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ec45f704b1
* Drop python 3.6/3.7, remove unneeded compat * Remove unused import * Minimal python 3.8+ docs updates
115 lines
3.9 KiB
Python
115 lines
3.9 KiB
Python
from typing import Optional, List, Tuple, Any, Literal
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from thinc.types import Floats2d
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from thinc.api import Model
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import warnings
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from ...errors import Errors, Warnings
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from ...util import registry
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from ..tb_framework import TransitionModel
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from ...tokens.doc import Doc
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TransitionSystem = Any # TODO
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State = Any # TODO
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@registry.architectures.register("spacy.TransitionBasedParser.v2")
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def transition_parser_v2(
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tok2vec: Model[List[Doc], List[Floats2d]],
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state_type: Literal["parser", "ner"],
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extra_state_tokens: bool,
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hidden_width: int,
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maxout_pieces: int,
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use_upper: bool,
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nO: Optional[int] = None,
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) -> Model:
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if not use_upper:
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warnings.warn(Warnings.W400)
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return build_tb_parser_model(
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tok2vec,
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state_type,
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extra_state_tokens,
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hidden_width,
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maxout_pieces,
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nO=nO,
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)
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@registry.architectures.register("spacy.TransitionBasedParser.v3")
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def transition_parser_v3(
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tok2vec: Model[List[Doc], List[Floats2d]],
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state_type: Literal["parser", "ner"],
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extra_state_tokens: bool,
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hidden_width: int,
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maxout_pieces: int,
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nO: Optional[int] = None,
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) -> Model:
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return build_tb_parser_model(
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tok2vec,
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state_type,
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extra_state_tokens,
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hidden_width,
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maxout_pieces,
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nO=nO,
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)
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def build_tb_parser_model(
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tok2vec: Model[List[Doc], List[Floats2d]],
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state_type: Literal["parser", "ner"],
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extra_state_tokens: bool,
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hidden_width: int,
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maxout_pieces: int,
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nO: Optional[int] = None,
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) -> Model:
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"""
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Build a transition-based parser model. Can apply to NER or dependency-parsing.
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Transition-based parsing is an approach to structured prediction where the
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task of predicting the structure is mapped to a series of state transitions.
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You might find this tutorial helpful as background:
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https://explosion.ai/blog/parsing-english-in-python
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The neural network state prediction model consists of either two or three
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subnetworks:
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* tok2vec: Map each token into a vector representations. This subnetwork
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is run once for each batch.
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* lower: Construct a feature-specific vector for each (token, feature) pair.
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This is also run once for each batch. Constructing the state
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representation is then simply a matter of summing the component features
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and applying the non-linearity.
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* upper (optional): A feed-forward network that predicts scores from the
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state representation. If not present, the output from the lower model is
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used as action scores directly.
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tok2vec (Model[List[Doc], List[Floats2d]]):
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Subnetwork to map tokens into vector representations.
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state_type (str):
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String value denoting the type of parser model: "parser" or "ner"
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extra_state_tokens (bool): Whether or not to use additional tokens in the context
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to construct the state vector. Defaults to `False`, which means 3 and 8
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for the NER and parser respectively. When set to `True`, this would become 6
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feature sets (for the NER) or 13 (for the parser).
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hidden_width (int): The width of the hidden layer.
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maxout_pieces (int): How many pieces to use in the state prediction layer.
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Recommended values are 1, 2 or 3.
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nO (int or None): The number of actions the model will predict between.
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Usually inferred from data at the beginning of training, or loaded from
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disk.
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"""
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if state_type == "parser":
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nr_feature_tokens = 13 if extra_state_tokens else 8
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elif state_type == "ner":
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nr_feature_tokens = 6 if extra_state_tokens else 3
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else:
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raise ValueError(Errors.E917.format(value=state_type))
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return TransitionModel(
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tok2vec=tok2vec,
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state_tokens=nr_feature_tokens,
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hidden_width=hidden_width,
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maxout_pieces=maxout_pieces,
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nO=nO,
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unseen_classes=set(),
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)
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