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.
* 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
* 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>
* 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
* 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
* [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
* change logging call for spacy.LookupsDataLoader.v1
* substitutions in language and _util
* various more substitutions
* add string formatting guidelines to contribution guidelines
* Init
* fix tests
* Update spacy/errors.py
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Fix test_blank_languages
* Rename xx to mul in docs
* Format _util with black
* prettier formatting
---------
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add the configuration schema for distillation
This also adds the default configuration and some tests. The schema will
be used by the training loop and `distill` subcommand.
* Format
* Change distillation shortopt to -d
* Fix descripion of max_epochs
* Rename distillation flag to -dt
* Rename `pipe_map` to `student_to_teacher`
* Don't re-download installed models
When downloading a model, this checks if the same version of the same
model is already installed. If it is then the download is skipped.
This is necessary because pip uses the final download URL for its
caching feature, but because of the way models are hosted on Github,
their URLs change every few minutes.
* Use importlib instead of meta.json
* Use get_package_version
* Add untested, disabled test
---------
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* 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>
* Try to fix doc.copy
* Set dev version
* Make vocab always own lexemes
* Change version
* Add SpanGroups.copy method
* Fix set_annotations during Parser.update
* Fix dict proxy copy
* Upd version
* Fix copying SpanGroups
* Fix set_annotations in parser.update
* Fix parser set_annotations during update
* Revert "Fix parser set_annotations during update"
This reverts commit eb138c89ed.
* Revert "Fix set_annotations in parser.update"
This reverts commit c6df0eafd0.
* Fix set_annotations during parser update
* Inc version
* Handle final states in get_oracle_sequence
* Inc version
* Try to fix parser training
* Inc version
* Fix
* Inc version
* Fix parser oracle
* Inc version
* Inc version
* Fix transition has_gold
* Inc version
* Try to use real histories, not oracle
* Inc version
* Upd parser
* Inc version
* WIP on rewrite parser
* WIP refactor parser
* New progress on parser model refactor
* Prepare to remove parser_model.pyx
* Convert parser from cdef class
* Delete spacy.ml.parser_model
* Delete _precomputable_affine module
* Wire up tb_framework to new parser model
* Wire up parser model
* Uncython ner.pyx and dep_parser.pyx
* Uncython
* Work on parser model
* Support unseen_classes in parser model
* Support unseen classes in parser
* Cleaner handling of unseen classes
* Work through tests
* Keep working through errors
* Keep working through errors
* Work on parser. 15 tests failing
* Xfail beam stuff. 9 failures
* More xfail. 7 failures
* Xfail. 6 failures
* cleanup
* formatting
* fixes
* pass nO through
* Fix empty doc in update
* Hackishly fix resizing. 3 failures
* Fix redundant test. 2 failures
* Add reference version
* black formatting
* Get tests passing with reference implementation
* Fix missing prints
* Add missing file
* Improve indexing on reference implementation
* Get non-reference forward func working
* Start rigging beam back up
* removing redundant tests, cf #8106
* black formatting
* temporarily xfailing issue 4314
* make flake8 happy again
* mypy fixes
* ensure labels are added upon predict
* cleanup remnants from merge conflicts
* Improve unseen label masking
Two changes to speed up masking by ~10%:
- Use a bool array rather than an array of float32.
- Let the mask indicate whether a label was seen, rather than
unseen. The mask is most frequently used to index scores for
seen labels. However, since the mask marked unseen labels,
this required computing an intermittent flipped mask.
* Write moves costs directly into numpy array (#10163)
This avoids elementwise indexing and the allocation of an additional
array.
Gives a ~15% speed improvement when using batch_by_sequence with size
32.
* Temporarily disable ner and rehearse tests
Until rehearse is implemented again in the refactored parser.
* Fix loss serialization issue (#10600)
* Fix loss serialization issue
Serialization of a model fails with:
TypeError: array(738.3855, dtype=float32) is not JSON serializable
Fix this using float conversion.
* Disable CI steps that require spacy.TransitionBasedParser.v2
After finishing the refactor, TransitionBasedParser.v2 should be
provided for backwards compat.
* Add back support for beam parsing to the refactored parser (#10633)
* Add back support for beam parsing
Beam parsing was already implemented as part of the `BeamBatch` class.
This change makes its counterpart `GreedyBatch`. Both classes are hooked
up in `TransitionModel`, selecting `GreedyBatch` when the beam size is
one, or `BeamBatch` otherwise.
* Use kwarg for beam width
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Avoid implicit default for beam_width and beam_density
* Parser.{beam,greedy}_parse: ensure labels are added
* Remove 'deprecated' comments
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Parser `StateC` optimizations (#10746)
* `StateC`: Optimizations
Avoid GIL acquisition in `__init__`
Increase default buffer capacities on init
Reduce C++ exception overhead
* Fix typo
* Replace `set::count` with `set::find`
* Add exception attribute to c'tor
* Remove unused import
* Use a power-of-two value for initial capacity
Use default-insert to init `_heads` and `_unshiftable`
* Merge `cdef` variable declarations and assignments
* Vectorize `example.get_aligned_parses` (#10789)
* `example`: Vectorize `get_aligned_parse`
Rename `numpy` import
* Convert aligned array to lists before returning
* Revert import renaming
* Elide slice arguments when selecting the entire range
* Tagger/morphologizer alignment performance optimizations (#10798)
* `example`: Unwrap `numpy` scalar arrays before passing them to `StringStore.__getitem__`
* `AlignmentArray`: Use native list as staging buffer for offset calculation
* `example`: Vectorize `get_aligned`
* Hoist inner functions out of `get_aligned`
* Replace inline `if..else` clause in assignment statement
* `AlignmentArray`: Use raw indexing into offset and data `numpy` arrays
* `example`: Replace array unique value check with `groupby`
* `example`: Correctly exclude tokens with no alignment in `_get_aligned_vectorized`
Simplify `_get_aligned_non_vectorized`
* `util`: Update `all_equal` docstring
* Explicitly use `int32_t*`
* Restore C CPU inference in the refactored parser (#10747)
* Bring back the C parsing model
The C parsing model is used for CPU inference and is still faster for
CPU inference than the forward pass of the Thinc model.
* Use C sgemm provided by the Ops implementation
* Make tb_framework module Cython, merge in C forward implementation
* TransitionModel: raise in backprop returned from forward_cpu
* Re-enable greedy parse test
* Return transition scores when forward_cpu is used
* Apply suggestions from code review
Import `Model` from `thinc.api`
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Use relative imports in tb_framework
* Don't assume a default for beam_width
* We don't have a direct dependency on BLIS anymore
* Rename forwards to _forward_{fallback,greedy_cpu}
* Require thinc >=8.1.0,<8.2.0
* tb_framework: clean up imports
* Fix return type of _get_seen_mask
* Move up _forward_greedy_cpu
* Style fixes.
* Lower thinc lowerbound to 8.1.0.dev0
* Formatting fix
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Reimplement parser rehearsal function (#10878)
* Reimplement parser rehearsal function
Before the parser refactor, rehearsal was driven by a loop in the
`rehearse` method itself. For each parsing step, the loops would:
1. Get the predictions of the teacher.
2. Get the predictions and backprop function of the student.
3. Compute the loss and backprop into the student.
4. Move the teacher and student forward with the predictions of
the student.
In the refactored parser, we cannot perform search stepwise rehearsal
anymore, since the model now predicts all parsing steps at once.
Therefore, rehearsal is performed in the following steps:
1. Get the predictions of all parsing steps from the student, along
with its backprop function.
2. Get the predictions from the teacher, but use the predictions of
the student to advance the parser while doing so.
3. Compute the loss and backprop into the student.
To support the second step a new method, `advance_with_actions` is
added to `GreedyBatch`, which performs the provided parsing steps.
* tb_framework: wrap upper_W and upper_b in Linear
Thinc's Optimizer cannot handle resizing of existing parameters. Until
it does, we work around this by wrapping the weights/biases of the upper
layer of the parser model in Linear. When the upper layer is resized, we
copy over the existing parameters into a new Linear instance. This does
not trigger an error in Optimizer, because it sees the resized layer as
a new set of parameters.
* Add test for TransitionSystem.apply_actions
* Better FIXME marker
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
* Fixes from Madeesh
* Apply suggestions from Sofie
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Remove useless assignment
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Rename some identifiers in the parser refactor (#10935)
* Rename _parseC to _parse_batch
* tb_framework: prefix many auxiliary functions with underscore
To clearly state the intent that they are private.
* Rename `lower` to `hidden`, `upper` to `output`
* Parser slow test fixup
We don't have TransitionBasedParser.{v1,v2} until we bring it back as a
legacy option.
* Remove last vestiges of PrecomputableAffine
This does not exist anymore as a separate layer.
* ner: re-enable sentence boundary checks
* Re-enable test that works now.
* test_ner: make loss test more strict again
* Remove commented line
* Re-enable some more beam parser tests
* Remove unused _forward_reference function
* Update for CBlas changes in Thinc 8.1.0.dev2
Bump thinc dependency to 8.1.0.dev3.
* Remove references to spacy.TransitionBasedParser.{v1,v2}
Since they will not be offered starting with spaCy v4.
* `tb_framework`: Replace references to `thinc.backends.linalg` with `CBlas`
* dont use get_array_module (#11056) (#11293)
Co-authored-by: kadarakos <kadar.akos@gmail.com>
* Move `thinc.extra.search` to `spacy.pipeline._parser_internals` (#11317)
* `search`: Move from `thinc.extra.search`
Fix NPE in `Beam.__dealloc__`
* `pytest`: Add support for executing Cython tests
Move `search` tests from thinc and patch them to run with `pytest`
* `mypy` fix
* Update comment
* `conftest`: Expose `register_cython_tests`
* Remove unused import
* Move `argmax` impls to new `_parser_utils` Cython module (#11410)
* Parser does not have to be a cdef class anymore
This also fixes validation of the initialization schema.
* Add back spacy.TransitionBasedParser.v2
* Fix a rename that was missed in #10878.
So that rehearsal tests pass.
* Remove module from setup.py that got added during the merge
* Bring back support for `update_with_oracle_cut_size` (#12086)
* Bring back support for `update_with_oracle_cut_size`
This option was available in the pre-refactor parser, but was never
implemented in the refactored parser. This option cuts transition
sequences that are longer than `update_with_oracle_cut` size into
separate sequences that have at most `update_with_oracle_cut`
transitions. The oracle (gold standard) transition sequence is used to
determine the cuts and the initial states for the additional sequences.
Applying this cut makes the batches more homogeneous in the transition
sequence lengths, making forward passes (and as a consequence training)
much faster.
Training time 1000 steps on de_core_news_lg:
- Before this change: 149s
- After this change: 68s
- Pre-refactor parser: 81s
* Fix a rename that was missed in #10878.
So that rehearsal tests pass.
* Apply suggestions from @shadeMe
* Use chained conditional
* Test with update_with_oracle_cut_size={0, 1, 5, 100}
And fix a git that occurs with a cut size of 1.
* Fix up some merge fall out
* Update parser distillation for the refactor
In the old parser, we'd iterate over the transitions in the distill
function and compute the loss/gradients on the go. In the refactored
parser, we first let the student model parse the inputs. Then we'll let
the teacher compute the transition probabilities of the states in the
student's transition sequence. We can then compute the gradients of the
student given the teacher.
* Add back spacy.TransitionBasedParser.v1 references
- Accordion in the architecture docs.
- Test in test_parse, but disabled until we have a spacy-legacy release.
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
Co-authored-by: svlandeg <svlandeg@github.com>
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
Co-authored-by: Madeesh Kannan <shadeMe@users.noreply.github.com>
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
Co-authored-by: kadarakos <kadar.akos@gmail.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`
* fix processing of "auto" in walk_directory
* add check for None
* move AUTO check to convert and fix verification of args
* add specific CLI test with CliRunner
* cleanup
* more cleanup
* update docstring
* Remove old model shortcuts
* Remove error, docs warnings about shortcuts
* Fix import in util
Accidentally deleted the whole import and not just the old part...
* Change universe example to v3 style
* 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
* Update some model loading in Universe
* Add v2 tag to neuralcoref
* Use the spacy-version feature instead of a v2 tag
Co-authored-by: svlandeg <svlandeg@github.com>
If you don't have spacy-transformers installed, but try to use `init
config` with the GPU flag, you'll get an error. The issue is that the
`use_transformers` flag in the config is conflated with the GPU flag,
and then there's an attempt to access transformers config info that may
not exist.
There may be a better way to do this, but this stops the error.
* Support local filesystem remotes for projects
* Fix support for local filesystem remotes for projects
* Use `FluidPath` instead of `Pathy` to support both filesystem and
remote paths
* Create missing parent directories if required for local filesystem
* Add a more general `_file_exists` method to support both `Pathy`,
`Path`, and `smart_open`-compatible URLs
* Add explicit `smart_open` dependency starting with support for
`compression` flag
* Update `pathy` dependency to exclude older versions that aren't
compatible with required `smart_open` version
* Update docs to refer to `Pathy` instead of `smart_open` for project
remotes (technically you can still push to any `smart_open`-compatible
path but you can't pull from them)
* Add tests for local filesystem remotes
* Update pathy for general BlobStat sorting
* Add import
* Remove _file_exists since only Pathy remotes are supported
* Format CLI docs
* Clean up merge
* Update warning, add tests for project requirements check
* Make warning more general for differences between PEP 508 and pip
* Add tests for _check_requirements
* Parameterize test
