* 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
* 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`.
* 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
