spaCy/spacy/ml/models/parser.py

from typing import Optional, List, Tuple, Any
from thinc.types import Floats2d
from thinc.api import Model
import warnings

from ...errors import Errors, Warnings
from ...compat import Literal
from ...util import registry
from ..tb_framework import TransitionModel
from ...tokens.doc import Doc

TransitionSystem = Any  # TODO
State = Any  # TODO


@registry.architectures.register("spacy.TransitionBasedParser.v2")
def transition_parser_v2(
    tok2vec: Model[List[Doc], List[Floats2d]],
    state_type: Literal["parser", "ner"],
    extra_state_tokens: bool,
    hidden_width: int,
    maxout_pieces: int,
    use_upper: bool,
    nO: Optional[int] = None,
) -> Model:
    if not use_upper:
        warnings.warn(Warnings.W400)

    return build_tb_parser_model(
        tok2vec,
        state_type,
        extra_state_tokens,
        hidden_width,
        maxout_pieces,
        nO=nO,
    )


@registry.architectures.register("spacy.TransitionBasedParser.v3")
def transition_parser_v3(
    tok2vec: Model[List[Doc], List[Floats2d]],
    state_type: Literal["parser", "ner"],
    extra_state_tokens: bool,
    hidden_width: int,
    maxout_pieces: int,
    nO: Optional[int] = None,
) -> Model:
    return build_tb_parser_model(
        tok2vec,
        state_type,
        extra_state_tokens,
        hidden_width,
        maxout_pieces,
        nO=nO,
    )


def build_tb_parser_model(
    tok2vec: Model[List[Doc], List[Floats2d]],
    state_type: Literal["parser", "ner"],
    extra_state_tokens: bool,
    hidden_width: int,
    maxout_pieces: int,
    nO: Optional[int] = None,
) -> Model:
    """
    Build a transition-based parser model. Can apply to NER or dependency-parsing.

    Transition-based parsing is an approach to structured prediction where the
    task of predicting the structure is mapped to a series of state transitions.
    You might find this tutorial helpful as background:
    https://explosion.ai/blog/parsing-english-in-python

    The neural network state prediction model consists of either two or three
    subnetworks:

    * tok2vec: Map each token into a vector representations. This subnetwork
        is run once for each batch.
    * lower: Construct a feature-specific vector for each (token, feature) pair.
        This is also run once for each batch. Constructing the state
        representation is then simply a matter of summing the component features
        and applying the non-linearity.
    * upper (optional): A feed-forward network that predicts scores from the
        state representation. If not present, the output from the lower model is
        used as action scores directly.

    tok2vec (Model[List[Doc], List[Floats2d]]):
        Subnetwork to map tokens into vector representations.
    state_type (str):
        String value denoting the type of parser model: "parser" or "ner"
    extra_state_tokens (bool): Whether or not to use additional tokens in the context
        to construct the state vector. Defaults to `False`, which means 3 and 8
        for the NER and parser respectively. When set to `True`, this would become 6
        feature sets (for the NER) or 13 (for the parser).
    hidden_width (int): The width of the hidden layer.
    maxout_pieces (int): How many pieces to use in the state prediction layer.
        Recommended values are 1, 2 or 3.
    nO (int or None): The number of actions the model will predict between.
        Usually inferred from data at the beginning of training, or loaded from
        disk.
    """
    if state_type == "parser":
        nr_feature_tokens = 13 if extra_state_tokens else 8
    elif state_type == "ner":
        nr_feature_tokens = 6 if extra_state_tokens else 3
    else:
        raise ValueError(Errors.E917.format(value=state_type))
    return TransitionModel(
        tok2vec=tok2vec,
        state_tokens=nr_feature_tokens,
        hidden_width=hidden_width,
        maxout_pieces=maxout_pieces,
        nO=nO,
        unseen_classes=set(),
    )