* [wip] Update
* [wip] Update
* Add initial port
* [wip] Update
* Fix all imports
* Add spancat_exclusive to pipeline
* [WIP] Update
* [ci skip] Add breakpoint for debugging
* Use spacy.SpanCategorizer.v1 as default archi
* Update spacy/pipeline/spancat_exclusive.py
Co-authored-by: kadarakos <kadar.akos@gmail.com>
* [ci skip] Small updates
* Use Softmax v2 directly from thinc
* Cache the label map
* Fix mypy errors
However, I ignored line 370 because it opened up a bunch of type errors
that might be trickier to solve and might lead to a more complicated
codebase.
* avoid multiplication with 1.0
Co-authored-by: kadarakos <kadar.akos@gmail.com>
* Update spacy/pipeline/spancat_exclusive.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update component versions to v2
* Add scorer to docstring
* Add _n_labels property to SpanCategorizer
Instead of using len(self.labels) in initialize() I am using a private
property self._n_labels. This achieves implementation parity and allows
me to delete the whole initialize() method for spancat_exclusive (since
it's now the same with spancat).
* Inherit from SpanCat instead of TrainablePipe
This commit changes the inheritance structure of Exclusive_Spancat,
now it's inheriting from SpanCategorizer than TrainablePipe. This
allows me to remove duplicate methods that are already present in
the parent function.
* Revert documentation link to spancat
* Fix init call for exclusive spancat
* Update spacy/pipeline/spancat_exclusive.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Import Suggester from spancat
* Include zero_init.v1 for spancat
* Implement _allow_extra_label to use _n_labels
To ensure that spancat / spancat_exclusive cannot be resized after
initialization, I inherited the _allow_extra_label() method from
spacy/pipeline/trainable_pipe.pyx and used self._n_labels instead
of len(self.labels) for checking.
I think that changing it locally is a better solution rather than
forcing each class that inherits TrainablePipe to use the self._n_labels
attribute.
Also note that I turned-off black formatting in this block of code
because it reads better without the overhang.
* Extend existing tests to spancat_exclusive
In this commit, I extended the existing tests for spancat to include
spancat_exclusive. I parametrized the test functions with 'name'
(similar var name with textcat and textcat_multilabel) for each
applicable test.
TODO: Add overfitting tests for spancat_exclusive
* Update documentation for spancat
* Turn on formatting for allow_extra_label
* Remove initializers in default config
* Use DEFAULT_EXCL_SPANCAT_MODEL
I also renamed spancat_exclusive_default_config into
spancat_excl_default_config because black does some not pretty
formatting changes.
* Update documentation
Update grammar and usage
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Clarify docstring for Exclusive_SpanCategorizer
* Remove mypy ignore and typecast labels to list
* Fix documentation API
* Use a single variable for tests
* Update defaults for number of rows
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Put back initializers in spancat config
Whenever I remove model.scorer.init_w and model.scorer.init_b,
I encounter an error in the test:
SystemError: <method '__getitem__' of 'dict' objects> returned a result
with an error set.
My Thinc version is 8.1.5, but I can't seem to check what's causing the
error.
* Update spancat_exclusive docstring
* Remove init_W and init_B parameters
This commit is expected to fail until the new Thinc release.
* Require thinc>=8.1.6 for serializable Softmax defaults
* Handle zero suggestions to make tests pass
I'm not sure if this is the most elegant solution. But what should
happen is that the _make_span_group function MUST return an empty
SpanGroup if there are no suggestions.
The error happens when the 'scores' variable is empty. We cannot
get the 'predicted' and other downstream vars.
* Better approach for handling zero suggestions
* Update website/docs/api/spancategorizer.md
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spancategorizer headers
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Add default value in negative_weight in docs
* Add default value in allow_overlap in docs
* Update how spancat_exclusive is constructed
In this commit, I added the following:
- Put the default values of negative_weight and allow_overlap
in the default_config dictionary.
- Rename make_spancat -> make_exclusive_spancat
* Run prettier on spancategorizer.mdx
* Change exactly one -> at most one
* Add suggester documentation in Exclusive_SpanCategorizer
* Add suggester to spancat docstrings
* merge multilabel and singlelabel spancat
* rename spancat_exclusive to singlelable
* wire up different make_spangroups for single and multilabel
* black
* black
* add docstrings
* more docstring and fix negative_label
* don't rely on default arguments
* black
* remove spancat exclusive
* replace single_label with add_negative_label and adjust inference
* mypy
* logical bug in configuration check
* add spans.attrs[scores]
* single label make_spangroup test
* bugfix
* black
* tests for make_span_group with negative labels
* refactor make_span_group
* black
* Update spacy/tests/pipeline/test_spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* remove duplicate declaration
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* raise error instead of just print
* make label mapper private
* update docs
* run prettier
* Update website/docs/api/spancategorizer.mdx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update website/docs/api/spancategorizer.mdx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* don't keep recomputing self._label_map for each span
* typo in docs
* Intervals to private and document 'name' param
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* add Tag to new features
* replace tags
* revert
* revert
* revert
* revert
* Update website/docs/api/spancategorizer.mdx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Update website/docs/api/spancategorizer.mdx
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* prettier
* Fix merge
* Update website/docs/api/spancategorizer.mdx
* remove references to 'single_label'
* remove old paragraph
* Add spancat_singlelabel to config template
* Format
* Extend init config tests
---------
Co-authored-by: kadarakos <kadar.akos@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Change GPU efficient textcat to use CNN, not BOW
If you generate a config with a textcat component using GPU
(transformers), the defaut option (efficiency) uses a BOW architecture,
which does not use tok2vec features. While that can make sense as part
of a larger pipeline, in the case of just a transformer and a textcat,
that means the transformer is doing a lot of work for no purpose.
This changes it so that the CNN architecture is used instead. It could
also be changed to be the same as the accuracy config, which uses the
ensemble architecture.
* Add the transformer when using a textcat with GPU
* Switch ubuntu-latest to ubuntu-20.04 in main tests (#11928)
* Switch ubuntu-latest to ubuntu-20.04 in main tests
* Only use 20.04 for 3.6
* Require thinc v8.1.7
* Require thinc v8.1.8
* Break up longer expression
---------
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Handle deprecation of pkg_resources
* Replace `pkg_resources` with `importlib_metadata` for `spacy info
--url`
* Remove requirements check from `spacy project` given the lack of
alternatives
* Fix installed model URL method and CI test
* Fix types/handling, simplify catch-all return
* Move imports instead of disabling requirements check
* Format
* Reenable test with ignored deprecation warning
* Fix except
* Fix return
* Make empty_kb() configurable.
* Format.
* Update docs.
* Be more specific in KB serialization test.
* Update KB serialization tests. Update docs.
* Remove doc update for batched candidate generation.
* Fix serialization of subclassed KB in tests.
* Format.
* Update docstring.
* Update docstring.
* Switch from pickle to json for custom field serialization.
* Add immediate left/right child/parent dependency relations
* Add tests for new REL_OPs: `>+`, `>-`, `<+`, and `<-`.
---------
Co-authored-by: Tan Long <tanloong@foxmail.com>
* add unittest for explosion#12311
* create punctuation.py for swedish
* removed : from infixes in swedish punctuation.py
* allow : as infix if succeeding char is uppercase
* standardize predicate key format
* single key function
* Make optional args in key function keyword-only
---------
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* change logging call for spacy.LookupsDataLoader.v1
* substitutions in language and _util
* various more substitutions
* add string formatting guidelines to contribution guidelines
* Normalize whitespace in evaluate CLI output test
Depending on terminal settings, lines may be padded to the screen width
so the comparison is too strict with only the command string replacement.
* Move to test util method
* Change to normalization method
* Add span_id to Span.char_span, update Doc/Span.char_span docs
`Span.char_span(id=)` should be removed in the future.
* Also use Union[int, str] in Doc docstring
* WIP
* rm ipython embeds
* rm total
* WIP
* cleanup
* cleanup + reword
* rm component function
* remove migration support form
* fix reference dataset for dev data
* additional fixes
- set approach to identifying unique trees
- adjust line length on messages
- add logic for detecting docs without annotations
* use 0 instead of none for no annotation
* partial annotation support
* initial tests for _compile_gold lemma attributes
Using the example data from the edit tree lemmatizer tests for:
- lemmatizer_trees
- partial_lemma_annotations
- n_low_cardinality_lemmas
- no_lemma_annotations
* adds output test for cli app
* switch msg level
* rm unclear uniqueness check
* Revert "rm unclear uniqueness check"
This reverts commit 6ea2b3524b.
* remove good message on uniqueness
* formatting
* use en_vocab fixture
* clarify data set source in messages
* remove unnecessary import
Co-authored-by: svlandeg <svlandeg@github.com>
* Add `spacy.PlainTextCorpusReader.v1`
This is a corpus reader that reads plain text corpora with the following
format:
- UTF-8 encoding
- One line per document.
- Blank lines are ignored.
It is useful for applications where we deal with very large corpora,
such as distillation, and don't want to deal with the space overhead of
serialized formats. Additionally, many large corpora already use such
a text format, keeping the necessary preprocessing to a minimum.
* Update spacy/training/corpus.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* docs: add version to `PlainTextCorpus`
* Add docstring to registry function
* Add plain text corpus tests
* Only strip newline/carriage return
* Add return type _string_to_tmp_file helper
* Use a temporary directory in place of file name
Different OS auto delete/sharing semantics are just wonky.
* This will be new in 3.5.1 (rather than 4)
* Test improvements from code review
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Refactor _scores2guesses
* Handle arrays on GPU
* Convert argmax result to raw integer
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
* Use NumpyOps() to copy data to CPU
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
* Changes based on review comments
* Use different _scores2guesses depending on tree_k
* Add tests for corner cases
* Add empty line for consistency
* Improve naming
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
* Improve naming
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
Co-authored-by: Daniël de Kok <me@github.danieldk.eu>
* API docs: Rename kb_in_memory to inmemorylookupkb, add to sidebar
* adjust to mdx
* linkout to InMemoryLookupKB at first occurrence in kb.mdx
* fix links to docs
* revert Azure trigger setting (I'll make a separate PR)
Co-authored-by: svlandeg <svlandeg@github.com>
* Add a `spacy evaluate speed` subcommand
This subcommand reports the mean batch performance of a model on a data set with
a 95% confidence interval. For reliability, it first performs some warmup
rounds. Then it will measure performance on batches with randomly shuffled
documents.
To avoid having too many spaCy commands, `speed` is a subcommand of `evaluate`
and accuracy evaluation is moved to its own `evaluate accuracy` subcommand.
* Fix import cycle
* Restore `spacy evaluate`, make `spacy benchmark speed` an alias
* Add documentation for `spacy benchmark`
* CREATES -> PRINTS
* WPS -> words/s
* Disable formatting of benchmark speed arguments
* Fail with an error message when trying to speed bench empty corpus
* Make it clearer that `benchmark accuracy` is a replacement for `evaluate`
* Fix docstring webpage reference
* tests: check `evaluate` output against `benchmark accuracy`
* Clean up displacy port-related error messages, docs
There were some issues in the error messages and docs in #11948.
1. the error messages didn't specify the port argument to displacy.serve correctly
2. the docs didn't mark the auto select argument as new
This addresses those issues.
* Update website/docs/api/top-level.md
Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
* Apply prettier
Co-authored-by: Raphael Mitsch <r.mitsch@outlook.com>
In the v3 scorer refactoring, `token_acc` was implemented incorrectly.
It should use `precision` instead of `fscore` for the measure of
correctly aligned tokens / number of predicted tokens.
Fix the docs to reflect that the measure uses the number of predicted
tokens rather than the number of gold tokens.
