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2bcceb80c4
* 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
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| .. | ||
| architectures.md | ||
| cli.md | ||
| corpus.md | ||
| cython-classes.md | ||
| cython-structs.md | ||
| cython.md | ||
| data-formats.md | ||
| dependencyparser.md | ||
| doc.md | ||
| docbin.md | ||
| entitylinker.md | ||
| entityrecognizer.md | ||
| entityruler.md | ||
| example.md | ||
| index.md | ||
| kb.md | ||
| language.md | ||
| lemmatizer.md | ||
| lexeme.md | ||
| lookups.md | ||
| matcher.md | ||
| morphologizer.md | ||
| phrasematcher.md | ||
| pipeline-functions.md | ||
| scorer.md | ||
| sentencerecognizer.md | ||
| sentencizer.md | ||
| span.md | ||
| stringstore.md | ||
| tagger.md | ||
| textcategorizer.md | ||
| tok2vec.md | ||
| token.md | ||
| tokenizer.md | ||
| top-level.md | ||
| vectors.md | ||
| vocab.md | ||