mirror of
https://github.com/explosion/spaCy.git
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282a3b49ea
* allow resizing of the parser model even when upper=False * update from spacy.TransitionBasedParser.v1 to v2 * bugfix
261 lines
9.5 KiB
Cython
261 lines
9.5 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 Model, Config
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from .transition_parser cimport Parser
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from ._parser_internals.arc_eager cimport ArcEager
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from .functions import merge_subtokens
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from ..language import Language
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from ._parser_internals import nonproj
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from ..scorer import Scorer
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from ..training import validate_examples
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default_model_config = """
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[model]
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@architectures = "spacy.TransitionBasedParser.v2"
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state_type = "parser"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = true
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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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"""
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DEFAULT_PARSER_MODEL = Config().from_str(default_model_config)["model"]
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@Language.factory(
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"parser",
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assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
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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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"learn_tokens": False,
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"min_action_freq": 30,
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"model": DEFAULT_PARSER_MODEL,
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},
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default_score_weights={
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"dep_uas": 0.5,
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"dep_las": 0.5,
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"dep_las_per_type": None,
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"sents_p": None,
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"sents_r": None,
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"sents_f": 0.0,
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},
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)
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def make_parser(
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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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learn_tokens: bool,
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min_action_freq: int
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):
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"""Create a transition-based DependencyParser component. The dependency parser
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jointly learns sentence segmentation and labelled dependency parsing, and can
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optionally learn to merge tokens that had been over-segmented by the tokenizer.
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The parser uses a variant of the non-monotonic arc-eager transition-system
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described by Honnibal and Johnson (2014), with the addition of a "break"
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transition to perform the sentence segmentation. Nivre's pseudo-projective
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dependency transformation is used to allow the parser to predict
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non-projective parses.
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The parser is trained using an imitation learning objective. The parser follows
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the actions predicted by the current weights, and at each state, determines
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which actions are compatible with the optimal parse that could be reached
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from the current state. The weights such that the scores assigned to the
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set of optimal actions is increased, while scores assigned to other
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actions are decreased. Note that more than one action may be optimal for
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a given state.
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model (Model): The model for the transition-based parser. The model needs
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to have a specific substructure of named components --- see the
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spacy.ml.tb_framework.TransitionModel for details.
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moves (List[str]): A list of transition names. Inferred from the data if not
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provided.
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update_with_oracle_cut_size (int):
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During training, cut long sequences into shorter segments by creating
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intermediate states based on the gold-standard history. The model is
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not very sensitive to this parameter, so you usually won't need to change
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it. 100 is a good default.
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learn_tokens (bool): Whether to learn to merge subtokens that are split
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relative to the gold standard. Experimental.
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min_action_freq (int): The minimum frequency of labelled actions to retain.
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Rarer labelled actions have their label backed-off to "dep". While this
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primarily affects the label accuracy, it can also affect the attachment
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structure, as the labels are used to represent the pseudo-projectivity
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transformation.
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"""
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return DependencyParser(
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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=[],
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learn_tokens=learn_tokens,
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min_action_freq=min_action_freq,
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beam_width=1,
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beam_density=0.0,
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beam_update_prob=0.0,
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)
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@Language.factory(
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"beam_parser",
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assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
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default_config={
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"beam_width": 8,
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"beam_density": 0.01,
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"beam_update_prob": 0.5,
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"moves": None,
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"update_with_oracle_cut_size": 100,
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"learn_tokens": False,
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"min_action_freq": 30,
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"model": DEFAULT_PARSER_MODEL,
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},
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default_score_weights={
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"dep_uas": 0.5,
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"dep_las": 0.5,
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"dep_las_per_type": None,
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"sents_p": None,
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"sents_r": None,
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"sents_f": 0.0,
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},
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)
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def make_beam_parser(
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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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learn_tokens: bool,
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min_action_freq: int,
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beam_width: int,
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beam_density: float,
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beam_update_prob: float,
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):
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"""Create a transition-based DependencyParser component that uses beam-search.
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The dependency parser jointly learns sentence segmentation and labelled
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dependency parsing, and can optionally learn to merge tokens that had been
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over-segmented by the tokenizer.
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|
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The parser uses a variant of the non-monotonic arc-eager transition-system
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described by Honnibal and Johnson (2014), with the addition of a "break"
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transition to perform the sentence segmentation. Nivre's pseudo-projective
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dependency transformation is used to allow the parser to predict
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non-projective parses.
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The parser is trained using a global objective. That is, it learns to assign
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probabilities to whole parses.
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model (Model): The model for the transition-based parser. The model needs
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to have a specific substructure of named components --- see the
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spacy.ml.tb_framework.TransitionModel for details.
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moves (List[str]): A list of transition names. Inferred from the data if not
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provided.
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beam_width (int): The number of candidate analyses to maintain.
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beam_density (float): The minimum ratio between the scores of the first and
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last candidates in the beam. This allows the parser to avoid exploring
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candidates that are too far behind. This is mostly intended to improve
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efficiency, but it can also improve accuracy as deeper search is not
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always better.
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beam_update_prob (float): The chance of making a beam update, instead of a
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greedy update. Greedy updates are an approximation for the beam updates,
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and are faster to compute.
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learn_tokens (bool): Whether to learn to merge subtokens that are split
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relative to the gold standard. Experimental.
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min_action_freq (int): The minimum frequency of labelled actions to retain.
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Rarer labelled actions have their label backed-off to "dep". While this
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primarily affects the label accuracy, it can also affect the attachment
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structure, as the labels are used to represent the pseudo-projectivity
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transformation.
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"""
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return DependencyParser(
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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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beam_width=beam_width,
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beam_density=beam_density,
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beam_update_prob=beam_update_prob,
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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 DependencyParser(Parser):
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"""Pipeline component for dependency parsing.
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DOCS: https://nightly.spacy.io/api/dependencyparser
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"""
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TransitionSystem = ArcEager
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@property
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def postprocesses(self):
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output = [nonproj.deprojectivize]
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if self.cfg.get("learn_tokens") is True:
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output.append(merge_subtokens)
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return tuple(output)
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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, nlp=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.initialize(get_examples, nlp=nlp)
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@property
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def labels(self):
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labels = set()
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# Get the labels from the model by looking at the available moves
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for move in self.move_names:
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if "-" in move:
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label = move.split("-")[1]
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if "||" in label:
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label = label.split("||")[1]
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labels.add(label)
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return tuple(sorted(labels))
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def score(self, examples, **kwargs):
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"""Score a batch of examples.
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examples (Iterable[Example]): The examples to score.
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RETURNS (Dict[str, Any]): The scores, produced by Scorer.score_spans
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and Scorer.score_deps.
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DOCS: https://nightly.spacy.io/api/dependencyparser#score
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"""
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def has_sents(doc):
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return doc.has_annotation("SENT_START")
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validate_examples(examples, "DependencyParser.score")
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def dep_getter(token, attr):
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dep = getattr(token, attr)
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dep = token.vocab.strings.as_string(dep).lower()
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return dep
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results = {}
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results.update(Scorer.score_spans(examples, "sents", has_annotation=has_sents, **kwargs))
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kwargs.setdefault("getter", dep_getter)
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kwargs.setdefault("ignore_labels", ("p", "punct"))
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results.update(Scorer.score_deps(examples, "dep", **kwargs))
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del results["sents_per_type"]
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return results
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