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state_type and extra_state_tokens instead of nr_feature_tokens

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This commit is contained in:
svlandeg 2020-09-23 13:35:09 +02:00
parent ae5dacf75f
commit 6c85fab316
7 changed files with 48 additions and 35 deletions
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12
spacy/cli/templates/quickstart_training.jinja
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@ -59,7 +59,8 @@ factory = "parser"
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
state_type = "deps"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = false
@ -79,7 +80,8 @@ factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 3
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = false
@ -183,7 +185,8 @@ factory = "parser"
[components.parser.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
state_type = "deps"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = true
@ -200,7 +203,8 @@ factory = "ner"
[components.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 6
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2
use_upper = true

31
spacy/ml/models/parser.py
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@ -11,7 +11,8 @@ 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,
state_type: str,
extra_state_tokens: bool,
hidden_width: int,
maxout_pieces: int,
use_upper: bool = True,
@ -40,20 +41,12 @@ def build_tb_parser_model(
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
state_type (str):
String value denoting the type of parser model: "deps" 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. If 1, the maxout non-linearity
@ -68,8 +61,14 @@ def build_tb_parser_model(
Usually inferred from data at the beginning of training, or loaded from
disk.
"""
if state_type == "deps":
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(f"unknown state type {state_type}") # TODO error
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)
lower = PrecomputableAffine(
nO=hidden_width if use_upper else nO,

3
spacy/pipeline/dep_parser.pyx
View File

@ -15,7 +15,8 @@ from ..training import validate_examples
default_model_config = """
[model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 8
state_type = "deps"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2

3
spacy/pipeline/ner.pyx
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@ -13,7 +13,8 @@ from ..training import validate_examples
default_model_config = """
[model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 6
state_type = "ner"
extra_state_tokens = false
hidden_width = 64
maxout_pieces = 2

9
spacy/tests/serialize/test_serialize_config.py
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@ -67,7 +67,8 @@ width = ${components.tok2vec.model.width}
parser_config_string = """
[model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 99
state_type = "deps"
extra_state_tokens = false
hidden_width = 66
maxout_pieces = 2
@ -95,7 +96,11 @@ def my_parser():
MaxoutWindowEncoder(width=321, window_size=3, maxout_pieces=4, depth=2),
)
parser = build_tb_parser_model(
tok2vec=tok2vec, nr_feature_tokens=7, hidden_width=65, maxout_pieces=5
tok2vec=tok2vec,
state_type="deps",
extra_state_tokens=True,
hidden_width=65,
maxout_pieces=5,
)
return parser

8
website/docs/api/architectures.md
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@ -414,7 +414,8 @@ one component.
> ```ini
> [model]
> @architectures = "spacy.TransitionBasedParser.v1"
> nr_feature_tokens = 6
> state_type = "ner"
> extra_state_tokens = false
> hidden_width = 64
> maxout_pieces = 2
>
@ -447,9 +448,10 @@ consists of either two or three subnetworks:
as action scores directly.
| Name | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tok2vec` | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~ |
| `nr_feature_tokens` | 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 entity recognizer. The recommended feature sets are `3` for NER, and `8` for the dependency parser. ~~int~~ |
| `state_type` | Which task to extract features for. Possible values are "ner" and "dependencies". ~~str~~ |
| `extra_state_tokens` | Whether to use an expanded feature set when extracting the state tokens. Slightly slower, but sometimes improves accuracy slightly. Defaults to `False`. ~~bool~~ |
| `hidden_width` | The width of the hidden layer. ~~int~~ |
| `maxout_pieces` | 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`](https://thinc.ai/docs/api-layers#relu) non-linearity if `use_upper` is `True`, and no non-linearity if `False`. ~~int~~ |
| `use_upper` | 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 `True` 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. ~~bool~~ |

3
website/docs/usage/embeddings-transformers.md
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@ -448,7 +448,8 @@ factory = "ner"
[nlp.pipeline.ner.model]
@architectures = "spacy.TransitionBasedParser.v1"
nr_feature_tokens = 3
state_type = "ner"
extra_state_tokens = false
hidden_width = 128
maxout_pieces = 3
use_upper = false