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state_type and extra_state_tokens instead of nr_feature_tokens
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svlandeg
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ae5dacf75f
commit
6c85fab316
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@ -59,7 +59,8 @@ factory = "parser"
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[components.parser.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 8
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state_type = "deps"
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extra_state_tokens = false
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hidden_width = 128
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maxout_pieces = 3
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use_upper = false
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@ -79,7 +80,8 @@ factory = "ner"
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[components.ner.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 3
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state_type = "ner"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = false
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@ -183,7 +185,8 @@ factory = "parser"
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[components.parser.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 8
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state_type = "deps"
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extra_state_tokens = false
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hidden_width = 128
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maxout_pieces = 3
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use_upper = true
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@ -200,7 +203,8 @@ factory = "ner"
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[components.ner.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 6
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state_type = "ner"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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use_upper = true
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@ -11,7 +11,8 @@ 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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state_type: str,
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extra_state_tokens: bool,
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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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@ -40,20 +41,12 @@ def build_tb_parser_model(
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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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state_type (str):
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String value denoting the type of parser model: "deps" or "ner"
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extra_state_tokens (bool): Whether or not to use additional tokens in the context
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to construct the state vector. Defaults to `False`, which means 3 and 8
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for the NER and parser respectively. When set to `True`, this would become 6
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feature sets (for the NER) or 13 (for the parser).
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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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@ -68,8 +61,14 @@ def build_tb_parser_model(
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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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if state_type == "deps":
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nr_feature_tokens = 13 if extra_state_tokens else 8
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elif state_type == "ner":
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nr_feature_tokens = 6 if extra_state_tokens else 3
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else:
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raise ValueError(f"unknown state type {state_type}") # TODO error
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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 = 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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@ -15,7 +15,8 @@ from ..training import validate_examples
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default_model_config = """
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[model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 8
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state_type = "deps"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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@ -13,7 +13,8 @@ from ..training import validate_examples
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default_model_config = """
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[model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 6
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state_type = "ner"
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extra_state_tokens = false
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hidden_width = 64
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maxout_pieces = 2
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@ -67,7 +67,8 @@ width = ${components.tok2vec.model.width}
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parser_config_string = """
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[model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 99
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state_type = "deps"
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extra_state_tokens = false
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hidden_width = 66
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maxout_pieces = 2
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@ -95,7 +96,11 @@ def my_parser():
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MaxoutWindowEncoder(width=321, window_size=3, maxout_pieces=4, depth=2),
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)
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parser = build_tb_parser_model(
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tok2vec=tok2vec, nr_feature_tokens=7, hidden_width=65, maxout_pieces=5
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tok2vec=tok2vec,
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state_type="deps",
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extra_state_tokens=True,
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hidden_width=65,
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maxout_pieces=5,
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)
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return parser
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@ -414,7 +414,8 @@ one component.
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> ```ini
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> [model]
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> @architectures = "spacy.TransitionBasedParser.v1"
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> nr_feature_tokens = 6
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> state_type = "ner"
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> extra_state_tokens = false
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> hidden_width = 64
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> maxout_pieces = 2
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>
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@ -446,15 +447,16 @@ consists of either two or three subnetworks:
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state representation. If not present, the output from the lower model is used
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as action scores directly.
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| Name | Description |
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| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `tok2vec` | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~ |
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| `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~~ |
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| `hidden_width` | The width of the hidden layer. ~~int~~ |
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| `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~~ |
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| `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~~ |
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| `nO` | The number of actions the model will predict between. Usually inferred from data at the beginning of training, or loaded from disk. ~~int~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Docs], List[List[Floats2d]]]~~ |
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| Name | Description |
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| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `tok2vec` | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~ |
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| `state_type` | Which task to extract features for. Possible values are "ner" and "dependencies". ~~str~~ |
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| `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~~ |
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| `hidden_width` | The width of the hidden layer. ~~int~~ |
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| `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~~ |
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| `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~~ |
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| `nO` | The number of actions the model will predict between. Usually inferred from data at the beginning of training, or loaded from disk. ~~int~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Docs], List[List[Floats2d]]]~~ |
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## Tagging architectures {#tagger source="spacy/ml/models/tagger.py"}
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@ -448,7 +448,8 @@ factory = "ner"
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[nlp.pipeline.ner.model]
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@architectures = "spacy.TransitionBasedParser.v1"
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nr_feature_tokens = 3
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state_type = "ner"
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extra_state_tokens = false
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hidden_width = 128
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maxout_pieces = 3
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use_upper = false
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