Add docstring for parser

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Matthew Honnibal 2020-08-07 14:59:34 +02:00
parent ef2c67cca5
commit 7ef8a64df9

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@ -8,13 +8,64 @@ from ..tb_framework import TransitionModel
@registry.architectures.register("spacy.TransitionBasedParser.v1") @registry.architectures.register("spacy.TransitionBasedParser.v1")
def build_tb_parser_model( def build_tb_parser_model(
tok2vec: Model, tok2vec: Model[List[Doc], List[Floats2d]],
nr_feature_tokens: int, nr_feature_tokens: int,
hidden_width: int, hidden_width: int,
maxout_pieces: int, maxout_pieces: int,
use_upper: bool = True, use_upper: bool = True,
nO: Optional[int] = None, nO: Optional[int] = None,
) -> Model: ) -> 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
ued 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 t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
tok2vec = chain(tok2vec, list2array(), Linear(hidden_width, t2v_width),) tok2vec = chain(tok2vec, list2array(), Linear(hidden_width, t2v_width),)
tok2vec.set_dim("nO", hidden_width) tok2vec.set_dim("nO", hidden_width)