* Allow `pkuseg_model` to be set to `None` on initialization
* Don't save config within tokenizer
* Force convert pkuseg_model to use pickle protocol 4 by reencoding with
`pickle5` on serialization
* Update pkuseg serialization test
* Fix skipped documents in entity scorer
* Add back the skipping of unannotated entities
* Update spacy/scorer.py
* Use more specific NER scorer
* Fix import
* Fix get_ner_prf
* Add scorer
* Fix scorer
Co-authored-by: Ines Montani <ines@ines.io>
* Add MORPH handling to Matcher
* Add `MORPH` to `Matcher` schema
* Rename `_SetMemberPredicate` to `_SetPredicate`
* Add `ISSUBSET` and `ISSUPERSET` operators to `_SetPredicate`
* Add special handling for normalization and conversion of morph
values into sets
* For other attrs, `ISSUBSET` acts like `IN` and `ISSUPERSET` only
matches for 0 or 1 values
* Update test
* Rename to IS_SUBSET and IS_SUPERSET
* Add option to disable Matcher errors
* Add option to disable Matcher errors when a doc doesn't contain a
particular type of annotation
Minor additional change:
* Update `AttributeRuler.load_from_morph_rules` to allow direct `MORPH`
values
* Rename suppress_errors to allow_missing
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Refactor annotation checks in Matcher and PhraseMatcher
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* NEL: read sentences and ents from reference
* fiddling with sent_start annotations
* add KB serialization test
* KB write additional file with strings.json
* score_links function to calculate NEL P/R/F
* formatting
* documentation
* create contributor agreement
* Update Indonesian example. (see #1107)
Update Indonesian examples with more proper phrases. the current phrases contains sensitive and violent words.
Similar to how vectors are handled, move the vocab lookups to be loaded
at the start of training rather than when the vocab is initialized,
since the vocab doesn't have access to the full config when it's
created.
The option moves from `nlp.load_vocab_data` to `training.lookups`.
Typically these tables will come from `spacy-lookups-data`, but any
`Lookups` object can be provided.
The loading from `spacy-lookups-data` is now strict, so configs for each
language should specify the exact tables required. This also makes it
easier to control whether the larger clusters and probs tables are
included.
To load `lexeme_norm` from `spacy-lookups-data`:
```
[training.lookups]
@misc = "spacy.LoadLookupsData.v1"
lang = ${nlp.lang}
tables = ["lexeme_norm"]
```
In order to make it easier to construct `Doc` objects as training data,
modify how missing and blocked entity tokens are set to prioritize
setting `O` and missing entity tokens for training purposes over setting
blocked entity tokens.
* `Doc.ents` setter sets tokens outside entity spans to `O` regardless
of the current state of each token
* For `Doc.ents`, setting a span with a missing label sets the `ent_iob`
to missing instead of blocked
* `Doc.block_ents(spans)` marks spans as hard `O` for use with the
`EntityRecognizer`
* Refactor Docs.is_ flags
* Add derived `Doc.has_annotation` method
* `Doc.has_annotation(attr)` returns `True` for partial annotation
* `Doc.has_annotation(attr, require_complete=True)` returns `True` for
complete annotation
* Add deprecation warnings to `is_tagged`, `is_parsed`, `is_sentenced`
and `is_nered`
* Add `Doc._get_array_attrs()`, which returns a full list of `Doc` attrs
for use with `Doc.to_array`, `Doc.to_bytes` and `Doc.from_docs`. The
list is the `DocBin` attributes list plus `SPACY` and `LENGTH`.
Notes on `Doc.has_annotation`:
* `HEAD` is converted to `DEP` because heads don't have an unset state
* Accept `IS_SENT_START` as a synonym of `SENT_START`
Additional changes:
* Add `NORM`, `ENT_ID` and `SENT_START` to default attributes for
`DocBin`
* In `Doc.from_array()` the presence of `DEP` causes `HEAD` to override
`SENT_START`
* In `Doc.from_array()` using `attrs` other than
`Doc._get_array_attrs()` (i.e., a user's custom list rather than our
default internal list) with both `HEAD` and `SENT_START` shows a warning
that `HEAD` will override `SENT_START`
* `set_children_from_heads` does not require dependency labels to set
sentence boundaries and sets `sent_start` for all non-sentence starts to
`-1`
* Fix call to set_children_form_heads
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Clean up spacy.tokens
* Update `set_children_from_heads`:
* Don't check `dep` when setting lr_* or sentence starts
* Set all non-sentence starts to `False`
* Use `set_children_from_heads` in `Token.head` setter
* Reduce similar/duplicate code (admittedly adds a bit of overhead)
* Update sentence starts consistently
* Remove unused `Doc.set_parse`
* Minor changes:
* Declare cython variables (to avoid cython warnings)
* Clean up imports
* Modify set_children_from_heads to set token range
Modify `set_children_from_heads` so that it adjust tokens within a
specified range rather then the whole document.
Modify the `Token.head` setter to adjust only the tokens affected by the
new head assignment.
For languages without provided models and with lemmatizer rules in
`spacy-lookups-data`, make the rule-based lemmatizer the default:
Bengali, Persian, Norwegian, Swedish
Modify `Token.morph` property so that `Token.c.morph` can be reset back
to an internal value of `0`. Allow setting `Token.morph` from a hash as
long as the morph string is already in the `StringStore`, setting it
indirectly through `Token.morph_` so that the value is added to the
morphology. If the hash is not in the `StringStore`, raise an error.
* raise error if no valid Example objects were found during initialization
* fix max_length parameter
* remove commit from other branch
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* ensure Language passes on valid examples for initialization
* fix tagger model initialization
* check for valid get_examples across components
* assume labels were added before begin_training
* fix senter initialization
* fix morphologizer initialization
* use methods to check arguments
* test textcat init, requires thinc>=8.0.0a31
* fix tok2vec init
* fix entity linker init
* use islice
* fix simple NER
* cleanup debug model
* fix assert statements
* fix tests
* throw error when adding a label if the output layer can't be resized anymore
* fix test
* add failing test for simple_ner
* UX improvements
* morphologizer UX
* assume begin_training gets a representative set and processes the labels
* remove assumptions for output of untrained NER model
* restore test for original purpose
This was the only registry that expected the registered objects to be dictionaries instead of functions that return something. We can still support plain dicts but we should also support functions for consistency
The Language.use_params method was failing if you passed in None, which
meant we had to use awkward conditionals for the parameter averaging.
This solves the problem.
Add official support for the `DependencyMatcher`. Redesign the pattern
specification. Fix and extend operator implementations. Update API docs
and add usage docs.
Patterns
--------
Refactor pattern structure to:
```
{
"LEFT_ID": str,
"REL_OP": str,
"RIGHT_ID": str,
"RIGHT_ATTRS": dict,
}
```
The first node contains only `RIGHT_ID` and `RIGHT_ATTRS` and all
subsequent nodes contain all four keys.
New operators
-------------
Because of the way patterns are constructed from left to right, it's
helpful to have `follows` operators along with `precedes` operators. Add
operators for simple precedes / follows alongside immediate precedes /
follows.
* `.*`: precedes
* `;`: immediately follows
* `;*`: follows
Operator fixes
--------------
* `<` and `<<` do not include the node itself
* Fix reversed order for all operators involving linear precedence (`.`,
all sibling operators)
* Linear precedence operators do not match nodes outside the same parse
Additional fixes
----------------
* Use v3 Matcher API
* Support `get` and `remove`
* Support pickling
Follow-ups to the parser efficiency fix.
* Avoid introducing new counter for number of pushes
* Base cut on number of transitions, keeping it more even
* Reintroduce the randomization we had in v2.
The parser training makes use of a trick for long documents, where we
use the oracle to cut up the document into sections, so that we can have
batch items in the middle of a document. For instance, if we have one
document of 600 words, we might make 6 states, starting at words 0, 100,
200, 300, 400 and 500.
The problem is for v3, I screwed this up and didn't stop parsing! So
instead of a batch of [100, 100, 100, 100, 100, 100], we'd have a batch
of [600, 500, 400, 300, 200, 100]. Oops.
The implementation here could probably be improved, it's annoying to
have this extra variable in the state. But this'll do.
This makes the v3 parser training 5-10 times faster, depending on document
lengths. This problem wasn't in v2.
A long time ago we went to some trouble to try to clean up "unused"
strings, to avoid the `StringStore` growing in long-running processes.
This never really worked reliably, and I think it was a really wrong
approach. It's much better to let the user reload the `nlp` object as
necessary, now that the string encoding is stable (in v1, the string IDs
were sequential integers, making reloading the NLP object really
annoying.)
The extra book-keeping does make some performance difference, and the
feature is unsed, so it's past time we killed it.
* Prevent Tagger model init with 0 labels
Raise an error before trying to initialize a tagger model with 0 labels.
* Add dummy tagger label for test
* Remove tagless tagger model initializiation
* Fix error number after merge
* Add dummy tagger label to test
* Fix formatting
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* rename to spacy-transformers.TransformerListener
* add some more tok2vec tests
* use select_pipes
* fix docs - annotation setter was not changed in the end
Sort the returned matches by rule order (the `match_id`) so that the
rules are applied in the order they were added. This is necessary, for
instance, if the `AttributeRuler` is used for the tag map and later
rules require POS tags.
Serialize `AttributeRuler.patterns` instead of the individual lists to
simplify the serialized and so that patterns are reloaded exactly as
they were originally provided (preserving `_attrs_unnormed`).
* Add AttributeRuler.score
Add scoring for TAG / POS / MORPH / LEMMA if these are present in the
assigned token attributes.
Add default score weights (that don't really make a lot of sense) so
that the scores are in the default config in some form.
* Update docs
* Fix up/download of http and local paths
* Support git_sparse_checkout for assets
* Fix scorer
* Handle already-present directories for git assets
* Improve convert command
* Fix support for existant files in git assets
* Support branches in git sparse checkout
* Format
* Fix git assets
* Document git block in assets
* Fix test
* Fix test
* Revert "Fix test"
This reverts commit cf3097260f.
* Revert "Fix test"
This reverts commit 964d636e27.
* Dont multiply p/r/f by 100
* Display scores * 100 during training
* Update stop_words.py
Hebrew STOP WORDS
* Update stop_words.py
* contributor
* contributor
* add some common domain extentions
support human number 1K/1M....
* support human number 1K/1M....
* hebrew number tokenize
1K/1M implement in EN
* test human tokenize fix
* test
* heb like num
revert human number change
* heb like num
* Create lex_attrs.py
Hello,
I am missing a CZECH language in SpaCy. So I would like to help to push it a little. This file is base on others lex_attrs.py files just with translation to Czech.
* Update __init__.py
Updated for use with new Czech Lex_attrs file
* Update stop_words.py
* Create test_text.py
* add like_num testing for czech
Co-authored-by: holubvl3 <47881982+holubvl3@users.noreply.github.com>
Co-authored-by: holubvl3 <vilemrousi@gmail.com>
Co-authored-by: Vladimír Holubec <vholubec@arcdata.cz>
* Create lex_attrs.py
Hello,
I am missing a CZECH language in SpaCy. So I would like to help to push it a little. This file is base on others lex_attrs.py files just with translation to Czech.
* Update __init__.py
Updated for use with new Czech Lex_attrs file
* Update stop_words.py
* Create test_text.py
Co-authored-by: Vladimír Holubec <vholubec@arcdata.cz>
- Accept any case for label names in ents and colors option, even if actual predicted label uses different casing
- Don't text-transform: uppercase visually, if it's important to users that the label is represented as-is in the UI
- As much as I dislike YAML, it seemed like a better format here because it allows us to add comments if we want to explain the different recommendations
- Don't include the generated JS in the repo by default and build it on the fly when running or deploying the site. This ensures it's always up to date.
- Simplify jinja_to_js script and use fewer dependencies
* candidate generator as separate part of EL config
* update comment
* ent instead of str as input for candidate generation
* Span instead of str: correct type indication
* fix types
* unit test to create new candidate generator
* fix replace_pipe argument passing
* move error message, general cleanup
* add vocab back to KB constructor
* provide KB as callable from Vocab arg
* rename to kb_loader, fix KB serialization as part of the EL pipe
* fix typo
* reformatting
* cleanup
* fix comment
* fix wrongly duplicated code from merge conflict
* rename dump to to_disk
* from_disk instead of load_bulk
* update test after recent removal of set_morphology in tagger
* remove old doc
* Add Lemmatizer and simplify related components
* Add `Lemmatizer` pipe with `lookup` and `rule` modes using the
`Lookups` tables.
* Reduce `Tagger` to a simple tagger that sets `Token.tag` (no pos or lemma)
* Reduce `Morphology` to only keep track of morph tags (no tag map, lemmatizer,
or morph rules)
* Remove lemmatizer from `Vocab`
* Adjust many many tests
Differences:
* No default lookup lemmas
* No special treatment of TAG in `from_array` and similar required
* Easier to modify labels in a `Tagger`
* No extra strings added from morphology / tag map
* Fix test
* Initial fix for Lemmatizer config/serialization
* Adjust init test to be more generic
* Adjust init test to force empty Lookups
* Add simple cache to rule-based lemmatizer
* Convert language-specific lemmatizers
Convert language-specific lemmatizers to component lemmatizers. Remove
previous lemmatizer class.
* Fix French and Polish lemmatizers
* Remove outdated UPOS conversions
* Update Russian lemmatizer init in tests
* Add minimal init/run tests for custom lemmatizers
* Add option to overwrite existing lemmas
* Update mode setting, lookup loading, and caching
* Make `mode` an immutable property
* Only enforce strict `load_lookups` for known supported modes
* Move caching into individual `_lemmatize` methods
* Implement strict when lang is not found in lookups
* Fix tables/lookups in make_lemmatizer
* Reallow provided lookups and allow for stricter checks
* Add lookups asset to all Lemmatizer pipe tests
* Rename lookups in lemmatizer init test
* Clean up merge
* Refactor lookup table loading
* Add helper from `load_lemmatizer_lookups` that loads required and
optional lookups tables based on settings provided by a config.
Additional slight refactor of lookups:
* Add `Lookups.set_table` to set a table from a provided `Table`
* Reorder class definitions to be able to specify type as `Table`
* Move registry assets into test methods
* Refactor lookups tables config
Use class methods within `Lemmatizer` to provide the config for
particular modes and to load the lookups from a config.
* Add pipe and score to lemmatizer
* Simplify Tagger.score
* Add missing import
* Clean up imports and auto-format
* Remove unused kwarg
* Tidy up and auto-format
* Update docstrings for Lemmatizer
Update docstrings for Lemmatizer.
Additionally modify `is_base_form` API to take `Token` instead of
individual features.
* Update docstrings
* Remove tag map values from Tagger.add_label
* Update API docs
* Fix relative link in Lemmatizer API docs
* WIP: Concept for modifying nlp object before and after init
* Make callbacks return nlp object
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Raise if callbacks don't return correct type
* Rename, update types, add after_pipeline_creation
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Add a warning when a subpattern is not processed and discarded
* Normalize subpattern attribute/operator keys to upper case like
top-level attributes
* Allow adding pipeline components from source model
* Config: name -> component
* Improve error messages
* Fix error and test
* Add frozen components and exclude logic
* Remove exclude from Language.evaluate
* Init sourced components with current vocab
* Fix error codes
* consistently use upper-case IDS in token_annotation format and for get_aligned
* remove ID from to_dict (not used in from_dict either)
* fix test
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Add AttributeRuler for token attribute exceptions
Add the `AttributeRuler` to handle exceptions for token-level
attributes. The `AttributeRuler` uses `Matcher` patterns to identify
target spans and applies the specified attributes to the token at the
provided index in the matched span. A negative index can be used to
index from the end of the matched span. The retokenizer is used to
"merge" the individual tokens and assign them the provided attributes.
Helper functions can import existing tag maps and morph rules to the
corresponding `Matcher` patterns.
There is an additional minor bug fix for `MORPH` attributes in the
retokenizer to correctly normalize the values and to handle `MORPH`
alongside `_` in an attrs dict.
* Fix default name
* Update name in error message
* Extend AttributeRuler functionality
* Add option to initialize with a dict of AttributeRuler patterns
* Instead of silently discarding overlapping matches (the default
behavior for the retokenizer if only the attrs differ), split the
matches into disjoint sets and retokenize each set separately. This
allows, for instance, one pattern to set the POS and another pattern to
set the lemma. (If two matches modify the same attribute, it looks like
the attrs are applied in the order they were added, but it may not be
deterministic?)
* Improve types
* Sort spans before processing
* Fix index boundaries in Span
* Refactor retokenizer to separate attrs methods
Add top-level `normalize_token_attrs` and `set_token_attrs` methods.
* Update AttributeRuler to use refactored methods
Update `AttributeRuler` to replace use of full retokenizer with only the
relevant methods for normalizing and setting attributes for a single
token.
* Update spacy/pipeline/attributeruler.py
Co-authored-by: Ines Montani <ines@ines.io>
* Make API more similar to EntityRuler
* Add `AttributeRuler.add_patterns` to add patterns from a list of dicts
* Return list of dicts as property `AttributeRuler.patterns`
* Make attrs_unnormed private
* Add test loading patterns from assets
* Revert "Fix index boundaries in Span"
This reverts commit 8f8a5c3386.
* Add Span index boundary checks (#5861)
* Add Span index boundary checks
* Return Span-specific IndexError in all cases
* Simplify and fix if/else
Co-authored-by: Ines Montani <ines@ines.io>
* remove empty gold.pyx
* add alignment unit test (to be used in docs)
* ensure that Alignment is only used on equal texts
* additional test using example.alignment
* formatting
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
* Allow Doc.char_span to snap to token boundaries
Add a `mode` option to allow `Doc.char_span` to snap to token
boundaries. The `mode` options:
* `strict`: character offsets must match token boundaries (default, same as
before)
* `inside`: all tokens completely within the character span
* `outside`: all tokens at least partially covered by the character span
Add a new helper function `token_by_char` that returns the token
corresponding to a character position in the text. Update
`token_by_start` and `token_by_end` to use `token_by_char` for more
efficient searching.
* Remove unused import
* Rename mode to alignment_mode
Rename `mode` to `alignment_mode` with the options
`strict`/`contract`/`expand`. Any unrecognized modes are silently
converted to `strict`.
