from typing import Optional, List from thinc.api import Model, chain, list2array, Linear, zero_init, use_ops from thinc.types import Floats2d from ...util import registry from .._precomputable_affine import PrecomputableAffine from ..tb_framework import TransitionModel from ...tokens import Doc @registry.architectures.register("spacy.TransitionBasedParser.v1") def build_tb_parser_model( tok2vec: Model[List[Doc], List[Floats2d]], nr_feature_tokens: int, hidden_width: int, maxout_pieces: int, use_upper: bool = True, 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. nr_feature_tokens (int): The number of tokens in the context to use to construct the state vector. Valid choices are 1, 2, 3, 6, 8 and 13. The 2, 8 and 13 feature sets are designed for the parser, while the 3 and 6 feature sets are designed for the NER. The recommended feature sets are 3 for NER, and 8 for the dependency parser. TODO: This feature should be split into two, state_type: ["deps", "ner"] and extra_state_features: [True, False]. This would map into: (deps, False): 8 (deps, True): 13 (ner, False): 3 (ner, True): 6 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. If 1, the maxout non-linearity is replaced with a ReLu non-linearity if use_upper=True, and no non-linearity if use_upper=False. use_upper (bool): Whether to use an additional hidden layer after the state vector in order to predict the action scores. It is recommended to set this to False for large pretrained models such as transformers, and False for smaller networks. The upper layer is computed on CPU, which becomes a bottleneck on larger GPU-based models, where it's also less necessary. 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. """ t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None tok2vec = chain(tok2vec, list2array(), Linear(hidden_width, t2v_width),) tok2vec.set_dim("nO", hidden_width) lower = PrecomputableAffine( nO=hidden_width if use_upper else nO, nF=nr_feature_tokens, nI=tok2vec.get_dim("nO"), nP=maxout_pieces, ) if use_upper: with use_ops("numpy"): # Initialize weights at zero, as it's a classification layer. upper = Linear(nO=nO, init_W=zero_init) else: upper = None return TransitionModel(tok2vec, lower, upper)