* Handle docs with no entities
If a whole batch contains no entities it won't make it to the model, but
it's possible for individual Docs to have no entities. Before this
commit, those Docs would cause an error when attempting to concatenate
arrays because the dimensions didn't match.
It turns out the process of preparing the Ragged at the end of the span
maker forward was a little different from list2ragged, which just uses
the flatten function directly. Letting list2ragged do the conversion
avoids the dimension issue.
This did not come up before because in NEL demo projects it's typical
for data with no entities to be discarded before it reaches the NEL
component.
This includes a simple direct test that shows the issue and checks it's
resolved. It doesn't check if there are any downstream changes, so a
more complete test could be added. A full run was tested by adding an
example with no entities to the Emerson sample project.
* Add a blank instance to default training data in tests
Rather than adding a specific test, since not failing on instances with
no entities is basic functionality, it makes sense to add it to the
default set.
* Fix without modifying architecture
If the architecture is modified this would have to be a new version, but
this change isn't big enough to merit that.
* Add failing test
* Partial fix for issue
This kind of works. The issue with token length mismatches is gone. The
problem is that when you get empty lists of encodings to compare, it
fails because the sizes are not the same, even though they're both zero:
(0, 3) vs (0,). Not sure why that happens...
* Short circuit on empties
* Remove spurious check
The check here isn't needed now the the short circuit is fixed.
* Update spacy/tests/pipeline/test_entity_linker.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Use "eg", not "example"
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* 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>
* Migrate regressions 1-1000
* Move serialize test to correct file
* Remove tests that won't work in v3
* Migrate regressions 1000-1500
Removed regression test 1250 because v3 doesn't support the old LEX
scheme anymore.
* Add missing imports in serializer tests
* Migrate tests 1500-2000
* Migrate regressions from 2000-2500
* Migrate regressions from 2501-3000
* Migrate regressions from 3000-3501
* Migrate regressions from 3501-4000
* Migrate regressions from 4001-4500
* Migrate regressions from 4501-5000
* Migrate regressions from 5001-5501
* Migrate regressions from 5501 to 7000
* Migrate regressions from 7001 to 8000
* Migrate remaining regression tests
* Fixing missing imports
* Update docs with new system [ci skip]
* Update CONTRIBUTING.md
- Fix formatting
- Update wording
* Remove lemmatizer tests in el lang
* Move a few tests into the general tokenizer
* Separate Doc and DocBin tests
* unit test for pickling KB
* add pickling test for NEL
* KB to_bytes and from_bytes
* NEL to_bytes and from_bytes
* xfail pickle tests for now
* fix docs
* cleanup
* 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
* 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
* 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
* 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
* 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>
