* Modify retokenizer to use span root attributes
* tag/pos/morph are set to root tag/pos/morph
* lemma and norm are reset and end up as orth (not ideal, but better
than orth of first token)
* Also handle individual merge case
* Add test
* Attempt to handle ent_iob and ent_type in merges
* Fix check for whether B-ENT should become I-ENT
* Move IOB consistency check to after attrs
Move all IOB consistency checks after attrs are set and simplify to
check entire document, modifying I to B at the beginning of the document
or if the entity type of the previous token isn't the same.
* Move IOB consistency check for single merge
Move IOB consistency check after the token array is compressed for the
single merge case.
* Update spacy/tokens/_retokenize.pyx
Co-Authored-By: Matthew Honnibal <honnibal+gh@gmail.com>
* Remove single vs. multiple merge distinction
Remove original single-instance `_merge()` and use `_bulk_merge()` (now
renamed `_merge()`) for all merges.
* Add out-of-bound check in previous entity check
* 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
* allow phrasematcher to link one match to multiple original patterns
* small fix for defining ent_id in the matcher (anti-ghost prevention)
* cleanup
* formatting
* Improve load_language_data helper
* WIP: Add Lookups implementation
* Start moving lemma data over to JSON
* WIP: move data over for more languages
* Convert more languages
* Fix lemmatizer fixtures in tests
* Finish conversion
* Auto-format JSON files
* Fix test for now
* Make sure tables are stored on instance
Check for relevant components in the pipeline when Matcher is called,
similar to the checks for PhraseMatcher in #4105.
* keep track of attributes seen in patterns
* when Matcher is called on a Doc, check for is_tagged for LEMMA, TAG,
POS and for is_parsed for DEP
* Fix typo in rule-based matching docs
* Improve token pattern checking without validation
Add more detailed token pattern checks without full JSON pattern validation and
provide more detailed error messages.
Addresses #4070 (also related: #4063, #4100).
* Check whether top-level attributes in patterns and attr for PhraseMatcher are
in token pattern schema
* Check whether attribute value types are supported in general (as opposed to
per attribute with full validation)
* Report various internal error types (OverflowError, AttributeError, KeyError)
as ValueError with standard error messages
* Check for tagger/parser in PhraseMatcher pipeline for attributes TAG, POS,
LEMMA, and DEP
* Add error messages with relevant details on how to use validate=True or nlp()
instead of nlp.make_doc()
* Support attr=TEXT for PhraseMatcher
* Add NORM to schema
* Expand tests for pattern validation, Matcher, PhraseMatcher, and EntityRuler
* Remove unnecessary .keys()
* Rephrase error messages
* Add another type check to Matcher
Add another type check to Matcher for more understandable error messages
in some rare cases.
* Support phrase_matcher_attr=TEXT for EntityRuler
* Don't use spacy.errors in examples and bin scripts
* Fix error code
* Auto-format
Also try get Azure pipelines to finally start a build :(
* Update errors.py
Co-authored-by: Ines Montani <ines@ines.io>
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Check whether two entities overlap
- biluo_gold_biluo_overlap now throw exception when entities passed in have overlaps
- added unit test
* SCA agreement
* pytest file for issue4104 established
* edited default lookup english lemmatizer for spun; fixes issue 4102
* eliminated parameterization and sorted dictionary dependnency in issue 4104 test
* added contributor agreement
* document token ent_kb_id
* document span kb_id
* update pipeline documentation
* prior and context weights as bool's instead
* entitylinker api documentation
* drop for both models
* finish entitylinker documentation
* small fixes
* documentation for KB
* candidate documentation
* links to api pages in code
* small fix
* frequency examples as counts for consistency
* consistent documentation about tensors returned by predict
* add entity linking to usage 101
* add entity linking infobox and KB section to 101
* entity-linking in linguistic features
* small typo corrections
* training example and docs for entity_linker
* predefined nlp and kb
* revert back to similarity encodings for simplicity (for now)
* set prior probabilities to 0 when excluded
* code clean up
* bugfix: deleting kb ID from tokens when entities were removed
* refactor train el example to use either model or vocab
* pretrain_kb example for example kb generation
* add to training docs for KB + EL example scripts
* small fixes
* error numbering
* ensure the language of vocab and nlp stay consistent across serialization
* equality with =
* avoid conflict in errors file
* add error 151
* final adjustements to the train scripts - consistency
* update of goldparse documentation
* small corrections
* push commit
* turn kb_creator into CLI script (wip)
* proper parameters for training entity vectors
* wikidata pipeline split up into two executable scripts
* remove context_width
* move wikidata scripts in bin directory, remove old dummy script
* refine KB script with logs and preprocessing options
* small edits
* small improvements to logging of EL CLI script
* Improve NER per type scoring
* include all gold labels in per type scoring, not only when recall > 0
* improve efficiency of per type scoring
* Create Scorer tests, initially with NER tests
* move regression test #3968 (per type NER scoring) to Scorer tests
* add new test for per type NER scoring with imperfect P/R/F and per
type P/R/F including a case where R == 0.0
* failing unit test for issue 3962
* attempt to fix Issue #3962
* create artificial unit test example
* using length instead of self.length
* sp
* reformat with black
* find better ancestor within span and use generic 'dep'
* attach to span.root if there is no appropriate ancestor
* comment span text
* clean up ancestor code
* reconstruct dep tree to keep same number of sentences
Expected an `entity_ruler.jsonl` file in the top-level model directory, so the path passed to from_disk by default (model path plus componentn name), but with the suffix ".jsonl".
* Perserve flags in EntityRuler
The EntityRuler (explosion/spaCy#3526) does not preserve
overwrite flags (or `ent_id_sep`) when serialized. This
commit adds support for serialization/deserialization preserving
overwrite and ent_id_sep flags.
* add signed contributor agreement
* flake8 cleanup
mostly blank line issues.
* mark test from the issue as needing a model
The test from the issue needs some language model for serialization
but the test wasn't originally marked correctly.
* Adds `phrase_matcher_attr` to allow args to PhraseMatcher
This is an added arg to pass to the `PhraseMatcher`. For example,
this allows creation of a case insensitive phrase matcher when the
`EntityRuler` is created. References explosion/spaCy#3822
* remove unneeded model loading
The model didn't need to be loaded, and I replaced it with
a change that doesn't require it (using existings fixtures)
* updated docstring for new argument
* updated docs to reflect new argument to the EntityRuler constructor
* change tempdir handling to be compatible with python 2.7
* return conflicted code to entityruler
Some stuff got cut out because of merge conflicts, this
returns that code for the phrase_matcher_attr.
* fixed typo in the code added back after conflicts
* flake8 compliance
When I deconflicted the branch there were some flake8 issues
introduced. This resolves the spacing problems.
* test changes: attempts to fix flaky test in python3.5
These tests seem to be alittle flaky in 3.5 so I changed the check to avoid
the comparisons that seem to be fail sometimes.
* Perserve flags in EntityRuler
The EntityRuler (explosion/spaCy#3526) does not preserve
overwrite flags (or `ent_id_sep`) when serialized. This
commit adds support for serialization/deserialization preserving
overwrite and ent_id_sep flags.
* add signed contributor agreement
* flake8 cleanup
mostly blank line issues.
* mark test from the issue as needing a model
The test from the issue needs some language model for serialization
but the test wasn't originally marked correctly.
* remove unneeded model loading
The model didn't need to be loaded, and I replaced it with
a change that doesn't require it (using existings fixtures)
* change tempdir handling to be compatible with python 2.7
* Adds code to handle item saved before this change.
This code chanes how the save files are handled and how the bytes
are stored as well. This code adds check to dispatch correctly
if it encounters bytes or files saved in the old format (and tests
for those cases).
* use util function for tempdir management
Updated after PR comments: this code now uses the make_tempdir function from util
instead of doing it by hand.
* initial LT lang support
* Added more stopwords. Started setting up some basic test environment (not complete)
* Initial morph rules for LT lang
* Closes#1 Adds tokenizer exceptions for Lithuanian
* Closes#5 Punctuation rules. Closes#6 Lexical Attributes
* test: add native examples to basic tests
* feat: add tag map for lt lang
* fix: remove undefined tag attribute 'Definite'
* feat: add lemmatizer for lt lang
* refactor: add new instances to lt lang morph rules; use tags from tag map
* refactor: add morph rules to lt lang defaults
* refactor: only keep nouns, verbs, adverbs and adjectives in lt lang lemmatizer lookup
* refactor: add capitalized words to lt lang lemmatizer
* refactor: add more num words to lt lang lex attrs
* refactor: update lt lang stop word set
* refactor: add new instances to lt lang tokenizer exceptions
* refactor: remove comments form lt lang init file
* refactor: use function instead of lambda in lt lex lang getter
* refactor: remove conversion to dict in lt init when dict is already provided
* chore: rename lt 'test_basic' to 'test_text'
* feat: add more lt text tests
* feat: add lemmatizer tests
* refactor: remove unused imports, add newline to end of file
* chore: add contributor agreement
* chore: change 'en' to 'lt' in lt example description
* fix: add missing encoding info
* style: add newline to end of file
* refactor: use python2 compatible syntax
* style: reformat code using black
* Adding support for entity_id in EntityRuler pipeline component
* Adding Spacy Contributor aggreement
* Updating EntityRuler to use string.format instead of f strings
* Update Entity Ruler to support an 'id' attribute per pattern that explicitly identifies an entity.
* Fixing tests
* Remove custom extension entity_id and use built in ent_id token attribute.
* Changing entity_id to ent_id for consistent naming
* entity_ids => ent_ids
* Removing kb, cleaning up tests, making util functions private, use rsplit instead of split
* 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
* Add custom __dir__ to Underscore (see #3707)
* Make sure custom extension methods keep their docstrings (see #3707)
* Improve tests
* Prepend note on partial to docstring (see #3707)
* Remove print statement
* Handle cases where docstring is None
<!--- 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. -->
Fix a bug in the test of JapaneseTokenizer.
This PR may require @polm's review.
### 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? -->
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(util): fix decaying function output
* fix(util): better test and adhere to code standards
* fix(util): correct variable name, pytestify test, update website text
v2.1 introduced a regression when deserializing the parser after
parser.add_label() had been called. The code around the class mapping is
pretty confusing currently, as it was written to accommodate backwards
model compatibility. It needs to be revised when the models are next
retrained.
Closes#3433
spaCy v2.1 switched to the built-in re module, where v2.0 had been using
the third-party regex library. When the tokenizer was deserialized on
Python2.7, the `re.compile()` function was called with expressions that
featured escaped unicode codepoints that were not in Python2.7's unicode
database.
Problems occurred when we had a range between two of these unknown
codepoints, like this:
```
'[\\uAA77-\\uAA79]'
```
On Python2.7, the unknown codepoints are not unescaped correctly,
resulting in arbitrary out-of-range characters being matched by the
expression.
This problem does not occur if we instead have a range between two
unicode literals, rather than the escape sequences. To fix the bug, we
therefore add a new compat function that unescapes unicode sequences
using the `ast.literal_eval()` function. Care is taken to ensure we
do not also escape non-unicode sequences.
Closes#3356.
- [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
* label in span not writable anymore
* more explicit unit test and error message for readonly label
* bit more explanation (view)
* error msg tailored to specific case
* fix None case
Closes#2091.
## Description
With the new `vocab.writing_system` property introduced in #3390 (exposed via the language defaults), I was able to finally fix this (I think!). Based on the `Doc`, dispaCy now detects whether it's a RTL or LTR language and adjusts the visualization accordingly. Wherever possible, I've also added `direction` and `lang` attributes.
Entity visualization now looks like this:
<img width="318" alt="Screenshot 2019-03-11 at 16 06 51" src="https://user-images.githubusercontent.com/13643239/54136866-d97afd80-441c-11e9-8c27-3d46994cc833.png">
And dependencies like this (ignore the most likely incorrect tags and dependencies):
<img width="621" alt="Screenshot 2019-03-11 at 16 51 59" src="https://user-images.githubusercontent.com/13643239/54137771-8b66f980-441e-11e9-8460-0682b95eef2a.png">
### Types of change
enhancement, 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.
* Add xfail test for vocab.writing_system
* Add vocab.writing_system property
* Set Language.Defaults.writing_system
* Set default writing system
* Remove xfail on test_vocab_writing_system
Closes#2203. Closes#3268.
Lemmas set from outside the `Morphology` class were being overwritten. The result was especially confusing when deserialising, as it meant some lemmas could change when storing and retrieving a `Doc` object.
This PR applies two fixes:
1) When we go to set the lemma in the `Morphology` class, first check whether a lemma is already set. If so, don't overwrite.
2) When we load with `doc.from_array()`, take care to apply the `TAG` field first. This allows other fields to overwrite the `TAG` implied properties, if they're provided explicitly (e.g. the `LEMMA`).
## 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.
* Make serialization methods consistent
exclude keyword argument instead of random named keyword arguments and deprecation handling
* Update docs and add section on serialization fields
* Use default return instead of else
* Add Doc.is_nered to indicate if entities have been set
* Add properties in Doc.to_json if they were set, not if they're available
This way, if a processed Doc exports "pos": None, it means that the tag was explicitly unset. If it exports "ents": [], it means that entity annotations are available but that this document doesn't contain any entities. Before, this would have been unclear and problematic for training.
* Improve handling of missing NER tags
GoldParse can accept missing NER tags, if entities is provided
in BILUO format (rather than as spans). Missing tags can be provided
as None values.
Fix bug that occurred when first tag was a None value. Closes#2603.
* Document specification of missing NER tags.
* Classes for Ukrainian; small fix in Russian.
* Contributor agreement
* pymorphy2 initialization split for ru and uk (#3327)
* stop-words fixed
* Unit-tests updated
