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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
95 lines
2.7 KiB
Cython
95 lines
2.7 KiB
Cython
# cython: infer_types=True, profile=True, binding=True
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from typing import Optional, Iterable
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from thinc.api import CosineDistance, to_categorical, get_array_module, Model, Config
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from ..syntax.nn_parser cimport Parser
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from ..syntax.ner cimport BiluoPushDown
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from ..language import Language
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from ..scorer import Scorer
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default_model_config = """
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[model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 6
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hidden_width = 64
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maxout_pieces = 2
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[model.tok2vec]
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@architectures = "spacy.HashEmbedCNN.v1"
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pretrained_vectors = null
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width = 96
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depth = 4
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embed_size = 2000
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window_size = 1
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maxout_pieces = 3
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subword_features = true
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dropout = null
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"""
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DEFAULT_NER_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"ner",
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assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
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default_config={
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"moves": None,
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"update_with_oracle_cut_size": 100,
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"multitasks": [],
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"learn_tokens": False,
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"min_action_freq": 30,
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"model": DEFAULT_NER_MODEL,
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}
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)
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def make_ner(
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nlp: Language,
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name: str,
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model: Model,
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moves: Optional[list],
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update_with_oracle_cut_size: int,
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multitasks: Iterable,
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learn_tokens: bool,
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min_action_freq: int
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):
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return EntityRecognizer(
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nlp.vocab,
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model,
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name,
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moves=moves,
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update_with_oracle_cut_size=update_with_oracle_cut_size,
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multitasks=multitasks,
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learn_tokens=learn_tokens,
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min_action_freq=min_action_freq
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)
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cdef class EntityRecognizer(Parser):
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"""Pipeline component for named entity recognition.
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DOCS: https://spacy.io/api/entityrecognizer
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"""
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TransitionSystem = BiluoPushDown
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def add_multitask_objective(self, mt_component):
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self._multitasks.append(mt_component)
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def init_multitask_objectives(self, get_examples, pipeline, sgd=None, **cfg):
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# TODO: transfer self.model.get_ref("tok2vec") to the multitask's model ?
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for labeller in self._multitasks:
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labeller.model.set_dim("nO", len(self.labels))
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if labeller.model.has_ref("output_layer"):
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labeller.model.get_ref("output_layer").set_dim("nO", len(self.labels))
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labeller.begin_training(get_examples, pipeline=pipeline)
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@property
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def labels(self):
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# Get the labels from the model by looking at the available moves, e.g.
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# B-PERSON, I-PERSON, L-PERSON, U-PERSON
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labels = set(move.split("-")[1] for move in self.move_names
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if move[0] in ("B", "I", "L", "U"))
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return tuple(sorted(labels))
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def score(self, examples, **kwargs):
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return Scorer.score_spans(examples, "ents", **kwargs)
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