* Alignment: use a simplified ragged type for performance
This introduces the AlignmentArray type, which is a simplified version
of Ragged that performs better on the simple(r) indexing performed for
alignment.
* AlignmentArray: raise an error when using unsupported index
* AlignmentArray: move error messages to Errors
* AlignmentArray: remove simlified ... with simplifications
* AlignmentArray: fix typo that broke a[n:n] indexing
* Fix get_matching_ents
Not sure what happened here - the code prior to this commit simply does
not work. It's already covered by entity linker tests, which were
succeeding in the NEL PR, but couldn't possibly succeed on master.
* Fix test
Test was indented inside another test and so doesn't seem to have been
running properly.
* Partial fix of entity linker batching
* Add import
* Better name
* Add `use_gold_ents` option, docs
* Change to v2, create stub v1, update docs etc.
* Fix error type
Honestly no idea what the right type to use here is.
ConfigValidationError seems wrong. Maybe a NotImplementedError?
* Make mypy happy
* Add hacky fix for init issue
* Add legacy pipeline entity linker
* Fix references to class name
* Add __init__.py for legacy
* Attempted fix for loss issue
* Remove placeholder V1
* formatting
* slightly more interesting train data
* Handle batches with no usable examples
This adds a test for batches that have docs but not entities, and a
check in the component that detects such cases and skips the update step
as thought the batch were empty.
* Remove todo about data verification
Check for empty data was moved further up so this should be OK now - the
case in question shouldn't be possible.
* Fix gradient calculation
The model doesn't know which entities are not in the kb, so it generates
embeddings for the context of all of them.
However, the loss does know which entities aren't in the kb, and it
ignores them, as there's no sensible gradient.
This has the issue that the gradient will not be calculated for some of
the input embeddings, which causes a dimension mismatch in backprop.
That should have caused a clear error, but with numpyops it was causing
nans to happen, which is another problem that should be addressed
separately.
This commit changes the loss to give a zero gradient for entities not in
the kb.
* add failing test for v1 EL legacy architecture
* Add nasty but simple working check for legacy arch
* Clarify why init hack works the way it does
* Clarify use_gold_ents use case
* Fix use gold ents related handling
* Add tests for no gold ents and fix other tests
* Use aligned ents function (not working)
This doesn't actually work because the "aligned" ents are gold-only. But
if I have a different function that returns the intersection, *then*
this will work as desired.
* Use proper matching ent check
This changes the process when gold ents are not used so that the
intersection of ents in the pred and gold is used.
* Move get_matching_ents to Example
* Use model attribute to check for legacy arch
* Rename flag
* bump spacy-legacy to lower 3.0.9
Co-authored-by: svlandeg <svlandeg@github.com>
Remove exception for whitespace tokens in `Example.get_aligned` so that
annotation on whitespace tokens is aligned in the same way as for
non-whitespace tokens.
* Support a cfg field in transition system
* Make NER 'has gold' check use right alignment for span
* Pass 'negative_samples_key' property into NER transition system
* Add field for negative samples to NER transition system
* Check neg_key in NER has_gold
* Support negative examples in NER oracle
* Test for negative examples in NER
* Fix name of config variable in NER
* Remove vestiges of old-style partial annotation
* Remove obsolete tests
* Add comment noting lack of support for negative samples in parser
* Additions to "neg examples" PR (#8201)
* add custom error and test for deprecated format
* add test for unlearning an entity
* add break also for Begin's cost
* add negative_samples_key property on Parser
* rename
* extend docs & fix some older docs issues
* add subclass constructors, clean up tests, fix docs
* add flaky test with ValueError if gold parse was not found
* remove ValueError if n_gold == 0
* fix docstring
* Hack in environment variables to try out training
* Remove hack
* Remove NER hack, and support 'negative O' samples
* Fix O oracle
* Fix transition parser
* Remove 'not O' from oracle
* Fix NER oracle
* check for spans in both gold.ents and gold.spans and raise if so, to prevent memory access violation
* use set instead of list in consistency check
Co-authored-by: svlandeg <sofie.vanlandeghem@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update sent_starts in Example.from_dict
Update `sent_starts` for `Example.from_dict` so that `Optional[bool]`
values have the same meaning as for `Token.is_sent_start`.
Use `Optional[bool]` as the type for sent start values in the docs.
* Use helper function for conversion to ternary ints
* extend span scorer with consider_label and allow_overlap
* unit test for spans y2x overlap
* add score_spans unit test
* docs for new fields in scorer.score_spans
* rename to include_label
* spell out if-else for clarity
* rename to 'labeled'
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Get basic beam tests working
* Get basic beam tests working
* Compile _beam_utils
* Remove prints
* Test beam density
* Beam parser seems to train
* Draft beam NER
* Upd beam
* Add hypothesis as dev dependency
* Implement missing is-gold-parse method
* Implement early update
* Fix state hashing
* Fix test
* Fix test
* Default to non-beam in parser constructor
* Improve oracle for beam
* Start refactoring beam
* Update test
* Refactor beam
* Update nn
* Refactor beam and weight by cost
* Update ner beam settings
* Update test
* Add __init__.pxd
* Upd test
* Fix test
* Upd test
* Fix test
* Remove ring buffer history from StateC
* WIP change arc-eager transitions
* Add state tests
* Support ternary sent start values
* Fix arc eager
* Fix NER
* Pass oracle cut size for beam
* Fix ner test
* Fix beam
* Improve StateC.clone
* Improve StateClass.borrow
* Work directly with StateC, not StateClass
* Remove print statements
* Fix state copy
* Improve state class
* Refactor parser oracles
* Fix arc eager oracle
* Fix arc eager oracle
* Use a vector to implement the stack
* Refactor state data structure
* Fix alignment of sent start
* Add get_aligned_sent_starts method
* Add test for ae oracle when bad sentence starts
* Fix sentence segment handling
* Avoid Reduce that inserts illegal sentence
* Update preset SBD test
* Fix test
* Remove prints
* Fix sent starts in Example
* Improve python API of StateClass
* Tweak comments and debug output of arc eager
* Upd test
* Fix state test
* Fix state test
* Replace pytokenizations with internal alignment
Replace pytokenizations with internal alignment algorithm that is
restricted to only allow differences in whitespace and capitalization.
* Rename `spacy.training.align` to `spacy.training.alignment` to contain
the `Alignment` dataclass
* Implement `get_alignments` in `spacy.training.align`
* Refactor trailing whitespace handling
* Remove unnecessary exception for empty docs
Allow a non-empty whitespace-only doc to be aligned with an empty doc
* Remove empty docs exceptions completely
* 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
In order to make it easier to construct `Doc` objects as training data,
modify how missing and blocked entity tokens are set to prioritize
setting `O` and missing entity tokens for training purposes over setting
blocked entity tokens.
* `Doc.ents` setter sets tokens outside entity spans to `O` regardless
of the current state of each token
* For `Doc.ents`, setting a span with a missing label sets the `ent_iob`
to missing instead of blocked
* `Doc.block_ents(spans)` marks spans as hard `O` for use with the
`EntityRecognizer`
