- 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* Remove outdated UPOS conversions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
* 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`.
* fix the wrong hash url in adding-languages.md file
change the #101 url hash path to #language-data
* filled in the spaCy Contributor Agreement
filled in the spaCy Contributor Agreement
* Refactor the Scorer to improve flexibility
Refactor the `Scorer` to improve flexibility for arbitrary pipeline
components.
* Individual pipeline components provide their own `evaluate` methods
that score a list of `Example`s and return a dictionary of scores
* `Scorer` is initialized either:
* with a provided pipeline containing components to be scored
* with a default pipeline containing the built-in statistical
components (senter, tagger, morphologizer, parser, ner)
* `Scorer.score` evaluates a list of `Example`s and returns a dictionary
of scores referring to the scores provided by the components in the
pipeline
Significant differences:
* `tags_acc` is renamed to `tag_acc` to be consistent with `token_acc`
and the new `morph_acc`, `pos_acc`, and `lemma_acc`
* Scoring is no longer cumulative: `Scorer.score` scores a list of
examples rather than a single example and does not retain any state
about previously scored examples
* PRF values in the returned scores are no longer multiplied by 100
* Add kwargs to Morphologizer.evaluate
* Create generalized scoring methods in Scorer
* Generalized static scoring methods are added to `Scorer`
* Methods require an attribute (either on Token or Doc) that is
used to key the returned scores
Naming differences:
* `uas`, `las`, and `las_per_type` in the scores dict are renamed to
`dep_uas`, `dep_las`, and `dep_las_per_type`
Scoring differences:
* `Doc.sents` is now scored as spans rather than on sentence-initial
token positions so that `Doc.sents` and `Doc.ents` can be scored with
the same method (this lowers scores since a single incorrect sentence
start results in two incorrect spans)
* Simplify / extend hasattr check for eval method
* Add hasattr check to tokenizer scoring
* Simplify to hasattr check for component scoring
* Reset Example alignment if docs are set
Reset the Example alignment if either doc is set in case the
tokenization has changed.
* Add PRF tokenization scoring for tokens as spans
Add PRF scores for tokens as character spans. The scores are:
* token_acc: # correct tokens / # gold tokens
* token_p/r/f: PRF for (token.idx, token.idx + len(token))
* Add docstring to Scorer.score_tokenization
* Rename component.evaluate() to component.score()
* Update Scorer API docs
* Update scoring for positive_label in textcat
* Fix TextCategorizer.score kwargs
* Update Language.evaluate docs
* Update score names in default config
* Refactor Chinese tokenizer configuration
Refactor `ChineseTokenizer` configuration so that it uses a single
`segmenter` setting to choose between character segmentation, jieba, and
pkuseg.
* replace `use_jieba`, `use_pkuseg`, `require_pkuseg` with the setting
`segmenter` with the supported values: `char`, `jieba`, `pkuseg`
* make the default segmenter plain character segmentation `char` (no
additional libraries required)
* Fix Chinese serialization test to use char default
* Warn if attempting to customize other segmenter
Add a warning if `Chinese.pkuseg_update_user_dict` is called when
another segmenter is selected.
* Use cosine loss in Cloze multitask
* Fix char_embed for gpu
* Call resume_training for base model in train CLI
* Fix bilstm_depth default in pretrain command
* Implement character-based pretraining objective
* Use chars loss in ClozeMultitask
* Add method to decode predicted characters
* Fix number characters
* Rescale gradients for mlm
* Fix char embed+vectors in ml
* Fix pipes
* Fix pretrain args
* Move get_characters_loss
* Fix import
* Fix import
* Mention characters loss option in pretrain
* Remove broken 'self attention' option in pretrain
* Revert "Remove broken 'self attention' option in pretrain"
This reverts commit 56b820f6af.
* Document 'characters' objective of pretrain
* Add static method to Doc to allow merging of multiple docs.
* Add error description for the error that occurs if docs with different
vocabs (from different languages) are merged in Doc.from_docs().
* Add test for Doc.from_docs() implementation.
* Fix using numpy's concatenate in Doc.from_docs.
* Replace typing's type annotations in from_docs.
* Simply remove type annotations in from_docs.
* Add documentation for Doc.from_docs to api.
* Simplify from_docs, its test and the api doc for codebase consistency.
* Fix merging of Doc objects that end with whitespaces (Achieved by simply not setting the SPACY attribute on whitespace tokens). Remove two unnecessary imports of attributes.
* Add merging of user data from Doc objects in from_docs. Add user data test case to corresponding test. Add applicable warning messages.
* Fix incorrect setting of tokens idx by using concatenated spaces (again). Add test case to corresponding test.
* Add MORPH to attrs
* Update warnings calls
* Remove out-dated error from merge
* Rename space_delimiter to ensure_whitespace
Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com>
* Add version number to DocBin
Add a version number to DocBin for future use.
* Add POS to all attributes in DocBin
* Add morph string to strings in DocBin
* Update DocBin API
* Add string for ENT_KB_ID in DocBin
Very minor fix in docs, specifically in this part:
```
matcher = PhraseMatcher(nlp.vocab)
> for doc in matcher.pipe(texts, batch_size=50):
> pass
```
`texts` suggests the input is an iterable of strings. I replaced it for `docs`.
Very minor fix in docs, specifically in this part:
```
matcher = PhraseMatcher(nlp.vocab)
> for doc in matcher.pipe(texts, batch_size=50):
> pass
```
`texts` suggests the input is an iterable of strings. I replaced it for `docs`.
* Update website models for v2.3.0
* Add docs for Chinese word segmentation
* Tighten up Chinese docs section
* Merge branch 'master' into docs/v2.3.0 [ci skip]
* Merge branch 'master' into docs/v2.3.0 [ci skip]
* Auto-format and update version
* Update matcher.md
* Update languages and sorting
* Typo in landing page
* Infobox about token_match behavior
* Add meta and basic docs for Japanese
* POS -> TAG in models table
* Add info about lookups for normalization
* Updates to API docs for v2.3
* Update adding norm exceptions for adding languages
* Add --omit-extra-lookups to CLI API docs
* Add initial draft of "What's New in v2.3"
* Add new in v2.3 tags to Chinese and Japanese sections
* Add tokenizer to migration section
* Add new in v2.3 flags to init-model
* Typo
* More what's new in v2.3
Co-authored-by: Ines Montani <ines@ines.io>
* verbose and tag_map options
* adding init_tok2vec option and only changing the tok2vec that is specified
* adding omit_extra_lookups and verifying textcat config
* wip
* pretrain bugfix
* add replace and resume options
* train_textcat fix
* raw text functionality
* improve UX when KeyError or when input data can't be parsed
* avoid unnecessary access to goldparse in TextCat pipe
* save performance information in nlp.meta
* add noise_level to config
* move nn_parser's defaults to config file
* multitask in config - doesn't work yet
* scorer offering both F and AUC options, need to be specified in config
* add textcat verification code from old train script
* small fixes to config files
* clean up
* set default config for ner/parser to allow create_pipe to work as before
* two more test fixes
* small fixes
* cleanup
* fix NER pickling + additional unit test
* create_pipe as before
* added contextualSpellCheck in spacy universe meta
* removed extra formatting by code
* updated with permanent links
* run json linter used by spacy
* filled SCA
* updated the description
* make disable_pipes deprecated in favour of the new toggle_pipes
* rewrite disable_pipes statements
* update documentation
* remove bin/wiki_entity_linking folder
* one more fix
* remove deprecated link to documentation
* few more doc fixes
* add note about name change to the docs
* restore original disable_pipes
* small fixes
* fix typo
* fix error number to W096
* rename to select_pipes
* also make changes to the documentation
Co-authored-by: Matthew Honnibal <honnibal+gh@gmail.com>
To fix the slow tokenizer URL (#4374) and allow `token_match` to take
priority over prefixes and suffixes by default, introduce a new
tokenizer option for a token match pattern that's applied after prefixes
and suffixes but before infixes.
* `debug-data`: determine coverage of provided vectors
* `evaluate`: support `blank:lg` model to make it possible to just evaluate
tokenization
* `init-model`: add option to truncate vectors to N most frequent vectors
from word2vec file
* `train`:
* if training on GPU, only run evaluation/timing on CPU in the first
iteration
* if training is aborted, exit with a non-0 exit status
