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>
* Move Turkish lemmas to a json file
Rather than a large dict in Python source, the data is now a big json
file. This includes a method for loading the json file, falling back to
a compressed file, and an update to MANIFEST.in that excludes json in
the spacy/lang directory.
This focuses on Turkish specifically because it has the most language
data in core.
* Transition all lemmatizer.py files to json
This covers all lemmatizer.py files of a significant size (>500k or so).
Small files were left alone.
None of the affected files have logic, so this was pretty
straightforward.
One unusual thing is that the lemma data for Urdu doesn't seem to be
used anywhere. That may require further investigation.
* Move large lang data to json for fr/nb/nl/sv
These are the languages that use a lemmatizer directory (rather than a
single file) and are larger than English.
For most of these languages there were many language data files, in
which case only the large ones (>500k or so) were converted to json. It
may or may not be a good idea to migrate the remaining Python files to
json in the future.
* Fix id lemmas.json
The contents of this file were originally just copied from the Python
source, but that used single quotes, so it had to be properly converted
to json first.
* Add .json.gz to gitignore
This covers the json.gz files built as part of distribution.
* Add language data gzip to build process
Currently this gzip data on every build; it works, but it should be
changed to only gzip when the source file has been updated.
* Remove Danish lemmatizer.py
Missed this when I added the json.
* Update to match latest explosion/srsly#9
The way gzipped json is loaded/saved in srsly changed a bit.
* Only compress language data if necessary
If a .json.gz file exists and is newer than the corresponding json file,
it's not recompressed.
* Move en/el language data to json
This only affected files >500kb, which was nouns for both languages and
the generic lookup table for English.
* Remove empty files in Norwegian tokenizer
It's unclear why, but the Norwegian (nb) tokenizer had empty files for
adj/adv/noun/verb lemmas. This may have been a result of copying the
structure of the English lemmatizer.
This removed the files, but still creates the empty sets in the
lemmatizer. That may not actually be necessary.
* Remove dubious entries in English lookup.json
" furthest" and " skilled" - both prefixed with a space - were in the
English lookup table. That seems obviously wrong so I have removed them.
* Fix small issues with en/fr lemmatizers
The en tokenizer was including the removed _nouns.py file, so that's
removed.
The fr tokenizer is unusual in that it has a lemmatizer directory with
both __init__.py and lemmatizer.py. lemmatizer.py had not been converted
to load the json language data, so that was fixed.
* Auto-format
* Auto-format
* Update srsly pin
* Consistently use pathlib paths
While working on an unrelated task I got warnings about an unsupported
escape sequence (`"\("`) in the tokenizer exceptions. Making the
tokenizer exceptions a raw string makes this warning go away.
The specific string that triggered this is `¯\(ツ)/¯`.
* customizable template for entities display, allowing to pass additional parameters along each entity
* contributor agreement
* simpler naming for the additional parameters given to the span entities renderer
Co-Authored-By: Ines Montani <ines@ines.io>
* change of default parameter, as suggested
Co-Authored-By: Ines Montani <ines@ines.io>
* 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
* Check whether two entities overlap
- biluo_gold_biluo_overlap now throw exception when entities passed in have overlaps
- added unit test
* SCA agreement
Provide the tokens in the cycle and the first 50 tokens from document in
the error message so it's easier to track down the location of the cycle
in the data.
Addresses feature request in #3698.
* 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
* Update gold corpus code to properly ingest a directory of jsonlines files
In response to: https://github.com/explosion/spaCy/issues/3975
* Update spacy/gold.pyx
Co-Authored-By: Ines Montani <ines@ines.io>
* 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
* Improve error message when model.from_bytes() dies
When Thinc's model.from_bytes() is called with a mismatched model, often
we get a particularly ungraceful error,
e.g. "AttributeError: FunctionLayer has no attribute G"
This is because we're trying to load the parameters for something like
a LayerNorm layer, and the model architecture has some other layer there
instead. This is obviously terrible, especially since the error *type*
is wrong.
I've changed it to raise a ValueError. The error message is still
probably a bit terse, but it's hard to be sure exactly what's gone
wrong.
* Update spacy/pipeline/pipes.pyx
* Update spacy/pipeline/pipes.pyx
* Update spacy/pipeline/pipes.pyx
* Update spacy/syntax/nn_parser.pyx
* Update spacy/syntax/nn_parser.pyx
* Update spacy/pipeline/pipes.pyx
Co-Authored-By: Matthew Honnibal <honnibal+gh@gmail.com>
* Update spacy/pipeline/pipes.pyx
Co-Authored-By: Matthew Honnibal <honnibal+gh@gmail.com>
Co-authored-by: Ines Montani <ines@ines.io>
* 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".
* 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
* Evaluation of NER model per entity type, closes ##3490
Now each ent score is tracked individually in order to have its own Precision, Recall and F1 Score
* Keep track of each entity individually using dicts
* Improving how to compute the scores for each entity
* Fixed bug computing scores for ents
* Formatting with black
* Added key ents_per_type to the scores function
The key `ents_per_type` contains the metrics Precision, Recall and F1-Score for each entity individually
* 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.
* Norwegian fix
Add support for alternative past tense verb form (vaska).
* Norwegian months
Add all Norwegian months to tokenizer excpetions.
* More Norwegian abbreviations
Add more Norwegian abbreviations to tokenizer_exceptions.
* Contributor agreement khellan
Add signed contributor agreement for khellan (Knut O. Hellan).
* 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
* 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
* 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
