix
* fix forward function of tok2vec listener
* more fixes
* trying to fix PrecomputableAffine (not succesful yet)
* alloc instead of allocate
* add morphologizer
* rename residual
* rename fixes
* Fix predict function
* Update parser and parser model
* fixing few more tests
* Fix precomputable affine
* Update component model
* Update parser model
* Move backprop padding to own function, for test
* Update test
* Fix p. affine
* Update NEL
* build_bow_text_classifier and extract_ngrams
* Fix parser init
* Fix test add label
* add build_simple_cnn_text_classifier
* Fix parser init
* Set gpu off by default in example
* Fix tok2vec listener
* Fix parser model
* Small fixes
* small fix for PyTorchLSTM parameters
* revert my_compounding hack (iterable fixed now)
* fix biLSTM
* Fix uniqued
* PyTorchRNNWrapper fix
* small fixes
* use helper function to calculate cosine loss
* small fixes for build_simple_cnn_text_classifier
* putting dropout default at 0.0 to ensure the layer gets built
* using thinc util's set_dropout_rate
* moving layer normalization inside of maxout definition to optimize dropout
* temp debugging in NEL
* fixed NEL model by using init defaults !
* fixing after set_dropout_rate refactor
* proper fix
* fix test_update_doc after refactoring optimizers in thinc
* Add CharacterEmbed layer
* Construct tagger Model
* Add missing import
* Remove unused stuff
* Work on textcat
* fix test (again :)) after optimizer refactor
* fixes to allow reading Tagger from_disk without overwriting dimensions
* don't build the tok2vec prematuraly
* fix CharachterEmbed init
* CharacterEmbed fixes
* Fix CharacterEmbed architecture
* fix imports
* renames from latest thinc update
* one more rename
* add initialize calls where appropriate
* fix parser initialization
* Update Thinc version
* Fix errors, auto-format and tidy up imports
* Fix validation
* fix if bias is cupy array
* revert for now
* ensure it's a numpy array before running bp in ParserStepModel
* no reason to call require_gpu twice
* use CupyOps.to_numpy instead of cupy directly
* fix initialize of ParserModel
* remove unnecessary import
* fixes for CosineDistance
* fix device renaming
* use refactored loss functions (Thinc PR 251)
* overfitting test for tagger
* experimental settings for the tagger: avoid zero-init and subword normalization
* clean up tagger overfitting test
* use previous default value for nP
* remove toy config
* bringing layernorm back (had a bug - fixed in thinc)
* revert setting nP explicitly
* remove setting default in constructor
* restore values as they used to be
* add overfitting test for NER
* add overfitting test for dep parser
* add overfitting test for textcat
* fixing init for linear (previously affine)
* larger eps window for textcat
* ensure doc is not None
* Require newer thinc
* Make float check vaguer
* Slop the textcat overfit test more
* Fix textcat test
* Fix exclusive classes for textcat
* fix after renaming of alloc methods
* fixing renames and mandatory arguments (staticvectors WIP)
* upgrade to thinc==8.0.0.dev3
* refer to vocab.vectors directly instead of its name
* rename alpha to learn_rate
* adding hashembed and staticvectors dropout
* upgrade to thinc 8.0.0.dev4
* add name back to avoid warning W020
* thinc dev4
* update srsly
* using thinc 8.0.0a0 !
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
Co-authored-by: Ines Montani <ines@ines.io>
* Restructure tag maps for MorphAnalysis changes
Prepare tag maps for upcoming MorphAnalysis changes that allow
arbritrary features.
* Use default tag map rather than duplicating for ca / uk / vi
* Import tag map into defaults for ga
* Modify tag maps so all morphological fields and features are strings
* Move features from `"Other"` to the top level
* Rewrite tuples as strings separated by `","`
* Rewrite morph symbols for fr lemmatizer as strings
* Export MorphAnalysis under spacy.tokens
* Modify morphology to support arbitrary features
Modify `Morphology` and `MorphAnalysis` so that arbitrary features are
supported.
* Modify `MorphAnalysisC` so that it can support arbitrary features and
multiple values per field. `MorphAnalysisC` is redesigned to contain:
* key: hash of UD FEATS string of morphological features
* array of `MorphFeatureC` structs that each contain a hash of `Field`
and `Field=Value` for a given morphological feature, which makes it
possible to:
* find features by field
* represent multiple values for a given field
* `get_field()` is renamed to `get_by_field()` and is no longer `nogil`.
Instead a new helper function `get_n_by_field()` is `nogil` and returns
`n` features by field.
* `MorphAnalysis.get()` returns all possible values for a field as a
list of individual features such as `["Tense=Pres", "Tense=Past"]`.
* `MorphAnalysis`'s `str()` and `repr()` are the UD FEATS string.
* `Morphology.feats_to_dict()` converts a UD FEATS string to a dict
where:
* Each field has one entry in the dict
* Multiple values remain separated by a separator in the value string
* `Token.morph_` returns the UD FEATS string and you can set
`Token.morph_` with a UD FEATS string or with a tag map dict.
* Modify get_by_field to use np.ndarray
Modify `get_by_field()` to use np.ndarray. Remove `max_results` from
`get_n_by_field()` and always iterate over all the fields.
* Rewrite without MorphFeatureC
* Add shortcut for existing feats strings as keys
Add shortcut for existing feats strings as keys in `Morphology.add()`.
* Check for '_' as empty analysis when adding morphs
* Extend helper converters in Morphology
Add and extend helper converters that convert and normalize between:
* UD FEATS strings (`"Case=dat,gen|Number=sing"`)
* per-field dict of feats (`{"Case": "dat,gen", "Number": "sing"}`)
* list of individual features (`["Case=dat", "Case=gen",
"Number=sing"]`)
All converters sort fields and values where applicable.
* Fix ent_ids and labels properties when id attribute used in patterns
* use set for labels
* sort end_ids for comparison in entity_ruler tests
* fixing entity_ruler ent_ids test
* add to set
* Mark most Hungarian tokenizer test cases as slow
Mark most Hungarian tokenizer test cases as slow to reduce the runtime
of the test suite in ordinary usage:
* for normal tests: run default tests plus 10% of the detailed tests
* for slow tests: run all tests
* Rework to mark individual tests as slow
* match domains longer than `hostname.domain.tld` like `www.foo.co.uk`
* expand allowed characters in domain names while only matching
lowercase TLDs so that "this.That" isn't matched as a URL and can be
split on the period as an infix (relevant for at least English, German,
and Tatar)
* expand serialization test for custom token attribute
* add failing test for issue 4849
* define ENT_ID as attr and use in doc serialization
* fix few typos
* Adding Support for Yoruba
* test text
* Updated test string.
* Fixing encoding declaration.
* Adding encoding to stop_words.py
* Added contributor agreement and removed iranlowo.
* Added removed test files and removed iranlowo to keep project bare.
* Returned CONTRIBUTING.md to default state.
* Added delted conftest entries
* Tidy up and auto-format
* Revert CONTRIBUTING.md
Co-authored-by: Ines Montani <ines@ines.io>
Instead of a hard-coded NER tag simplification function that was only
intended for NorNE, map NER tags in CoNLL-U converter using a dict
provided as JSON as a command-line option.
Map NER entity types or new tag or to "" for 'O', e.g.:
```
{"PER": "PERSON", "BAD": ""}
=>
B-PER -> B-PERSON
B-BAD -> O
```
* Include Doc.cats in to_bytes()
* Include Doc.cats in DocBin serialization
* Add tests for serialization of cats
Test serialization of cats for Doc and DocBin.
* Enable lex_attrs on Finnish
* Copy the Danish tokenizer rules to Finnish
Specifically, don't break hyphenated compound words
* Contributor agreement
* A new file for Finnish tokenizer rules instead of including the Danish ones
- added some tests for tokenization issues
- fixed some issues with tokenization of words with hyphen infix
- rewrote the "tokenizer_exceptions.py" file (stemming from the German version)
* Add sent_starts to GoldParse
* Add SentTagger pipeline component
Add `SentTagger` pipeline component as a subclass of `Tagger`.
* Model reduces default parameters from `Tagger` to be small and fast
* Hard-coded set of two labels:
* S (1): token at beginning of sentence
* I (0): all other sentence positions
* Sets `token.sent_start` values
* Add sentence segmentation to Scorer
Report `sent_p/r/f` for sentence boundaries, which may be provided by
various pipeline components.
* Add sentence segmentation to CLI evaluate
* Add senttagger metrics/scoring to train CLI
* Rename SentTagger to SentenceRecognizer
* Add SentenceRecognizer to spacy.pipes imports
* Add SentenceRecognizer serialization test
* Shorten component name to sentrec
* Remove duplicates from train CLI output metrics
* Restructure Sentencizer to follow Pipe API
Restructure Sentencizer to follow Pipe API so that it can be scored with
`nlp.evaluate()`.
* Add Sentencizer pipe() test
Replace old gold alignment that allowed for some noise in the alignment between raw and orth with the new simpler alignment that requires that the raw and orth strings are identical except for whitespace and capitalization.
* Replace old alignment with new alignment, removing `_align.pyx` and
its tests
* Remove all quote normalizations
* Enable test for new align
* Modify test case for quote normalization
* Switch to train_dataset() function in train CLI
* Fixes for pipe() methods in pipeline components
* Don't clobber `examples` variable with `as_example` in pipe() methods
* Remove unnecessary traversals of `examples`
* Update Parser.pipe() for Examples
* Add `as_examples` kwarg to `pipe()` with implementation to return
`Example`s
* Accept `Doc` or `Example` in `pipe()` with `_get_doc()` (copied from
`Pipe`)
* Fixes to Example implementation in spacy.gold
* Move `make_projective` from an attribute of Example to an argument of
`Example.get_gold_parses()`
* Head of 0 are not treated as unset
* Unset heads are set to self rather than `None` (which causes problems
while projectivizing)
* Check for `Doc` (not just not `None`) when creating GoldParses for
pre-merged example
* Don't clobber `examples` variable in `iter_gold_docs()`
* Add/modify gold tests for handling projectivity
* In JSON roundtrip compare results from `dev_dataset` rather than
`train_dataset` to avoid projectivization (and other potential
modifications)
* Add test for projective train vs. nonprojective dev versions of the
same `Doc`
* Handle ignore_misaligned as arg rather than attr
Move `ignore_misaligned` from an attribute of `Example` to an argument
to `Example.get_gold_parses()`, which makes it parallel to
`make_projective`.
Add test with old and new align that checks whether `ignore_misaligned`
errors are raised as expected (only for new align).
* Remove unused attrs from gold.pxd
Remove `ignore_misaligned` and `make_projective` from `gold.pxd`
* Restructure Example with merged sents as default
An `Example` now includes a single `TokenAnnotation` that includes all
the information from one `Doc` (=JSON `paragraph`). If required, the
individual sentences can be returned as a list of examples with
`Example.split_sents()` with no raw text available.
* Input/output a single `Example.token_annotation`
* Add `sent_starts` to `TokenAnnotation` to handle sentence boundaries
* Replace `Example.merge_sents()` with `Example.split_sents()`
* Modify components to use a single `Example.token_annotation`
* Pipeline components
* conllu2json converter
* Rework/rename `add_token_annotation()` and `add_doc_annotation()` to
`set_token_annotation()` and `set_doc_annotation()`, functions that set
rather then appending/extending.
* Rename `morphology` to `morphs` in `TokenAnnotation` and `GoldParse`
* Add getters to `TokenAnnotation` to supply default values when a given
attribute is not available
* `Example.get_gold_parses()` in `spacy.gold._make_golds()` is only
applied on single examples, so the `GoldParse` is returned saved in the
provided `Example` rather than creating a new `Example` with no other
internal annotation
* Update tests for API changes and `merge_sents()` vs. `split_sents()`
* Refer to Example.goldparse in iter_gold_docs()
Use `Example.goldparse` in `iter_gold_docs()` instead of `Example.gold`
because a `None` `GoldParse` is generated with ignore_misaligned and
generating it on-the-fly can raise an unwanted AlignmentError
* Fix make_orth_variants()
Fix bug in make_orth_variants() related to conversion from multiple to
one TokenAnnotation per Example.
* Add basic test for make_orth_variants()
* Replace try/except with conditionals
* Replace default morph value with set
Iterate over lr_edges until all heads are within the current sentence.
Instead of iterating over them for a fixed number of iterations, check
whether the sentence boundaries are correct for the heads and stop when
all are correct. Stop after a maximum of 10 iterations, providing a
warning in this case since the sentence boundaries may not be correct.
* Switch to train_dataset() function in train CLI
* Fixes for pipe() methods in pipeline components
* Don't clobber `examples` variable with `as_example` in pipe() methods
* Remove unnecessary traversals of `examples`
* Update Parser.pipe() for Examples
* Add `as_examples` kwarg to `pipe()` with implementation to return
`Example`s
* Accept `Doc` or `Example` in `pipe()` with `_get_doc()` (copied from
`Pipe`)
* Fixes to Example implementation in spacy.gold
* Move `make_projective` from an attribute of Example to an argument of
`Example.get_gold_parses()`
* Head of 0 are not treated as unset
* Unset heads are set to self rather than `None` (which causes problems
while projectivizing)
* Check for `Doc` (not just not `None`) when creating GoldParses for
pre-merged example
* Don't clobber `examples` variable in `iter_gold_docs()`
* Add/modify gold tests for handling projectivity
* In JSON roundtrip compare results from `dev_dataset` rather than
`train_dataset` to avoid projectivization (and other potential
modifications)
* Add test for projective train vs. nonprojective dev versions of the
same `Doc`
* Handle ignore_misaligned as arg rather than attr
Move `ignore_misaligned` from an attribute of `Example` to an argument
to `Example.get_gold_parses()`, which makes it parallel to
`make_projective`.
Add test with old and new align that checks whether `ignore_misaligned`
errors are raised as expected (only for new align).
* Remove unused attrs from gold.pxd
Remove `ignore_misaligned` and `make_projective` from `gold.pxd`
* Refer to Example.goldparse in iter_gold_docs()
Use `Example.goldparse` in `iter_gold_docs()` instead of `Example.gold`
because a `None` `GoldParse` is generated with ignore_misaligned and
generating it on-the-fly can raise an unwanted AlignmentError
* Update test for ignore_misaligned
* Switch from mecab-python3 to fugashi
mecab-python3 has been the best MeCab binding for a long time but it's
not very actively maintained, and since it's based on old SWIG code
distributed with MeCab there's a limit to how effectively it can be
maintained.
Fugashi is a new Cython-based MeCab wrapper I wrote. Since it's not
based on the old SWIG code it's easier to keep it current and make small
deviations from the MeCab C/C++ API where that makes sense.
* Change mecab-python3 to fugashi in setup.cfg
* Change "mecab tags" to "unidic tags"
The tags come from MeCab, but the tag schema is specified by Unidic, so
it's more proper to refer to it that way.
* Update conftest
* Add fugashi link to external deps list for Japanese
* Detect more empty matches in tokenizer.explain()
* Include a few languages in explain non-slow tests
Mark a few languages in tokenizer.explain() tests as not slow so they're
run by default.
* Expose tokenizer rules as a property
Expose the tokenizer rules property in the same way as the other core
properties. (The cache resetting is overkill, but consistent with
`from_bytes` for now.)
Add tests and update Tokenizer API docs.
* Update Hungarian punctuation to remove empty string
Update Hungarian punctuation definitions so that `_units` does not match
an empty string.
* Use _load_special_tokenization consistently
Use `_load_special_tokenization()` and have it to handle `None` checks.
* Fix precedence of `token_match` vs. special cases
Remove `token_match` check from `_split_affixes()` so that special cases
have precedence over `token_match`. `token_match` is checked only before
infixes are split.
* Add `make_debug_doc()` to the Tokenizer
Add `make_debug_doc()` to the Tokenizer as a working implementation of
the pseudo-code in the docs.
Add a test (marked as slow) that checks that `nlp.tokenizer()` and
`nlp.tokenizer.make_debug_doc()` return the same non-whitespace tokens
for all languages that have `examples.sentences` that can be imported.
* Update tokenization usage docs
Update pseudo-code and algorithm description to correspond to
`nlp.tokenizer.make_debug_doc()` with example debugging usage.
Add more examples for customizing tokenizers while preserving the
existing defaults.
Minor edits / clarifications.
* Revert "Update Hungarian punctuation to remove empty string"
This reverts commit f0a577f7a5.
* Rework `make_debug_doc()` as `explain()`
Rework `make_debug_doc()` as `explain()`, which returns a list of
`(pattern_string, token_string)` tuples rather than a non-standard
`Doc`. Update docs and tests accordingly, leaving the visualization for
future work.
* Handle cases with bad tokenizer patterns
Detect when tokenizer patterns match empty prefixes and suffixes so that
`explain()` does not hang on bad patterns.
* Remove unused displacy image
* Add tokenizer.explain() to usage docs
