SpaCy's HashEmbedCNN layer performs convolutions over tokens to produce
contextualized embeddings using a `MaxoutWindowEncoder` layer. These
convolutions are implemented using Thinc's `expand_window` layer, which
concatenates `window_size` neighboring sequence items on either side of
the sequence item being processed. This is repeated across `depth`
convolutional layers.
For example, consider the sequence "ABCDE" and a `MaxoutWindowEncoder`
layer with a context window of 1 and a depth of 2. We'll focus on the
token "C". We can visually represent the contextual embedding produced
for "C" as:
```mermaid
flowchart LR
A0(A<sub>0</sub>)
B0(B<sub>0</sub>)
C0(C<sub>0</sub>)
D0(D<sub>0</sub>)
E0(E<sub>0</sub>)
B1(B<sub>1</sub>)
C1(C<sub>1</sub>)
D1(D<sub>1</sub>)
C2(C<sub>2</sub>)
A0 --> B1
B0 --> B1
C0 --> B1
B0 --> C1
C0 --> C1
D0 --> C1
C0 --> D1
D0 --> D1
E0 --> D1
B1 --> C2
C1 --> C2
D1 --> C2
```
Described in words, this graph shows that before the first layer of the
convolution, the "receptive field" centered at each token consists only
of that same token. That is to say, that we have a receptive field of 1.
The first layer of the convolution adds one neighboring token on either
side to the receptive field. Since this is done on both sides, the
receptive field increases by 2, giving the first layer a receptive field
of 3. The second layer of the convolutions adds an _additional_
neighboring token on either side to the receptive field, giving a final
receptive field of 5.
However, this doesn't match the formula currently given in the docs,
which read:
> The receptive field of the CNN will be
> `depth * (window_size * 2 + 1)`, so a 4-layer network with a window
> size of `2` will be sensitive to 20 words at a time.
Substituting in our depth of 2 and window size of 1, this formula gives
us a receptive field of:
```
depth * (window_size * 2 + 1)
= 2 * (1 * 2 + 1)
= 2 * (2 + 1)
= 2 * 3
= 6
```
This not only doesn't match our computations from above, it's also an
even number! This is suspicious, since the receptive field is supposed
to be centered on a token, and not between tokens. Generally, this
formula results in an even number for any even value of `depth`.
The error in this formula is that the adjustment for the center token
is multiplied by the depth, when it should occur only once. The
corrected formula, `depth * window_size * 2 + 1`, gives the correct
value for our small example from above:
```
depth * window_size * 2 + 1
= 2 * 1 * 2 + 1
= 4 + 1
= 5
```
These changes update the docs to correct the receptive field formula and
the example receptive field size.
* feat: add example stubs
* fix: add required annotations
* fix: mypy issues
* fix: use Py36-compatible Portocol
* Minor reformatting
* adding further type specifications and removing internal methods
* black formatting
* widen type to iterable
* add private methods that are being used by the built-in convertors
* revert changes to corpus.py
* fixes
* fixes
* fix typing of PlainTextCorpus
---------
Co-authored-by: Basile Dura <basile@bdura.me>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Support registered vectors
* Format
* Auto-fill [nlp] on load from config and from bytes/disk
* Only auto-fill [nlp]
* Undo all changes to Language.from_disk
* Expand BaseVectors
These methods are needed in various places for training and vector
similarity.
* isort
* More linting
* Only fill [nlp.vectors]
* Update spacy/vocab.pyx
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Revert changes to test related to auto-filling [nlp]
* Add vectors registry
* Rephrase error about vocab methods for vectors
* Switch to dummy implementation for BaseVectors.to_ops
* Add initial draft of docs
* Remove example from BaseVectors docs
* Apply suggestions from code review
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Update website/docs/api/basevectors.mdx
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix type and lint bpemb example
* Update website/docs/api/basevectors.mdx
---------
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* remove migration support form
* initial test commit
* add fixture
* add combo test
* pull out parameter example data
* fix formatting on examples
* remove unused import
* remove unncessary fmt:off instructions
* only set logger level if verbose flag is explicitly set
---------
Co-authored-by: svlandeg <svlandeg@github.com>
* Add data structures to docs
* Adjusted descriptions for more consistency
* Add _optional_ flag to parameters
* Add tests and adjust optional title key in doc
* Add title to dep visualizations
* fix typo
---------
Co-authored-by: thomashacker <EdwardSchmuhl@web.de>
* Add cli for finding locations of registered func
* fixes: naming and typing
* isort
* update naming
* remove to find-function
* remove file:// bit
* use registry name if given and exit gracefully if a registry was not found
* clean up failure msg
* specify registry_name options
* mypy fixes
* return location for internal usage
* add documentation
* more mypy fixes
* clean up example
* add section to menu
* add tests
---------
Co-authored-by: svlandeg <svlandeg@github.com>
* Update numpy build constraints for numpy 1.25
Starting in numpy 1.25 (see
https://github.com/numpy/numpy/releases/tag/v1.25.0), the numpy C API is
backwards-compatible by default.
For python 3.9+, we should be able to drop the specific numpy build
requirements and use `numpy>=1.25`, which is currently
backwards-compatible to `numpy>=1.19`.
In the future, the python <3.9 requirements could be dropped and the
lower numpy pin could correspond to the oldest supported version for the
current lower python pin.
* Turn off fail-fast
* Revert "Turn off fail-fast"
This reverts commit 4306f516bc.
* Update for python 3.6
* Fix typo
* Update universe.json
* Update universe.json
add some missing commas in the greCy's description.
* Update punctuation.py
Add mathematical left and right angle brackets as punctuation for ancient Greek for better tokenization.
* modified: spacy/language.py
- corrected typo in docstring for :method:`Language.replace_listeners`
- added noqa comment on unused local variable assignment in :method:`Language.from_config` as I wasn't sure if it should be unassigned
modified: website/docs/api/language.mdx
- corrected typo in `Language.replace_listeners` markdown
* modified: spacy/language.py
- removed noqa comment
---------
Co-authored-by: Ian Thompson <ian.thompson@hrblock.com>
These changes add a missing call to `escape_html` in the displaCy span
renderer. Previously span-annotated tokens would be inserted into the
page markup without being escaped, resulting in potentially incorrect
rendering. When I encountered this issue, it resulted in some docs and
span underlines being superimposed on top of properly rendered docs and
span underlines near the beginning of the visualization (due to an
unescaped `<span>` tag).
* Setting up weasel branch (#12456)
* remove project-specific functionality
* remove project-specific tests
* remove project-specific schemas
* remove project-specific information in about
* remove project-specific functions in util.py
* remove project-specific error strings
* remove project-specific CLI commands
* black formatting
* restore some functions that are used beyond projects
* remove project imports
* remove imports
* remove remote_storage tests
* remove one more project unit test
* update for PR 12394
* remove get_hash and get_checksum
* remove upload_ and download_file methods
* remove ensure_pathy
* revert clumsy fingers
* reinstate E970
* feat: use weasel as spacy project command (#12473)
* feat: use weasel as spacy project command
* build: use constrained requirement for weasel
* feat: add weasel to the library requirements
* build: update weasel to new version
* build: use specific weasel tag
* build: use weasel-0.1.0rc1 from PyPI
* fix: remove weasel from requirements.txt
* fix: requirements.txt and setup.cfg need to reflect each other
* feat: remove legacy spacy project code
* bump version
* further merge fixes
* isort
---------
Co-authored-by: Basile Dura <bdura@users.noreply.github.com>
When the default `max_length` is not set and there are longer training
documents, it can be difficult to train and evaluate the span finder due
to memory limits and the time it takes to evaluate a huge number of
predicted spans.
* `Language.replace_listeners`: Pass the replaced listener and the `tok2vec` pipe to the callback
* Update developer docs
* `isort` fixes
* Add error message to assertion
* Add clarification to dev docs
* Replace assertion with exception
* Doc fixes
* Support custom token/lexeme attribute for vectors
* Fix imports
* Back off to ORTH without Vectors.attr
* Fallback if vectors.attr doesn't exist
* Update docs
When sourcing a component, the object from the original pipeline is added to the new pipeline as the same object. This creates a situation where there are several attributes that cannot be in sync between the original pipeline and the new pipeline at the same time for this one object:
* component.name
* component.listener_map / component.listening_components for tok2vec and transformer
When running replace_listeners on a component, the config is not updated correctly if the state of the component is incorrect for the current pipeline (in particular changes that should be applied from model.attrs["replace_listener_cfg"] as used in spacy-transformers) due to the fact that:
* find_listeners relies on component.name to set the name in the listener_map
* replace_listeners relies on listener_map to determine how to modify the configs
In addition, there are several places where pipeline components are modified and the listener map and/or internal component names aren't currently updated.
In cases where there is a component shared by two pipelines that cannot be in sync, this PR chooses to prioritize the most recently modified or initialized pipeline. There is no actual solution with the current source behavior that will make both pipelines usable, so the current pipeline is updated whenever components are added/renamed/removed or the pipeline is initialized for training.
* Use isort with Black profile
* isort all the things
* Fix import cycles as a result of import sorting
* Add DOCBIN_ALL_ATTRS type definition
* Add isort to requirements
* Remove isort from build dependencies check
* Typo
* span finder integrated into spacy from experimental
* black
* isort
* black
* default spankey constant
* black
* Update spacy/pipeline/spancat.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* rename
* rename
* max_length and min_length as Optional[int] and strict checking
* black
* mypy fix for integer type infinity
* revert line order
* implement all comparison operators for inf int
* avoid two for loops over all docs by not precomputing
* interleave thresholding with span creation
* black
* revert to not interleaving (relized its faster)
* black
* Update spacy/errors.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* update dosctring
* enforce that the gold and predicted documents have the same text
* new error for ensuring reference and predicted texts are the same
* remove todo
* adjust test
* black
* handle misaligned tokenization
* return correct variable
* failing overfit test
* only use a single spans_key like in spancat
* black
* remove debug lines
* typo
* remove comment
* remove near duplicate reduntant method
* use the 'spans_key' variable name everywhere
* Update spacy/pipeline/span_finder.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* flaky test fix suggestion, hand set bias terms
* only test suggester and test result exhaustively
* make it clear that the span_finder_suggester is more general (not specific to span_finder)
* Update spacy/tests/pipeline/test_span_finder.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Apply suggestions from code review
* remove question comment
* move preset_spans_suggester test to spancat tests
* Add docs and unify default configs for spancat and span finder
* Add `allow_overlap=True` to span finder scorer
* Fix offset bug in set_annotations
* Ignore labels in span finder scorer
* Format
* Add span_finder to quickstart template
* Move settings to self.cfg, store min/max unset as None
* Remove debugging
* Update docstrings and docs
* Update spacy/pipeline/span_finder.py
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix imports
---------
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Require that all SpanGroup spans are from the current doc
The restriction on only adding spans from the current doc were already
implemented for all operations except for `SpanGroup.__init__`.
Initialize copied spans for `SpanGroup.copy` with `Doc.char_span` in
order to validate the character offsets and to make it possible to copy
spans between documents with differing tokenization. Currently there is
no validation that the document texts are identical, but the span char
offsets must be valid spans in the target doc, which prevents you from
ending up with completely invalid spans.
* Undo change in test_beam_overfitting_IO
* Address upcoming numpy v1.25 deprecations in test suite
* Temporarily test most recent numpy prerelease in CI
* Revert "Temporarily test most recent numpy prerelease in CI"
This reverts commit d75a66e55e.
* Add scorer option to return per-component scores
Add `per_component` option to `Language.evaluate` and `Scorer.score` to
return scores keyed by `tokenizer` (hard-coded) or by component name.
Add option to `evaluate` CLI to score by component. Per-component scores
can only be saved to JSON.
* Update help text and messages
This reverts commit 6f314f99c4.
We are reverting this until we can support this normalization more
consistently across vectors, training corpora, and lemmatizer data.
* Use Latin normalization for Serbian attrs
Use Latin normalization for Serbian `NORM`, `PREFIX`, and `SUFFIX`.
* Update NORMs in tokenizer exceptions and related tests
* Add tests for all custom lex attrs
* Remove unused imports
* Add spans in spacy benchmark
The current implementation of spaCy benchmark accuracy / spacy evaluate
doesn't include the "spans" type, so calling the command doesn't render
the HTML displaCy file needed.
This PR attempts to fix that by creating a new parameter for "spans"
and calling the appropriate displaCy value.
* Reformat file with black
* Add tests for evaluate
* Fix spans -> span for displacy style
* Update test to check render instead
* Update source so mypy passes
* Add parser information to avoid warnings
* avoid nesting then flattening
* mypy fix
* Apply suggestions from code review
* Add type for indices
* Run full matrix for mypy
* Add back modified type: ignore
* Revert "Run full matrix for mypy"
This reverts commit e218873d04.
---------
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add noun chunking to la syntax iterators
* Expand list of numeral, ordinal words
* Expand abbreviations in la tokenizer_exceptions
* Add example sents
* Update spacy/lang/la/syntax_iterators.py
Reorganize la syntax iterators
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Minor updates based on review
* fix call
---------
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Add default to MorphAnalysis.get
Similar to `dict`, allow a `default` option for `MorphAnalysis.get` for
the user to provide a default return value if the field is not found.
The default return value remains `[]`, which is not the same as
`dict.get`, but is already established as this method's default return
value with the return type `List[str]`. However the new `default` option
does not enforce that the user-provided default is actually `List[str]`.
* Restore test case
In `Tokenizer.from_bytes`, the exceptions should be loaded last so that
they are only processed once as part of loading the model.
The exceptions are tokenized as phrase matcher patterns in the
background and the internal tokenization needs to be synced with all the
remaining tokenizer settings. If the exceptions are not loaded last,
there are speed regressions for `Tokenizer.from_bytes/disk` vs.
`Tokenizer.add_special_case` as the caches are reloaded more than
necessary during deserialization.
* [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>
