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