* Add regression test
* Run PhraseMatcher on Spans
* Add test for PhraseMatcher on Spans and Docs
* Add SCA
* Add test with 3 matches in Doc, 1 match in Span
* Update docs
* Use doc.length for find_matches in tokenizer
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Now that the initialize step is fully implemented, the source of E923 is
typically missing or improperly converted/formatted data rather than a
bug in spaCy, so rephrase the error and message and remove the prompt to
open an issue.
```python
def test_vocab_lexeme_add_flag_auto_id(en_vocab):
is_len4 = en_vocab.add_flag(lambda string: len(string) == 4)
assert en_vocab["1999"].check_flag(is_len4) is True
assert en_vocab["1999"].check_flag(IS_DIGIT) is True
assert en_vocab["199"].check_flag(is_len4) is False
> assert en_vocab["199"].check_flag(IS_DIGIT) is True
E assert False is True
E + where False = <built-in method check_flag of spacy.lexeme.Lexeme object at 0x7fa155c36840>(3)
E + where <built-in method check_flag of spacy.lexeme.Lexeme object at 0x7fa155c36840> = <spacy.lexeme.Lexeme object at 0x7fa155c36840>.check_flag
spacy/tests/vocab_vectors/test_lexeme.py:49: AssertionError
```
> `pytest==6.1.1`
>
> `numpy==1.19.2`
>
> `Python version: 3.8.3`
To reproduce the error, run `pytest --random-order-bucket=global --random-order-seed=170158 -v spacy/tests`
If `test_vocab_lexeme_add_flag_auto_id` is run after `test_vocab_lexeme_add_flag_provided_id`, it fails.
It seems like `test_vocab_lexeme_add_flag_provided_id` uses the `IS_DIGIT` bit for testing purposes but does not reset the bit.
This solution seems to work but, if anyone has a better fix, please let me know and I will integrate it.
* add capture argument to project_run and run_commands
* git bump to 3.0.1
* Set version to 3.0.1.dev0
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
When `--no-cache-dir` is present, it prevents caching to properly function.
If the user still wants to do this, there is the possibility to pass options with `user_pip_args`.
But you should not enforce options like these. In my case this is preventing some docker build (using buildkit caching) to have proper caching of models.
Instead of silently using only the first token in each matched span:
* Forbid `OP: ?/*/+` through `DependencyMatcher` validation
* As a fail-safe, add warning if a token match that's not exactly one
token long is found by a token pattern.
* add error handler for pipe methods
* add unit tests
* remove pipe method that are the same as their base class
* have Language keep track of a default error handler
* cleanup
* formatting
* small refactor
* add documentation
* Initial Spanish lemmatizer
* Handle merged verb+pron(s) multi-word tokens
* Use VERB for AUX rule lookup
* Add morph to lemma cache key
* Fix aux lookups, minor refactoring
* Improve verb+pron handling
* Move verb+pron handling into its own method
* Check for exceptions (primarily for se)
* Collect pronouns in the same (not reversed) order
* Only add modified possible lemmas
* Fix `spacy.util.minibatch` when the size iterator is finished (#6745)
* Skip 0-length matches (#6759)
Add hack to prevent matcher from returning 0-length matches.
* support IS_SENT_START in PhraseMatcher (#6771)
* support IS_SENT_START in PhraseMatcher
* add unit test and friendlier error
* use IDS.get instead
* ensure span.text works for an empty span (#6772)
* Remove unicode_literals
Co-authored-by: Santiago Castro <bryant@montevideo.com.uy>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Allow output_path to be None during training
* Fix cat scoring (?)
* Improve error message for weighted None score
* Improve messages
So we can call this in other places etc.
* FIx output path check
* Use latest wasabi
* Revert "Improve error message for weighted None score"
This reverts commit 7059926763.
* Exclude None scores from final score by default
It's otherwise very difficult to keep track of the score weights if we modify a config programmatically, source components etc.
* Update warnings and use logger.warning
* Spacy Cli info method causing backward compatibility issues #6791
fix backward compatibility by setting default value to exclude in info
method.
* setting empty list as default argument is dangerous.
so setting default to None and then setting it to emptylist, if None.
Reference : https://nikos7am.com/posts/mutable-default-arguments/
* Adding contributor agreement for user werew
* [DependencyMatcher] Comment and clean code
* [DependencyMatcher] Use defaultdicts
* [DependencyMatcher] Simplify _retrieve_tree method
* [DependencyMatcher] Remove prepended underscores
* [DependencyMatcher] Address TODO and move grouping of token's positions out of the loop
* [DependencyMatcher] Remove _nodes attribute
* [DependencyMatcher] Use enumerate in _retrieve_tree method
* [DependencyMatcher] Clean unused vars and use camel_case naming
* [DependencyMatcher] Memoize node+operator map
* Add root property to Token
* [DependencyMatcher] Groups matches by root
* [DependencyMatcher] Remove unused _keys_to_token attribute
* [DependencyMatcher] Use a list to map tokens to matcher's keys
* [DependencyMatcher] Remove recursion
* [DependencyMatcher] Use a generator to retrieve matches
* [DependencyMatcher] Remove unused memory pool
* [DependencyMatcher] Hide private methods and attributes
* [DependencyMatcher] Improvements to the matches validation
* Apply suggestions from code review
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* [DependencyMatcher] Fix keys_to_position_maps
* Remove Token.root property
* [DependencyMatcher] Remove functools' lru_cache
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* warn when frozen components break listener pattern
* few notes in the documentation
* update arg name
* formatting
* cleanup
* specify listeners return type
* raise NotImplementedError when noun_chunks iterator is not implemented
* bring back, fix and document span.noun_chunks
* formatting
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Add long_token_splitter component
Add a `long_token_splitter` component for use with transformer
pipelines. This component splits up long tokens like URLs into smaller
tokens. This is particularly relevant for pretrained pipelines with
`strided_spans`, since the user can't change the length of the span
`window` and may not wish to preprocess the input texts.
The `long_token_splitter` splits tokens that are at least
`long_token_length` tokens long into smaller tokens of `split_length`
size.
Notes:
* Since this is intended for use as the first component in a pipeline,
the token splitter does not try to preserve any token annotation.
* API docs to come when the API is stable.
* Adjust API, add test
* Fix name in factory
Add all strings from the source model when adding a pipe from a source
model.
Minor:
* Skip `disable=["vocab", "tokenizer"]` when loading a source model from
the config, since this doesn't do anything and is misleading.
* Handle unset token.morph in Morphologizer
Handle unset `token.morph` in `Morphologizer.initialize` and
`Morphologizer.get_loss`. If both `token.morph` and `token.pos` are
unset, treat the annotation as missing rather than empty.
* Add token.has_morph()
* Override language defaults for null token and URL match
When the serialized `token_match` or `url_match` is `None`, override the
language defaults to preserve `None` on deserialization.
* Fix fixtures in tests
* Draft out initial Spans data structure
* Initial span group commit
* Basic span group support on Doc
* Basic test for span group
* Compile span_group.pyx
* Draft addition of SpanGroup to DocBin
* Add deserialization for SpanGroup
* Add tests for serializing SpanGroup
* Fix serialization of SpanGroup
* Add EdgeC and GraphC structs
* Add draft Graph data structure
* Compile graph
* More work on Graph
* Update GraphC
* Upd graph
* Fix walk functions
* Let Graph take nodes and edges on construction
* Fix walking and getting
* Add graph tests
* Fix import
* Add module with the SpanGroups dict thingy
* Update test
* Rename 'span_groups' attribute
* Try to fix c++11 compilation
* Fix test
* Update DocBin
* Try to fix compilation
* Try to fix graph
* Improve SpanGroup docstrings
* Add doc.spans to documentation
* Fix serialization
* Tidy up and add docs
* Update docs [ci skip]
* Add SpanGroup.has_overlap
* WIP updated Graph API
* Start testing new Graph API
* Update Graph tests
* Update Graph
* Add docstring
Co-authored-by: Ines Montani <ines@ines.io>
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.
