Validate both `[initialize]` and `[training]` in `debug data` and
`nlp.initialize()` with separate config validation error blocks that
indicate which block of the config is being validated.
Add `initialize.before_init` and `initialize.after_init` callbacks to
the config. The `initialize.before_init` callback is a place to
implement one-time tokenizer customizations that are then saved with the
model.
* Update stop_words.py
Added three aditional stopwords: "a" and "o" that means "the", and "e" that means "and"
* Create cristianasp.md
* zero edit to push CI
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* fix TorchBiLSTMEncoder documentation
* ensure the types of the encoding Tok2vec layers are correct
* update references from v1 to v2 for the new architectures
* add syntax iterators for danish
* add test noun chunks for danish syntax iterators
* add contributor agreement
* update da syntax iterators to remove nested chunks
* add tests for da noun chunks
* Fix test
* add missing import
* fix example
* Prevent overlapping noun chunks
Prevent overlapping noun chunks by tracking the end index of the
previous noun chunk span.
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* clean up of ner tests
* beam_parser tests
* implement get_beam_parses and scored_parses for the dep parser
* we don't have to add the parse if there are no arcs
* add convenience method to determine tok2vec width in a model
* fix transformer tok2vec dimensions in TextCatEnsemble architecture
* init function should not be nested to avoid pickle issues
* small fixes and formatting
* bring test_issue4313 up-to-date, currently fails
* formatting
* add get_beam_parses method back
* add scored_ents function
* delete tag map
Instead of unsetting lemmas on retokenized tokens, set the default
lemmas to:
* merge: concatenate any existing lemmas with `SPACY` preserved
* split: use the new `ORTH` values if lemmas were previously set,
otherwise leave unset
* multi-label textcat component
* formatting
* fix comment
* cleanup
* fix from #6481
* random edit to push the tests
* add explicit error when textcat is called with multi-label gold data
* fix error nr
* small fix
* Fix memory issues in Language.evaluate
Reset annotation in predicted docs before evaluating and store all data
in `examples`.
* Minor refactor to docs generator init
* Fix generator expression
* Fix final generator check
* Refactor pipeline loop
* Handle examples generator in Language.evaluate
* Add test with generator
* Use make_doc
* Add Amharic to space
* clean up
* Add some PRON_LEMMA
* add Tigrinya support
* remove text_noun_chunks
* Tigrinya Support
* added some more details for ti
* fix unit test
* add amharic char range
* changes from review
* amharic and tigrinya share same unicode block
* get rid of _amharic/_tigrinya in char_classes
Co-authored-by: Josiah Solomon <jsolomon@meteorcomm.com>
Fix lookup of empty morph in the morphology table, which fixes a memory
leak where a new morphology tag was allocated each time the empty morph
tag was added.
* Switch converters to generator functions
To reduce the memory usage when converting large corpora, refactor the
convert methods to be generator functions.
* Update tests
* 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
* add test for multi-label textcat reproducibility
* remove positive_label
* fix lengths dtype
* fix comments
* remove comment that we should not have forgotten :-)
Remove the non-working `--use-chars` option from the train CLI. The
implementation of the option across component types and the CLI settings
could be fixed, but the `CharacterEmbed` model does not work on GPU in
v2 so it's better to remove it.
* define new architectures for the pretraining objective
* add loss function as attr of the omdel
* cleanup
* cleanup
* shorten name
* fix typo
* remove unused error
Preserve `token.spacy` corresponding to the span end token in the
original doc rather than adjusting for the current offset.
* If not modifying in place, this checks in the original document
(`doc.c` rather than `tokens`).
* If modifying in place, the document has not been modified past the
current span start position so the value at the current span end
position is valid.
* When checking for token alignments, check not only that the tokens are
identical but that the character positions are both at the start of a
token.
It's possible for the tokens to be identical even though the two
tokens aren't aligned one-to-one in a case like `["a'", "''"]` vs.
`["a", "''", "'"]`, where the middle tokens are identical but should not
be aligned on the token level at character position 2 since it's the
start of one token but the middle of another.
* Use the lowercased version of the token texts to create the
character-to-token alignment because lowercasing can change the string
length (e.g., for `İ`, see the not-a-bug bug report:
https://bugs.python.org/issue34723)
* Only set NORM on Token in retokenizer
Instead of setting `NORM` on both the token and lexeme, set `NORM` only
on the token.
The retokenizer tries to set all possible attributes with
`Token/Lexeme.set_struct_attr` so that it doesn't have to enumerate
which attributes are available for each. `NORM` is the only attribute
that's stored on both and for most cases it doesn't make sense to set
the global norms based on a individual retokenization. For lexeme-only
attributes like `IS_STOP` there's no way to avoid the global side
effects, but I think that `NORM` would be better only on the token.
* Fix test
