* rename Pipe to TrainablePipe
* split functionality between Pipe and TrainablePipe
* remove unnecessary methods from certain components
* cleanup
* hasattr(component, "pipe") should be sufficient again
* remove serialization and vocab/cfg from Pipe
* unify _ensure_examples and validate_examples
* small fixes
* hasattr checks for self.cfg and self.vocab
* make is_resizable and is_trainable properties
* serialize strings.json instead of vocab
* fix KB IO + tests
* fix typos
* more typos
* _added_strings as a set
* few more tests specifically for _added_strings field
* bump to 3.0.0a36
* Refactor Token morph setting
* Remove `Token.morph_`
* Add `Token.set_morph()`
* `0` resets `token.c.morph` to unset
* Any other values are passed to `Morphology.add`
* Add token.morph setter to set from MorphAnalysis
* NEL: read sentences and ents from reference
* fiddling with sent_start annotations
* add KB serialization test
* KB write additional file with strings.json
* score_links function to calculate NEL P/R/F
* formatting
* documentation
* 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>
* ensure Language passes on valid examples for initialization
* fix tagger model initialization
* check for valid get_examples across components
* assume labels were added before begin_training
* fix senter initialization
* fix morphologizer initialization
* use methods to check arguments
* test textcat init, requires thinc>=8.0.0a31
* fix tok2vec init
* fix entity linker init
* use islice
* fix simple NER
* cleanup debug model
* fix assert statements
* fix tests
* throw error when adding a label if the output layer can't be resized anymore
* fix test
* add failing test for simple_ner
* UX improvements
* morphologizer UX
* assume begin_training gets a representative set and processes the labels
* remove assumptions for output of untrained NER model
* restore test for original purpose
* Prevent Tagger model init with 0 labels
Raise an error before trying to initialize a tagger model with 0 labels.
* Add dummy tagger label for test
* Remove tagless tagger model initializiation
* Fix error number after merge
* Add dummy tagger label to test
* Fix formatting
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
Sort the returned matches by rule order (the `match_id`) so that the
rules are applied in the order they were added. This is necessary, for
instance, if the `AttributeRuler` is used for the tag map and later
rules require POS tags.
Serialize `AttributeRuler.patterns` instead of the individual lists to
simplify the serialized and so that patterns are reloaded exactly as
they were originally provided (preserving `_attrs_unnormed`).
* Add AttributeRuler.score
Add scoring for TAG / POS / MORPH / LEMMA if these are present in the
assigned token attributes.
Add default score weights (that don't really make a lot of sense) so
that the scores are in the default config in some form.
* Update docs
* candidate generator as separate part of EL config
* update comment
* ent instead of str as input for candidate generation
* Span instead of str: correct type indication
* fix types
* unit test to create new candidate generator
* fix replace_pipe argument passing
* move error message, general cleanup
* add vocab back to KB constructor
* provide KB as callable from Vocab arg
* rename to kb_loader, fix KB serialization as part of the EL pipe
* fix typo
* reformatting
* cleanup
* fix comment
* fix wrongly duplicated code from merge conflict
* rename dump to to_disk
* from_disk instead of load_bulk
* update test after recent removal of set_morphology in tagger
* remove old doc
* 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
* Allow adding pipeline components from source model
* Config: name -> component
* Improve error messages
* Fix error and test
* Add frozen components and exclude logic
* Remove exclude from Language.evaluate
* Init sourced components with current vocab
* Fix error codes
* Add AttributeRuler for token attribute exceptions
Add the `AttributeRuler` to handle exceptions for token-level
attributes. The `AttributeRuler` uses `Matcher` patterns to identify
target spans and applies the specified attributes to the token at the
provided index in the matched span. A negative index can be used to
index from the end of the matched span. The retokenizer is used to
"merge" the individual tokens and assign them the provided attributes.
Helper functions can import existing tag maps and morph rules to the
corresponding `Matcher` patterns.
There is an additional minor bug fix for `MORPH` attributes in the
retokenizer to correctly normalize the values and to handle `MORPH`
alongside `_` in an attrs dict.
* Fix default name
* Update name in error message
* Extend AttributeRuler functionality
* Add option to initialize with a dict of AttributeRuler patterns
* Instead of silently discarding overlapping matches (the default
behavior for the retokenizer if only the attrs differ), split the
matches into disjoint sets and retokenize each set separately. This
allows, for instance, one pattern to set the POS and another pattern to
set the lemma. (If two matches modify the same attribute, it looks like
the attrs are applied in the order they were added, but it may not be
deterministic?)
* Improve types
* Sort spans before processing
* Fix index boundaries in Span
* Refactor retokenizer to separate attrs methods
Add top-level `normalize_token_attrs` and `set_token_attrs` methods.
* Update AttributeRuler to use refactored methods
Update `AttributeRuler` to replace use of full retokenizer with only the
relevant methods for normalizing and setting attributes for a single
token.
* Update spacy/pipeline/attributeruler.py
Co-authored-by: Ines Montani <ines@ines.io>
* Make API more similar to EntityRuler
* Add `AttributeRuler.add_patterns` to add patterns from a list of dicts
* Return list of dicts as property `AttributeRuler.patterns`
* Make attrs_unnormed private
* Add test loading patterns from assets
* Revert "Fix index boundaries in Span"
This reverts commit 8f8a5c3386.
* Add Span index boundary checks (#5861)
* Add Span index boundary checks
* Return Span-specific IndexError in all cases
* Simplify and fix if/else
Co-authored-by: Ines Montani <ines@ines.io>
Add and update `score` methods, provided `scores`, and default weights
`default_score_weights` for pipeline components.
* `scores` provides all top-level keys returned by `score` (merely informative, similar to `assigns`).
* `default_score_weights` provides the default weights for a default config.
* The keys from `default_score_weights` determine which values will be
shown in the `spacy train` output, so keys with weight `0.0` will be
displayed but not counted toward the overall score.
* Provide top-level score as `attr_score`
* Provide a description of the score as `attr_score_desc`
* Provide all potential scores keys, setting unused keys to `None`
* Update CLI evaluate accordingly
* Refactor the Scorer to improve flexibility
Refactor the `Scorer` to improve flexibility for arbitrary pipeline
components.
* Individual pipeline components provide their own `evaluate` methods
that score a list of `Example`s and return a dictionary of scores
* `Scorer` is initialized either:
* with a provided pipeline containing components to be scored
* with a default pipeline containing the built-in statistical
components (senter, tagger, morphologizer, parser, ner)
* `Scorer.score` evaluates a list of `Example`s and returns a dictionary
of scores referring to the scores provided by the components in the
pipeline
Significant differences:
* `tags_acc` is renamed to `tag_acc` to be consistent with `token_acc`
and the new `morph_acc`, `pos_acc`, and `lemma_acc`
* Scoring is no longer cumulative: `Scorer.score` scores a list of
examples rather than a single example and does not retain any state
about previously scored examples
* PRF values in the returned scores are no longer multiplied by 100
* Add kwargs to Morphologizer.evaluate
* Create generalized scoring methods in Scorer
* Generalized static scoring methods are added to `Scorer`
* Methods require an attribute (either on Token or Doc) that is
used to key the returned scores
Naming differences:
* `uas`, `las`, and `las_per_type` in the scores dict are renamed to
`dep_uas`, `dep_las`, and `dep_las_per_type`
Scoring differences:
* `Doc.sents` is now scored as spans rather than on sentence-initial
token positions so that `Doc.sents` and `Doc.ents` can be scored with
the same method (this lowers scores since a single incorrect sentence
start results in two incorrect spans)
* Simplify / extend hasattr check for eval method
* Add hasattr check to tokenizer scoring
* Simplify to hasattr check for component scoring
* Reset Example alignment if docs are set
Reset the Example alignment if either doc is set in case the
tokenization has changed.
* Add PRF tokenization scoring for tokens as spans
Add PRF scores for tokens as character spans. The scores are:
* token_acc: # correct tokens / # gold tokens
* token_p/r/f: PRF for (token.idx, token.idx + len(token))
* Add docstring to Scorer.score_tokenization
* Rename component.evaluate() to component.score()
* Update Scorer API docs
* Update scoring for positive_label in textcat
* Fix TextCategorizer.score kwargs
* Update Language.evaluate docs
* Update score names in default config
* Update 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>
* Improve tag map initialization and updating
Generalize tag map initialization and updating so that the tag map can
be loaded correctly prior to loading a `Corpus` with `spacy debug-data`
and `spacy train`.
* normalize provided tag map as necessary
* use the same method for initializing and updating the tag map
* Replace rather than update tag map
Replace rather than update tag map when loading a custom tag map.
Updating the tag map is problematic due to the sorted list of tag names
and the fact that the tag map will contain lingering/unwanted tags from
the default tag map.
* Update CLI scripts
* Reinitialize cache after loading new tag map
Reinitialize the cache with the right size after loading a new tag map.
* update `Morphologizer.begin_training` for use with `Example`
* make init and begin_training more consistent
* add `Morphology.normalize_features` to normalize outside of
`Morphology.add`
* make sure `get_loss` doesn't create unknown labels when the POS and
morph alignments differ
Serialize `morph_rules` with the tagger alongside the `tag_map`.
Use `Morphology.load_tag_map` and `Morphology.load_morph_exceptions` to
load these settings rather than reinitializing the morphology each time
they are changed.
* 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`.
* 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
* 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
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
* 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>
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`.
* 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>
* 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)
* 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
* 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
