* Add sent_starts to GoldParse
* Add SentTagger pipeline component
Add `SentTagger` pipeline component as a subclass of `Tagger`.
* Model reduces default parameters from `Tagger` to be small and fast
* Hard-coded set of two labels:
* S (1): token at beginning of sentence
* I (0): all other sentence positions
* Sets `token.sent_start` values
* Add sentence segmentation to Scorer
Report `sent_p/r/f` for sentence boundaries, which may be provided by
various pipeline components.
* Add sentence segmentation to CLI evaluate
* Add senttagger metrics/scoring to train CLI
* Rename SentTagger to SentenceRecognizer
* Add SentenceRecognizer to spacy.pipes imports
* Add SentenceRecognizer serialization test
* Shorten component name to sentrec
* Remove duplicates from train CLI output metrics
* Switch to train_dataset() function in train CLI
* Fixes for pipe() methods in pipeline components
* Don't clobber `examples` variable with `as_example` in pipe() methods
* Remove unnecessary traversals of `examples`
* Update Parser.pipe() for Examples
* Add `as_examples` kwarg to `pipe()` with implementation to return
`Example`s
* Accept `Doc` or `Example` in `pipe()` with `_get_doc()` (copied from
`Pipe`)
* Fixes to Example implementation in spacy.gold
* Move `make_projective` from an attribute of Example to an argument of
`Example.get_gold_parses()`
* Head of 0 are not treated as unset
* Unset heads are set to self rather than `None` (which causes problems
while projectivizing)
* Check for `Doc` (not just not `None`) when creating GoldParses for
pre-merged example
* Don't clobber `examples` variable in `iter_gold_docs()`
* Add/modify gold tests for handling projectivity
* In JSON roundtrip compare results from `dev_dataset` rather than
`train_dataset` to avoid projectivization (and other potential
modifications)
* Add test for projective train vs. nonprojective dev versions of the
same `Doc`
* Handle ignore_misaligned as arg rather than attr
Move `ignore_misaligned` from an attribute of `Example` to an argument
to `Example.get_gold_parses()`, which makes it parallel to
`make_projective`.
Add test with old and new align that checks whether `ignore_misaligned`
errors are raised as expected (only for new align).
* Remove unused attrs from gold.pxd
Remove `ignore_misaligned` and `make_projective` from `gold.pxd`
* Restructure Example with merged sents as default
An `Example` now includes a single `TokenAnnotation` that includes all
the information from one `Doc` (=JSON `paragraph`). If required, the
individual sentences can be returned as a list of examples with
`Example.split_sents()` with no raw text available.
* Input/output a single `Example.token_annotation`
* Add `sent_starts` to `TokenAnnotation` to handle sentence boundaries
* Replace `Example.merge_sents()` with `Example.split_sents()`
* Modify components to use a single `Example.token_annotation`
* Pipeline components
* conllu2json converter
* Rework/rename `add_token_annotation()` and `add_doc_annotation()` to
`set_token_annotation()` and `set_doc_annotation()`, functions that set
rather then appending/extending.
* Rename `morphology` to `morphs` in `TokenAnnotation` and `GoldParse`
* Add getters to `TokenAnnotation` to supply default values when a given
attribute is not available
* `Example.get_gold_parses()` in `spacy.gold._make_golds()` is only
applied on single examples, so the `GoldParse` is returned saved in the
provided `Example` rather than creating a new `Example` with no other
internal annotation
* Update tests for API changes and `merge_sents()` vs. `split_sents()`
* Refer to Example.goldparse in iter_gold_docs()
Use `Example.goldparse` in `iter_gold_docs()` instead of `Example.gold`
because a `None` `GoldParse` is generated with ignore_misaligned and
generating it on-the-fly can raise an unwanted AlignmentError
* Fix make_orth_variants()
Fix bug in make_orth_variants() related to conversion from multiple to
one TokenAnnotation per Example.
* Add basic test for make_orth_variants()
* Replace try/except with conditionals
* Replace default morph value with set
* Switch to train_dataset() function in train CLI
* Fixes for pipe() methods in pipeline components
* Don't clobber `examples` variable with `as_example` in pipe() methods
* Remove unnecessary traversals of `examples`
* Update Parser.pipe() for Examples
* Add `as_examples` kwarg to `pipe()` with implementation to return
`Example`s
* Accept `Doc` or `Example` in `pipe()` with `_get_doc()` (copied from
`Pipe`)
* Fixes to Example implementation in spacy.gold
* Move `make_projective` from an attribute of Example to an argument of
`Example.get_gold_parses()`
* Head of 0 are not treated as unset
* Unset heads are set to self rather than `None` (which causes problems
while projectivizing)
* Check for `Doc` (not just not `None`) when creating GoldParses for
pre-merged example
* Don't clobber `examples` variable in `iter_gold_docs()`
* Add/modify gold tests for handling projectivity
* In JSON roundtrip compare results from `dev_dataset` rather than
`train_dataset` to avoid projectivization (and other potential
modifications)
* Add test for projective train vs. nonprojective dev versions of the
same `Doc`
* Handle ignore_misaligned as arg rather than attr
Move `ignore_misaligned` from an attribute of `Example` to an argument
to `Example.get_gold_parses()`, which makes it parallel to
`make_projective`.
Add test with old and new align that checks whether `ignore_misaligned`
errors are raised as expected (only for new align).
* Remove unused attrs from gold.pxd
Remove `ignore_misaligned` and `make_projective` from `gold.pxd`
* Refer to Example.goldparse in iter_gold_docs()
Use `Example.goldparse` in `iter_gold_docs()` instead of `Example.gold`
because a `None` `GoldParse` is generated with ignore_misaligned and
generating it on-the-fly can raise an unwanted AlignmentError
* Update test for ignore_misaligned
* Generalize handling of tokenizer special cases
Handle tokenizer special cases more generally by using the Matcher
internally to match special cases after the affix/token_match
tokenization is complete.
Instead of only matching special cases while processing balanced or
nearly balanced prefixes and suffixes, this recognizes special cases in
a wider range of contexts:
* Allows arbitrary numbers of prefixes/affixes around special cases
* Allows special cases separated by infixes
Existing tests/settings that couldn't be preserved as before:
* The emoticon '")' is no longer a supported special case
* The emoticon ':)' in "example:)" is a false positive again
When merged with #4258 (or the relevant cache bugfix), the affix and
token_match properties should be modified to flush and reload all
special cases to use the updated internal tokenization with the Matcher.
* Remove accidentally added test case
* Really remove accidentally added test
* Reload special cases when necessary
Reload special cases when affixes or token_match are modified. Skip
reloading during initialization.
* Update error code number
* Fix offset and whitespace in Matcher special cases
* Fix offset bugs when merging and splitting tokens
* Set final whitespace on final token in inserted special case
* Improve cache flushing in tokenizer
* Separate cache and specials memory (temporarily)
* Flush cache when adding special cases
* Repeated `self._cache = PreshMap()` and `self._specials = PreshMap()`
are necessary due to this bug:
https://github.com/explosion/preshed/issues/21
* Remove reinitialized PreshMaps on cache flush
* Update UD bin scripts
* Update imports for `bin/`
* Add all currently supported languages
* Update subtok merger for new Matcher validation
* Modify blinded check to look at tokens instead of lemmas (for corpora
with tokens but not lemmas like Telugu)
* Use special Matcher only for cases with affixes
* Reinsert specials cache checks during normal tokenization for special
cases as much as possible
* Additionally include specials cache checks while splitting on infixes
* Since the special Matcher needs consistent affix-only tokenization
for the special cases themselves, introduce the argument
`with_special_cases` in order to do tokenization with or without
specials cache checks
* After normal tokenization, postprocess with special cases Matcher for
special cases containing affixes
* Replace PhraseMatcher with Aho-Corasick
Replace PhraseMatcher with the Aho-Corasick algorithm over numpy arrays
of the hash values for the relevant attribute. The implementation is
based on FlashText.
The speed should be similar to the previous PhraseMatcher. It is now
possible to easily remove match IDs and matches don't go missing with
large keyword lists / vocabularies.
Fixes#4308.
* Restore support for pickling
* Fix internal keyword add/remove for numpy arrays
* Add test for #4248, clean up test
* Improve efficiency of special cases handling
* Use PhraseMatcher instead of Matcher
* Improve efficiency of merging/splitting special cases in document
* Process merge/splits in one pass without repeated token shifting
* Merge in place if no splits
* Update error message number
* Remove UD script modifications
Only used for timing/testing, should be a separate PR
* Remove final traces of UD script modifications
* Update UD bin scripts
* Update imports for `bin/`
* Add all currently supported languages
* Update subtok merger for new Matcher validation
* Modify blinded check to look at tokens instead of lemmas (for corpora
with tokens but not lemmas like Telugu)
* Add missing loop for match ID set in search loop
* Remove cruft in matching loop for partial matches
There was a bit of unnecessary code left over from FlashText in the
matching loop to handle partial token matches, which we don't have with
PhraseMatcher.
* Replace dict trie with MapStruct trie
* Fix how match ID hash is stored/added
* Update fix for match ID vocab
* Switch from map_get_unless_missing to map_get
* Switch from numpy array to Token.get_struct_attr
Access token attributes directly in Doc instead of making a copy of the
relevant values in a numpy array.
Add unsatisfactory warning for hash collision with reserved terminal
hash key. (Ideally it would change the reserved terminal hash and redo
the whole trie, but for now, I'm hoping there won't be collisions.)
* Restructure imports to export find_matches
* Implement full remove()
Remove unnecessary trie paths and free unused maps.
Parallel to Matcher, raise KeyError when attempting to remove a match ID
that has not been added.
* Switch to PhraseMatcher.find_matches
* Switch to local cdef functions for span filtering
* Switch special case reload threshold to variable
Refer to variable instead of hard-coded threshold
* Move more of special case retokenize to cdef nogil
Move as much of the special case retokenization to nogil as possible.
* Rewrap sort as stdsort for OS X
* Rewrap stdsort with specific types
* Switch to qsort
* Fix merge
* Improve cmp functions
* Fix realloc
* Fix realloc again
* Initialize span struct while retokenizing
* Temporarily skip retokenizing
* Revert "Move more of special case retokenize to cdef nogil"
This reverts commit 0b7e52c797.
* Revert "Switch to qsort"
This reverts commit a98d71a942.
* Fix specials check while caching
* Modify URL test with emoticons
The multiple suffix tests result in the emoticon `:>`, which is now
retokenized into one token as a special case after the suffixes are
split off.
* Refactor _apply_special_cases()
* Use cdef ints for span info used in multiple spots
* Modify _filter_special_spans() to prefer earlier
Parallel to #4414, modify _filter_special_spans() so that the earlier
span is preferred for overlapping spans of the same length.
* Replace MatchStruct with Entity
Replace MatchStruct with Entity since the existing Entity struct is
nearly identical.
* Replace Entity with more general SpanC
* Replace MatchStruct with SpanC
* Add error in debug-data if no dev docs are available (see #4575)
* Update azure-pipelines.yml
* Revert "Update azure-pipelines.yml"
This reverts commit ed1060cf59.
* Use latest wasabi
* Reorganise install_requires
* add dframcy to universe.json (#4580)
* Update universe.json [ci skip]
* Fix multiprocessing for as_tuples=True (#4582)
* Fix conllu script (#4579)
* force extensions to avoid clash between example scripts
* fix arg order and default file encoding
* add example config for conllu script
* newline
* move extension definitions to main function
* few more encodings fixes
* Add load_from_docbin example [ci skip]
TODO: upload the file somewhere
* Update README.md
* Add warnings about 3.8 (resolves#4593) [ci skip]
* Fixed typo: Added space between "recognize" and "various" (#4600)
* Fix DocBin.merge() example (#4599)
* Replace function registries with catalogue (#4584)
* Replace functions registries with catalogue
* Update __init__.py
* Fix test
* Revert unrelated flag [ci skip]
* Bugfix/dep matcher issue 4590 (#4601)
* add contributor agreement for prilopes
* add test for issue #4590
* fix on_match params for DependencyMacther (#4590)
* Minor updates to language example sentences (#4608)
* Add punctuation to Spanish example sentences
* Combine multilanguage examples for lang xx
* Add punctuation to nb examples
* Always realloc to a larger size
Avoid potential (unlikely) edge case and cymem error seen in #4604.
* Add error in debug-data if no dev docs are available (see #4575)
* Update debug-data for GoldCorpus / Example
* Ignore None label in misaligned NER data
* Add error in debug-data if no dev docs are available (see #4575)
* Update debug-data for GoldCorpus / Example
* Ignore None label in misaligned NER data
* OrigAnnot class instead of gold.orig_annot list of zipped tuples
* from_orig to replace from_annot_tuples
* rename to RawAnnot
* some unit tests for GoldParse creation and internal format
* removing orig_annot and switching to lists instead of tuple
* rewriting tuples to use RawAnnot (+ debug statements, WIP)
* fix pop() changing the data
* small fixes
* pop-append fixes
* return RawAnnot for existing GoldParse to have uniform interface
* clean up imports
* fix merge_sents
* add unit test for 4402 with new structure (not working yet)
* introduce DocAnnot
* typo fixes
* add unit test for merge_sents
* rename from_orig to from_raw
* fixing unit tests
* fix nn parser
* read_annots to produce text, doc_annot pairs
* _make_golds fix
* rename golds_to_gold_annots
* small fixes
* fix encoding
* have golds_to_gold_annots use DocAnnot
* missed a spot
* merge_sents as function in DocAnnot
* allow specifying only part of the token-level annotations
* refactor with Example class + underlying dicts
* pipeline components to work with Example objects (wip)
* input checking
* fix yielding
* fix calls to update
* small fixes
* fix scorer unit test with new format
* fix kwargs order
* fixes for ud and conllu scripts
* fix reading data for conllu script
* add in proper errors (not fixed numbering yet to avoid merge conflicts)
* fixing few more small bugs
* fix EL script
* Add arch for MishWindowEncoder
* Support mish in tok2vec and conv window >=2
* Pass new tok2vec settings from parser
* Syntax error
* Fix tok2vec setting
* Fix registration of MishWindowEncoder
* Fix receptive field setting
* Fix mish arch
* Pass more options from parser
* Support more tok2vec options in pretrain
* Require thinc 7.3
* Add docs [ci skip]
* Require thinc 7.3.0.dev0 to run CI
* Run black
* Fix typo
* Update Thinc version
Co-authored-by: Ines Montani <ines@ines.io>
* Flag to ignore examples with mismatched raw/gold text
After #4525, we're seeing some alignment failures on our OntoNotes data. I think we actually have fixes for most of these cases.
In general it's better to fix the data, but it seems good to allow the GoldCorpus class to just skip cases where the raw text doesn't
match up to the gold words. I think previously we were silently ignoring these cases.
* Try to fix test on Python 2.7
* Error for ill-formed input to iob_to_biluo()
Check for empty label in iob_to_biluo(), which can result from
ill-formed input.
* Check for empty NER label in debug-data
* fix overflow error on windows
* more documentation & logging fixes
* md fix
* 3 different limit parameters to play with execution time
* bug fixes directory locations
* small fixes
* exclude dev test articles from prior probabilities stats
* small fixes
* filtering wikidata entities, removing numeric and meta items
* adding aliases from wikidata also to the KB
* fix adding WD aliases
* adding also new aliases to previously added entities
* fixing comma's
* small doc fixes
* adding subclassof filtering
* append alias functionality in KB
* prevent appending the same entity-alias pair
* fix for appending WD aliases
* remove date filter
* remove unnecessary import
* small corrections and reformatting
* remove WD aliases for now (too slow)
* removing numeric entities from training and evaluation
* small fixes
* shortcut during prediction if there is only one candidate
* add counts and fscore logging, remove FP NER from evaluation
* fix entity_linker.predict to take docs instead of single sentences
* remove enumeration sentences from the WP dataset
* entity_linker.update to process full doc instead of single sentence
* spelling corrections and dump locations in readme
* NLP IO fix
* reading KB is unnecessary at the end of the pipeline
* small logging fix
* remove empty files
* Only import pkg_resources where it's needed
Apparently it's really slow
* Use importlib_metadata for entry points
* Revert "Only import pkg_resources where it's needed"
This reverts commit 5ed8c03afa.
* Revert "Revert "Only import pkg_resources where it's needed""
This reverts commit 8b30b57957.
* Revert "Use importlib_metadata for entry points"
This reverts commit 9f071f5c40.
* Revert "Revert "Use importlib_metadata for entry points""
This reverts commit 02e12a17ec.
* Skip test that weirdly hangs
* Fix hanging test by using global
* Allow vectors name to be specified in init-model
* Document --vectors-name argument to init-model
* Update website/docs/api/cli.md
Co-Authored-By: Ines Montani <ines@ines.io>
* Add doc.cats to spacy.gold at the paragraph level
Support `doc.cats` as `"cats": [{"label": string, "value": number}]` in
the spacy JSON training format at the paragraph level.
* `spacy.gold.docs_to_json()` writes `docs.cats`
* `GoldCorpus` reads in cats in each `GoldParse`
* Update instances of gold_tuples to handle cats
Update iteration over gold_tuples / gold_parses to handle addition of
cats at the paragraph level.
* Add textcat to train CLI
* Add textcat options to train CLI
* Add textcat labels in `TextCategorizer.begin_training()`
* Add textcat evaluation to `Scorer`:
* For binary exclusive classes with provided label: F1 for label
* For 2+ exclusive classes: F1 macro average
* For multilabel (not exclusive): ROC AUC macro average (currently
relying on sklearn)
* Provide user info on textcat evaluation settings, potential
incompatibilities
* Provide pipeline to Scorer in `Language.evaluate` for textcat config
* Customize train CLI output to include only metrics relevant to current
pipeline
* Add textcat evaluation to evaluate CLI
* Fix handling of unset arguments and config params
Fix handling of unset arguments and model confiug parameters in Scorer
initialization.
* Temporarily add sklearn requirement
* Remove sklearn version number
* Improve Scorer handling of models without textcats
* Fixing Scorer handling of models without textcats
* Update Scorer output for python 2.7
* Modify inf in Scorer for python 2.7
* Auto-format
Also make small adjustments to make auto-formatting with black easier and produce nicer results
* Move error message to Errors
* Update documentation
* Add cats to annotation JSON format [ci skip]
* Fix tpl flag and docs [ci skip]
* Switch to internal roc_auc_score
Switch to internal `roc_auc_score()` adapted from scikit-learn.
* Add AUCROCScore tests and improve errors/warnings
* Add tests for AUCROCScore and roc_auc_score
* Add missing error for only positive/negative values
* Remove unnecessary warnings and errors
* Make reduced roc_auc_score functions private
Because most of the checks and warnings have been stripped for the
internal functions and access is only intended through `ROCAUCScore`,
make the functions for roc_auc_score adapted from scikit-learn private.
* Check that data corresponds with multilabel flag
Check that the training instances correspond with the multilabel flag,
adding the multilabel flag if required.
* Add textcat score to early stopping check
* Add more checks to debug-data for textcat
* Add example training data for textcat
* Add more checks to textcat train CLI
* Check configuration when extending base model
* Fix typos
* Update textcat example data
* Provide licensing details and licenses for data
* Remove two labels with no positive instances from jigsaw-toxic-comment
data.
Co-authored-by: Ines Montani <ines@ines.io>
* Updates/bugfixes for NER/IOB converters
* Converter formats `ner` and `iob` use autodetect to choose a converter if
possible
* `iob2json` is reverted to handle sentence-per-line data like
`word1|pos1|ent1 word2|pos2|ent2`
* Fix bug in `merge_sentences()` so the second sentence in each batch isn't
skipped
* `conll_ner2json` is made more general so it can handle more formats with
whitespace-separated columns
* Supports all formats where the first column is the token and the final
column is the IOB tag; if present, the second column is the POS tag
* As in CoNLL 2003 NER, blank lines separate sentences, `-DOCSTART- -X- O O`
separates documents
* Add option for segmenting sentences (new flag `-s`)
* Parser-based sentence segmentation with a provided model, otherwise with
sentencizer (new option `-b` to specify model)
* Can group sentences into documents with `n_sents` as long as sentence
segmentation is available
* Only applies automatic segmentation when there are no existing delimiters
in the data
* Provide info about settings applied during conversion with warnings and
suggestions if settings conflict or might not be not optimal.
* Add tests for common formats
* Add '(default)' back to docs for -c auto
* Add document count back to output
* Revert changes to converter output message
* Use explicit tabs in convert CLI test data
* Adjust/add messages for n_sents=1 default
* Add sample NER data to training examples
* Update README
* Add links in docs to example NER data
* Define msg within converters
* Prevent subtok label if not learning tokens
The parser introduces the subtok label to mark tokens that should be
merged during post-processing. Previously this happened even if we did
not have the --learn-tokens flag set. This patch passes the config
through to the parser, to prevent the problem.
* Make merge_subtokens a parser post-process if learn_subtokens
* Fix train script
* Add test for 3830: subtok problem
* Fix handlign of non-subtok in parser training
* Extending debug-data with dependency checks, etc.
* Modify debug-data to load with GoldCorpus to iterate over .json/.jsonl
files within directories
* Add GoldCorpus iterator train_docs_without_preprocessing to load
original train docs without shuffling and projectivizing
* Report number of misaligned tokens
* Add more dependency checks and messages
* Update spacy/cli/debug_data.py
Co-Authored-By: Ines Montani <ines@ines.io>
* Fixed conflict
* Move counts to _compile_gold()
* Move all dependency nonproj/sent/head/cycle counting to
_compile_gold()
* Unclobber previous merges
* Update variable names
* Update more variable names, fix misspelling
* Don't clobber loading error messages
* Only warn about misaligned tokens if present
* Update pretrain to prevent unintended overwriting of weight files for #3859
* Add '--epoch-start' to pretrain docs
* Add mising pretrain arguments to bash example
* Update doc tag for v2.1.5
* Add error to `get_vectors_loss` for unsupported loss function of `pretrain`
* Add missing "--loss-func" argument to pretrain docs. Update pretrain plac annotations to match docs.
* Add missing quotation marks
* Add check for empty input file to CLI pretrain
* Raise error if JSONL is not a dict or contains neither `tokens` nor `text` key
* Skip empty values for correct pretrain keys and log a counter as warning
* Add tests for CLI pretrain core function make_docs.
* Add a short hint for the `tokens` key to the CLI pretrain docs
* Add success message to CLI pretrain
* Update model loading to fix the tests
* Skip empty values and do not create docs out of it
<!--- Provide a general summary of your changes in the title. -->
When using `spacy pretrain`, the model is saved only after every epoch. But each epoch can be very big since `pretrain` is used for language modeling tasks. So I added a `--save-every` option in the CLI to save after every `--save-every` batches.
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
To test...
Save this file to `sample_sents.jsonl`
```
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
{"text": "hello there."}
```
Then run `--save-every 2` when pretraining.
```bash
spacy pretrain sample_sents.jsonl en_core_web_md here -nw 1 -bs 1 -i 10 --save-every 2
```
And it should save the model to the `here/` folder after every 2 batches. The models that are saved during an epoch will have a `.temp` appended to the save name.
At the end the training, you should see these files (`ls here/`):
```bash
config.json model2.bin model5.bin model8.bin
log.jsonl model2.temp.bin model5.temp.bin model8.temp.bin
model0.bin model3.bin model6.bin model9.bin
model0.temp.bin model3.temp.bin model6.temp.bin model9.temp.bin
model1.bin model4.bin model7.bin
model1.temp.bin model4.temp.bin model7.temp.bin
```
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
This is a new feature to `spacy pretrain`.
🌵 **Unfortunately, I haven't been able to test this because compiling from source is not working (cythonize error).**
```
Processing matcher.pyx
[Errno 2] No such file or directory: '/Users/mwu/github/spaCy/spacy/matcher.pyx'
Traceback (most recent call last):
File "/Users/mwu/github/spaCy/bin/cythonize.py", line 169, in <module>
run(args.root)
File "/Users/mwu/github/spaCy/bin/cythonize.py", line 158, in run
process(base, filename, db)
File "/Users/mwu/github/spaCy/bin/cythonize.py", line 124, in process
preserve_cwd(base, process_pyx, root + ".pyx", root + ".cpp")
File "/Users/mwu/github/spaCy/bin/cythonize.py", line 87, in preserve_cwd
func(*args)
File "/Users/mwu/github/spaCy/bin/cythonize.py", line 63, in process_pyx
raise Exception("Cython failed")
Exception: Cython failed
Traceback (most recent call last):
File "setup.py", line 276, in <module>
setup_package()
File "setup.py", line 209, in setup_package
generate_cython(root, "spacy")
File "setup.py", line 132, in generate_cython
raise RuntimeError("Running cythonize failed")
RuntimeError: Running cythonize failed
```
Edit: Fixed! after deleting all `.cpp` files: `find spacy -name "*.cpp" | xargs rm`
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Add early stopping
* Add return_score option to evaluate
* Fix missing str to path conversion
* Fix import + old python compatibility
* Fix bad beam_width setting during cpu evaluation in spacy train with gpu option turned on
Add and document CLI options for batch size, max doc length, min doc length for `spacy pretrain`.
Also improve CLI output.
Closes#3216
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* merging conllu/conll and conllubio scripts
* tabs to spaces
* removing conllubio2json from converters/__init__.py
* Move not-really-CLI tests to misc
* Add converter test using no-ud data
* Fix test I broke
* removing include_biluo parameter
* fixing read_conllx
* remove include_biluo from convert.py
* Add custom MatchPatternError
* Improve validators and add validation option to Matcher
* Adjust formatting
* Never validate in Matcher within PhraseMatcher
If we do decide to make validate default to True, the PhraseMatcher's Matcher shouldn't ever validate. Here, we create the patterns automatically anyways (and it's currently unclear whether the validation has performance impacts at a very large scale).
* running UD eval
* printing timing of tokenizer: tokens per second
* timing of default English model
* structured output and parameterization to compare different runs
* additional flag to allow evaluation without parsing info
* printing verbose log of errors for manual inspection
* printing over- and undersegmented cases (and combo's)
* add under and oversegmented numbers to Score and structured output
* print high-freq over/under segmented words and word shapes
* printing examples as part of the structured output
* print the results to file
* batch run of different models and treebanks per language
* cleaning up code
* commandline script to process all languages in spaCy & UD
* heuristic to remove blinded corpora and option to run one single best per language
* pathlib instead of os for file paths
* Try to implement cosine loss
This one seems to be correct? Still unsure, but it performs okay
* Try to implement the von Mises-Fisher loss
This one's definitely not right yet.
The new spacy pretrain command implemented BERT/ULMFit/etc-like transfer learning, using our Language Modelling with Approximate Outputs version of BERT's cloze task. Pretraining is convenient, but in some ways it's a bit of a strange solution. All we're doing is initialising the weights. At the same time, we're putting a lot of work into our optimisation so that it's less sensitive to initial conditions, and more likely to find good optima. I discuss this a bit in the pseudo-rehearsal blog post: https://explosion.ai/blog/pseudo-rehearsal-catastrophic-forgetting
Support semi-supervised learning in spacy train
One obvious way to improve these pretraining methods is to do multi-task learning, instead of just transfer learning. This has been shown to work very well: https://arxiv.org/pdf/1809.08370.pdf . This patch makes it easy to do this sort of thing.
Add a new argument to spacy train, --raw-text. This takes a jsonl file with unlabelled data that can be used in arbitrary ways to do semi-supervised learning.
Add a new method to the Language class and to pipeline components, .rehearse(). This is like .update(), but doesn't expect GoldParse objects. It takes a batch of Doc objects, and performs an update on some semi-supervised objective.
Move the BERT-LMAO objective out from spacy/cli/pretrain.py into spacy/_ml.py, so we can create a new pipeline component, ClozeMultitask. This can be specified as a parser or NER multitask in the spacy train command. Example usage:
python -m spacy train en ./tmp ~/data/en-core-web/train/nw.json ~/data/en-core-web/dev/nw.json --pipeline parser --raw-textt ~/data/unlabelled/reddit-100k.jsonl --vectors en_vectors_web_lg --parser-multitasks cloze
Implement rehearsal methods for pipeline components
The new --raw-text argument and nlp.rehearse() method also gives us a good place to implement the the idea in the pseudo-rehearsal blog post in the parser. This works as follows:
Add a new nlp.resume_training() method. This allocates copies of pre-trained models in the pipeline, setting things up for the rehearsal updates. It also returns an optimizer object. This also greatly reduces confusion around the nlp.begin_training() method, which randomises the weights, making it not suitable for adding new labels or otherwise fine-tuning a pre-trained model.
Implement rehearsal updates on the Parser class, making it available for the dependency parser and NER. During rehearsal, the initial model is used to supervise the model being trained. The current model is asked to match the predictions of the initial model on some data. This minimises catastrophic forgetting, by keeping the model's predictions close to the original. See the blog post for details.
Implement rehearsal updates for tagger
Implement rehearsal updates for text categoriz
* Add todo
* Auto-format
* Update wasabi pin
* Format training results with wasabi
* Remove loading animation from model saving
Currently behaves weirdly
* Inline messages
* Remove unnecessary path2str
Already taken care of by printer
* Inline messages in CLI
* Remove unused function
* Move loading indicator into loading function
* Check for invalid whitespace entities
Remove hacks and wrappers, keep code in sync across our libraries and move spaCy a few steps closer to only depending on packages with binary wheels 🎉
See here: https://github.com/explosion/srsly
Serialization is hard, especially across Python versions and multiple platforms. After dealing with many subtle bugs over the years (encodings, locales, large files) our libraries like spaCy and Prodigy have steadily grown a number of utility functions to wrap the multiple serialization formats we need to support (especially json, msgpack and pickle). These wrapping functions ended up duplicated across our codebases, so we wanted to put them in one place.
At the same time, we noticed that having a lot of small dependencies was making maintainence harder, and making installation slower. To solve this, we've made srsly standalone, by including the component packages directly within it. This way we can provide all the serialization utilities we need in a single binary wheel.
srsly currently includes forks of the following packages:
ujson
msgpack
msgpack-numpy
cloudpickle
* WIP: replace json/ujson with srsly
* Replace ujson in examples
Use regular json instead of srsly to make code easier to read and follow
* Update requirements
* Fix imports
* Fix typos
* Replace msgpack with srsly
* Fix warning
* Support nowrap setting in util.prints
* Tidy up and fix whitespace
* Simplify script and use read_jsonl helper
* Add JSON schemas (see #2928)
* Deprecate Doc.print_tree
Will be replaced with Doc.to_json, which will produce a unified format
* Add Doc.to_json() method (see #2928)
Converts Doc objects to JSON using the same unified format as the training data. Method also supports serializing selected custom attributes in the doc._. space.
* Remove outdated test
* Add write_json and write_jsonl helpers
* WIP: Update spacy train
* Tidy up spacy train
* WIP: Use wasabi for formatting
* Add GoldParse helpers for JSON format
* WIP: add debug-data command
* Fix typo
* Add missing import
* Update wasabi pin
* Add missing import
* 💫 Refactor CLI (#2943)
To be merged into #2932.
## Description
- [x] refactor CLI To use [`wasabi`](https://github.com/ines/wasabi)
- [x] use [`black`](https://github.com/ambv/black) for auto-formatting
- [x] add `flake8` config
- [x] move all messy UD-related scripts to `cli.ud`
- [x] make converters function that take the opened file and return the converted data (instead of having them handle the IO)
### Types of change
enhancement
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Update wasabi pin
* Delete old test
* Update errors
* Fix typo
* Tidy up and format remaining code
* Fix formatting
* Improve formatting of messages
* Auto-format remaining code
* Add tok2vec stuff to spacy.train
* Fix typo
* Update wasabi pin
* Fix path checks for when train() is called as function
* Reformat and tidy up pretrain script
* Update argument annotations
* Raise error if model language doesn't match lang
* Document new train command
* Create aryaprabhudesai.md (#2681)
* Update _install.jade (#2688)
Typo fix: "models" -> "model"
* Add FAC to spacy.explain (resolves#2706)
* Remove docstrings for deprecated arguments (see #2703)
* When calling getoption() in conftest.py, pass a default option (#2709)
* When calling getoption() in conftest.py, pass a default option
This is necessary to allow testing an installed spacy by running:
pytest --pyargs spacy
* Add contributor agreement
* update bengali token rules for hyphen and digits (#2731)
* Less norm computations in token similarity (#2730)
* Less norm computations in token similarity
* Contributor agreement
* Remove ')' for clarity (#2737)
Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know.
* added contributor agreement for mbkupfer (#2738)
* Basic support for Telugu language (#2751)
* Lex _attrs for polish language (#2750)
* Signed spaCy contributor agreement
* Added polish version of english lex_attrs
* Introduces a bulk merge function, in order to solve issue #653 (#2696)
* Fix comment
* Introduce bulk merge to increase performance on many span merges
* Sign contributor agreement
* Implement pull request suggestions
* Describe converters more explicitly (see #2643)
* Add multi-threading note to Language.pipe (resolves#2582) [ci skip]
* Fix formatting
* Fix dependency scheme docs (closes#2705) [ci skip]
* Don't set stop word in example (closes#2657) [ci skip]
* Add words to portuguese language _num_words (#2759)
* Add words to portuguese language _num_words
* Add words to portuguese language _num_words
* Update Indonesian model (#2752)
* adding e-KTP in tokenizer exceptions list
* add exception token
* removing lines with containing space as it won't matter since we use .split() method in the end, added new tokens in exception
* add tokenizer exceptions list
* combining base_norms with norm_exceptions
* adding norm_exception
* fix double key in lemmatizer
* remove unused import on punctuation.py
* reformat stop_words to reduce number of lines, improve readibility
* updating tokenizer exception
* implement is_currency for lang/id
* adding orth_first_upper in tokenizer_exceptions
* update the norm_exception list
* remove bunch of abbreviations
* adding contributors file
* Fixed spaCy+Keras example (#2763)
* bug fixes in keras example
* created contributor agreement
* Adding French hyphenated first name (#2786)
* Fix typo (closes#2784)
* Fix typo (#2795) [ci skip]
Fixed typo on line 6 "regcognizer --> recognizer"
* Adding basic support for Sinhala language. (#2788)
* adding Sinhala language package, stop words, examples and lex_attrs.
* Adding contributor agreement
* Updating contributor agreement
* Also include lowercase norm exceptions
* Fix error (#2802)
* Fix error
ValueError: cannot resize an array that references or is referenced
by another array in this way. Use the resize function
* added spaCy Contributor Agreement
* Add charlax's contributor agreement (#2805)
* agreement of contributor, may I introduce a tiny pl languge contribution (#2799)
* Contributors agreement
* Contributors agreement
* Contributors agreement
* Add jupyter=True to displacy.render in documentation (#2806)
* Revert "Also include lowercase norm exceptions"
This reverts commit 70f4e8adf3.
* Remove deprecated encoding argument to msgpack
* Set up dependency tree pattern matching skeleton (#2732)
* Fix bug when too many entity types. Fixes#2800
* Fix Python 2 test failure
* Require older msgpack-numpy
* Restore encoding arg on msgpack-numpy
* Try to fix version pin for msgpack-numpy
* Update Portuguese Language (#2790)
* Add words to portuguese language _num_words
* Add words to portuguese language _num_words
* Portuguese - Add/remove stopwords, fix tokenizer, add currency symbols
* Extended punctuation and norm_exceptions in the Portuguese language
* Correct error in spacy universe docs concerning spacy-lookup (#2814)
* Update Keras Example for (Parikh et al, 2016) implementation (#2803)
* bug fixes in keras example
* created contributor agreement
* baseline for Parikh model
* initial version of parikh 2016 implemented
* tested asymmetric models
* fixed grevious error in normalization
* use standard SNLI test file
* begin to rework parikh example
* initial version of running example
* start to document the new version
* start to document the new version
* Update Decompositional Attention.ipynb
* fixed calls to similarity
* updated the README
* import sys package duh
* simplified indexing on mapping word to IDs
* stupid python indent error
* added code from https://github.com/tensorflow/tensorflow/issues/3388 for tf bug workaround
* Fix typo (closes#2815) [ci skip]
* Update regex version dependency
* Set version to 2.0.13.dev3
* Skip seemingly problematic test
* Remove problematic test
* Try previous version of regex
* Revert "Remove problematic test"
This reverts commit bdebbef455.
* Unskip test
* Try older version of regex
* 💫 Update training examples and use minibatching (#2830)
<!--- Provide a general summary of your changes in the title. -->
## Description
Update the training examples in `/examples/training` to show usage of spaCy's `minibatch` and `compounding` helpers ([see here](https://spacy.io/usage/training#tips-batch-size) for details). The lack of batching in the examples has caused some confusion in the past, especially for beginners who would copy-paste the examples, update them with large training sets and experienced slow and unsatisfying results.
### Types of change
enhancements
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Visual C++ link updated (#2842) (closes#2841) [ci skip]
* New landing page
* Add contribution agreement
* Correcting lang/ru/examples.py (#2845)
* Correct some grammatical inaccuracies in lang\ru\examples.py; filled Contributor Agreement
* Correct some grammatical inaccuracies in lang\ru\examples.py
* Move contributor agreement to separate file
* Set version to 2.0.13.dev4
* Add Persian(Farsi) language support (#2797)
* Also include lowercase norm exceptions
* Remove in favour of https://github.com/explosion/spaCy/graphs/contributors
* Rule-based French Lemmatizer (#2818)
<!--- Provide a general summary of your changes in the title. -->
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
Add a rule-based French Lemmatizer following the english one and the excellent PR for [greek language optimizations](https://github.com/explosion/spaCy/pull/2558) to adapt the Lemmatizer class.
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
- Lemma dictionary used can be found [here](http://infolingu.univ-mlv.fr/DonneesLinguistiques/Dictionnaires/telechargement.html), I used the XML version.
- Add several files containing exhaustive list of words for each part of speech
- Add some lemma rules
- Add POS that are not checked in the standard Lemmatizer, i.e PRON, DET, ADV and AUX
- Modify the Lemmatizer class to check in lookup table as a last resort if POS not mentionned
- Modify the lemmatize function to check in lookup table as a last resort
- Init files are updated so the model can support all the functionalities mentioned above
- Add words to tokenizer_exceptions_list.py in respect to regex used in tokenizer_exceptions.py
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [X] I have submitted the spaCy Contributor Agreement.
- [X] I ran the tests, and all new and existing tests passed.
- [X] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Set version to 2.0.13
* Fix formatting and consistency
* Update docs for new version [ci skip]
* Increment version [ci skip]
* Add info on wheels [ci skip]
* Adding "This is a sentence" example to Sinhala (#2846)
* Add wheels badge
* Update badge [ci skip]
* Update README.rst [ci skip]
* Update murmurhash pin
* Increment version to 2.0.14.dev0
* Update GPU docs for v2.0.14
* Add wheel to setup_requires
* Import prefer_gpu and require_gpu functions from Thinc
* Add tests for prefer_gpu() and require_gpu()
* Update requirements and setup.py
* Workaround bug in thinc require_gpu
* Set version to v2.0.14
* Update push-tag script
* Unhack prefer_gpu
* Require thinc 6.10.6
* Update prefer_gpu and require_gpu docs [ci skip]
* Fix specifiers for GPU
* Set version to 2.0.14.dev1
* Set version to 2.0.14
* Update Thinc version pin
* Increment version
* Fix msgpack-numpy version pin
* Increment version
* Update version to 2.0.16
* Update version [ci skip]
* Redundant ')' in the Stop words' example (#2856)
<!--- Provide a general summary of your changes in the title. -->
## Description
<!--- Use this section to describe your changes. If your changes required
testing, include information about the testing environment and the tests you
ran. If your test fixes a bug reported in an issue, don't forget to include the
issue number. If your PR is still a work in progress, that's totally fine – just
include a note to let us know. -->
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [ ] I have submitted the spaCy Contributor Agreement.
- [ ] I ran the tests, and all new and existing tests passed.
- [ ] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Documentation improvement regarding joblib and SO (#2867)
Some documentation improvements
## Description
1. Fixed the dead URL to joblib
2. Fixed Stack Overflow brand name (with space)
### Types of change
Documentation
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* raise error when setting overlapping entities as doc.ents (#2880)
* Fix out-of-bounds access in NER training
The helper method state.B(1) gets the index of the first token of the
buffer, or -1 if no such token exists. Normally this is safe because we
pass this to functions like state.safe_get(), which returns an empty
token. Here we used it directly as an array index, which is not okay!
This error may have been the cause of out-of-bounds access errors during
training. Similar errors may still be around, so much be hunted down.
Hunting this one down took a long time...I printed out values across
training runs and diffed, looking for points of divergence between
runs, when no randomness should be allowed.
* Change PyThaiNLP Url (#2876)
* Fix missing comma
* Add example showing a fix-up rule for space entities
* Set version to 2.0.17.dev0
* Update regex version
* Revert "Update regex version"
This reverts commit 62358dd867.
* Try setting older regex version, to align with conda
* Set version to 2.0.17
* Add spacy-js to universe [ci-skip]
* Add spacy-raspberry to universe (closes#2889)
* Add script to validate universe json [ci skip]
* Removed space in docs + added contributor indo (#2909)
* - removed unneeded space in documentation
* - added contributor info
* Allow input text of length up to max_length, inclusive (#2922)
* Include universe spec for spacy-wordnet component (#2919)
* feat: include universe spec for spacy-wordnet component
* chore: include spaCy contributor agreement
* Minor formatting changes [ci skip]
* Fix image [ci skip]
Twitter URL doesn't work on live site
* Check if the word is in one of the regular lists specific to each POS (#2886)
* 💫 Create random IDs for SVGs to prevent ID clashes (#2927)
Resolves#2924.
## Description
Fixes problem where multiple visualizations in Jupyter notebooks would have clashing arc IDs, resulting in weirdly positioned arc labels. Generating a random ID prefix so even identical parses won't receive the same IDs for consistency (even if effect of ID clash isn't noticable here.)
### Types of change
bug fix
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Fix typo [ci skip]
* fixes symbolic link on py3 and windows (#2949)
* fixes symbolic link on py3 and windows
during setup of spacy using command
python -m spacy link en_core_web_sm en
closes#2948
* Update spacy/compat.py
Co-Authored-By: cicorias <cicorias@users.noreply.github.com>
* Fix formatting
* Update universe [ci skip]
* Catalan Language Support (#2940)
* Catalan language Support
* Ddding Catalan to documentation
* Sort languages alphabetically [ci skip]
* Update tests for pytest 4.x (#2965)
<!--- Provide a general summary of your changes in the title. -->
## Description
- [x] Replace marks in params for pytest 4.0 compat ([see here](https://docs.pytest.org/en/latest/deprecations.html#marks-in-pytest-mark-parametrize))
- [x] Un-xfail passing tests (some fixes in a recent update resolved a bunch of issues, but tests were apparently never updated here)
### Types of change
<!-- What type of change does your PR cover? Is it a bug fix, an enhancement
or new feature, or a change to the documentation? -->
## Checklist
<!--- Before you submit the PR, go over this checklist and make sure you can
tick off all the boxes. [] -> [x] -->
- [x] I have submitted the spaCy Contributor Agreement.
- [x] I ran the tests, and all new and existing tests passed.
- [x] My changes don't require a change to the documentation, or if they do, I've added all required information.
* Fix regex pin to harmonize with conda (#2964)
* Update README.rst
* Fix bug where Vocab.prune_vector did not use 'batch_size' (#2977)
Fixes#2976
* Fix typo
* Fix typo
* Remove duplicate file
* Require thinc 7.0.0.dev2
Fixes bug in gpu_ops that would use cupy instead of numpy on CPU
* Add missing import
* Fix error IDs
* Fix tests
These experiments were completed a few weeks ago, but I didn't make the PR, pending model release.
Token vector width: 128->96
Hidden width: 128->64
Embed size: 5000->2000
Dropout: 0.2->0.1
Updated optimizer defaults (unclear how important?)
This should improve speed, model size and load time, while keeping
similar or slightly better accuracy.
The tl;dr is we prefer to prevent over-fitting by reducing model size,
rather than using more dropout.
Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know.
* subword_features: Controls whether subword features are used in the
word embeddings. True by default (specifically, prefix, suffix and word
shape). Should be set to False for languages like Chinese and Japanese.
* conv_depth: Depth of the convolutional layers. Defaults to 4.
* Go back to using requests instead of urllib (closes#2320)
Fewer dependencies are good, but this one was simply causing too many other problems around SSL verification and Python 2/3 compatibility. requests is a popular enough package that it's okay for spaCy to depend on it – and this will hopefully make model downloads less flakey.
* Only download model if not installed (see #1456)
Use #egg=model==version to allow pip to check for existing installations. The download is only started if no installation matching the package/version is found. Fixes a long-standing inconvenience.
* Pass additional options to pip when installing model (resolves#1456)
Treat all additional arguments passed to the download command as pip options to allow user to customise the command. For example:
python -m spacy download en --user
* Add CLI option to enable installing model package dependencies
* Revert "Add CLI option to enable installing model package dependencies"
This reverts commit 9336ffe695.
* Update documentation
* Add spacy.errors module
* Update deprecation and user warnings
* Replace errors and asserts with new error message system
* Remove redundant asserts
* Fix whitespace
* Add messages for print/util.prints statements
* Fix typo
* Fix typos
* Move CLI messages to spacy.cli._messages
* Add decorator to display error code with message
An implementation like this is nice because it only modifies the string when it's retrieved from the containing class – so we don't have to worry about manipulating tracebacks etc.
* Remove unused link in spacy.about
* Update errors for invalid pipeline components
* Improve error for unknown factories
* Add displaCy warnings
* Update formatting consistency
* Move error message to spacy.errors
* Update errors and check if doc returned by component is None
This patch does a few smallish things that tighten up the training workflow a little, and allow memory use during training to be reduced by letting the GoldCorpus stream data properly.
Previously, the parser and entity recognizer read and saved labels as lists, with extra labels noted separately. Lists were used becaue ordering is very important, to ensure that the label-to-class mapping is stable.
We now manage labels as nested dictionaries, first keyed by the action, and then keyed by the label. Values are frequencies. The trick is, how do we save new labels? We need to make sure we iterate over these in the same order they're added. Otherwise, we'll get different class IDs, and the model's predictions won't make sense.
To allow stable sorting, we map the new labels to negative values. If we have two new labels, they'll be noted as having "frequency" -1 and -2. The next new label will then have "frequency" -3. When we sort by (frequency, label), we then get a stable sort.
Storing frequencies then allows us to make the next nice improvement. Previously we had to iterate over the whole training set, to pre-process it for the deprojectivisation. This led to storing the whole training set in memory. This was most of the required memory during training.
To prevent this, we now store the frequencies as we stream in the data, and deprojectivize as we go. Once we've built the frequencies, we can then apply a frequency cut-off when we decide how many classes to make.
Finally, to allow proper data streaming, we also have to have some way of shuffling the iterator. This is awkward if the training files have multiple documents in them. To solve this, the GoldCorpus class now writes the training data to disk in msgpack files, one per document. We can then shuffle the data by shuffling the paths.
This is a squash merge, as I made a lot of very small commits. Individual commit messages below.
* Simplify label management for TransitionSystem and its subclasses
* Fix serialization for new label handling format in parser
* Simplify and improve GoldCorpus class. Reduce memory use, write to temp dir
* Set actions in transition system
* Require thinc 6.11.1.dev4
* Fix error in parser init
* Add unicode declaration
* Fix unicode declaration
* Update textcat test
* Try to get model training on less memory
* Print json loc for now
* Try rapidjson to reduce memory use
* Remove rapidjson requirement
* Try rapidjson for reduced mem usage
* Handle None heads when projectivising
* Stream json docs
* Fix train script
* Handle projectivity in GoldParse
* Fix projectivity handling
* Add minibatch_by_words util from ud_train
* Minibatch by number of words in spacy.cli.train
* Move minibatch_by_words util to spacy.util
* Fix label handling
* More hacking at label management in parser
* Fix encoding in msgpack serialization in GoldParse
* Adjust batch sizes in parser training
* Fix minibatch_by_words
* Add merge_subtokens function to pipeline.pyx
* Register merge_subtokens factory
* Restore use of msgpack tmp directory
* Use minibatch-by-words in train
* Handle retokenization in scorer
* Change back-off approach for missing labels. Use 'dep' label
* Update NER for new label management
* Set NER tags for over-segmented words
* Fix label alignment in gold
* Fix label back-off for infrequent labels
* Fix int type in labels dict key
* Fix int type in labels dict key
* Update feature definition for 8 feature set
* Update ud-train script for new label stuff
* Fix json streamer
* Print the line number if conll eval fails
* Update children and sentence boundaries after deprojectivisation
* Export set_children_from_heads from doc.pxd
* Render parses during UD training
* Remove print statement
* Require thinc 6.11.1.dev6. Try adding wheel as install_requires
* Set different dev version, to flush pip cache
* Update thinc version
* Update GoldCorpus docs
* Remove print statements
* Fix formatting and links [ci skip]
In general, it's nice for models to specify spaCy as a dependency. However, this tends to cause problems in conda environments, as pip will re-install spaCy and its dependencies (especially Thinc)
Check for is_symlink() to also overwrite invalid and outdated symlinks. Also show better error message if link path exists but is not symlink (i.e. file or directory).
Add accuracy scores to meta.json instead of accuracy.json and replace
all relevant properties like lang, pipeline, spacy_version in existing
meta.json. If not present, also add name and version placeholders to
make it packagable.
Re-create meta.json in model directory, even if it exists. Especially
useful when updating existing spaCy models or training with Prodigy.
Ensures user won't end up with multiple "en_core_web_sm" models, and
offers easy way to change the model's name and settings without having
to edit the meta.json file.
On fresh install via subprocess, pip.get_installed_distributions()
won't show new model, so is_package check in link command fails.
Solution for now is to get model package path explicitly and pass it to
link command.
* Obsolete .parser, .entity etc names in favour of .pipeline
* Components no longer create models on initialization
* Models created by loading method (from_disk(), from_bytes() etc), or
.begin_training()
* Add .predict(), .set_annotations() methods in components
* Pass state through pipeline, to allow components to share information
more flexibly.
Complain if no "lang", "name" or "version" is found (those settings are
used in directory / package names). Package will still build without,
but it'll inevitably fail somewhere down the line.
Add meta option to supply path to meta.json. If no meta path is set,
check if meta.json exists in input directory and use it. Otherwise,
prompt for details on the command line.
Otherwise, formatting the success message with model_path.as_posix()
fails when using a local path for linking (linking still works, but the
error message is confusing)
