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Merge pull request #6379 from svlandeg/fix/labels-constructor
This commit is contained in:
Ines Montani
GitHub
commit
82e88f0e3b
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@ -67,9 +67,6 @@ class Morphologizer(Tagger):
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vocab: Vocab,
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model: Model,
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name: str = "morphologizer",
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*,
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labels_morph: Optional[dict] = None,
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labels_pos: Optional[dict] = None,
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):
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"""Initialize a morphologizer.
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@ -77,8 +74,6 @@ class Morphologizer(Tagger):
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model (thinc.api.Model): The Thinc Model powering the pipeline component.
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name (str): The component instance name, used to add entries to the
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losses during training.
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labels_morph (dict): Mapping of morph + POS tags to morph labels.
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labels_pos (dict): Mapping of morph + POS tags to POS tags.
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DOCS: https://nightly.spacy.io/api/morphologizer#init
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"""
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@ -90,7 +85,7 @@ class Morphologizer(Tagger):
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# store mappings from morph+POS labels to token-level annotations:
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# 1) labels_morph stores a mapping from morph+POS->morph
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# 2) labels_pos stores a mapping from morph+POS->POS
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cfg = {"labels_morph": labels_morph or {}, "labels_pos": labels_pos or {}}
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cfg = {"labels_morph": {}, "labels_pos": {}}
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self.cfg = dict(sorted(cfg.items()))
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@property
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@ -47,7 +47,7 @@ class MultitaskObjective(Tagger):
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side-objective.
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"""
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def __init__(self, vocab, model, name="nn_labeller", *, labels, target):
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def __init__(self, vocab, model, name="nn_labeller", *, target):
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self.vocab = vocab
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self.model = model
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self.name = name
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@ -67,7 +67,7 @@ class MultitaskObjective(Tagger):
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self.make_label = target
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else:
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raise ValueError(Errors.E016)
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cfg = {"labels": labels or {}, "target": target}
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cfg = {"labels": {}, "target": target}
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self.cfg = dict(cfg)
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@property
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@ -81,15 +81,18 @@ class MultitaskObjective(Tagger):
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def set_annotations(self, docs, dep_ids):
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pass
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def initialize(self, get_examples, nlp=None):
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def initialize(self, get_examples, nlp=None, labels=None):
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if not hasattr(get_examples, "__call__"):
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err = Errors.E930.format(name="MultitaskObjective", obj=type(get_examples))
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raise ValueError(err)
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for example in get_examples():
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for token in example.y:
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label = self.make_label(token)
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if label is not None and label not in self.labels:
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self.labels[label] = len(self.labels)
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if labels is not None:
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self.labels = labels
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else:
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for example in get_examples():
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for token in example.y:
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label = self.make_label(token)
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if label is not None and label not in self.labels:
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self.labels[label] = len(self.labels)
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self.model.initialize() # TODO: fix initialization by defining X and Y
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def predict(self, docs):
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@ -61,14 +61,13 @@ class Tagger(TrainablePipe):
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DOCS: https://nightly.spacy.io/api/tagger
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"""
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def __init__(self, vocab, model, name="tagger", *, labels=None):
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def __init__(self, vocab, model, name="tagger"):
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"""Initialize a part-of-speech tagger.
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vocab (Vocab): The shared vocabulary.
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model (thinc.api.Model): The Thinc Model powering the pipeline component.
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name (str): The component instance name, used to add entries to the
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losses during training.
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labels (List): The set of labels. Defaults to None.
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DOCS: https://nightly.spacy.io/api/tagger#init
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"""
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@ -76,7 +75,7 @@ class Tagger(TrainablePipe):
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self.model = model
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self.name = name
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self._rehearsal_model = None
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cfg = {"labels": labels or []}
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cfg = {"labels": []}
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self.cfg = dict(sorted(cfg.items()))
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@property
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@ -66,9 +66,6 @@ shortcut for this and instantiate the component using its string name and
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | The [`Model`](https://thinc.ai/docs/api-model) powering the pipeline component. ~~Model[List[Doc], List[Floats2d]]~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| _keyword-only_ | |
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| `labels_morph` | Mapping of morph + POS tags to morph labels. ~~Dict[str, str]~~ |
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| `labels_pos` | Mapping of morph + POS tags to POS tags. ~~Dict[str, str]~~ |
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## Morphologizer.\_\_call\_\_ {#call tag="method"}
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@ -21,16 +21,12 @@ architectures and their arguments and hyperparameters.
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>
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> ```python
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> from spacy.pipeline.tagger import DEFAULT_TAGGER_MODEL
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> config = {
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> "set_morphology": False,
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> "model": DEFAULT_TAGGER_MODEL,
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> }
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> config = {"model": DEFAULT_TAGGER_MODEL}
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> nlp.add_pipe("tagger", config=config)
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> ```
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| Setting | Description |
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| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `set_morphology` | Whether to set morphological features. Defaults to `False`. ~~bool~~ |
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| `model` | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). Defaults to [Tagger](/api/architectures#Tagger). ~~Model[List[Doc], List[Floats2d]]~~ |
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```python
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@ -63,8 +59,6 @@ shortcut for this and instantiate the component using its string name and
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| `vocab` | The shared vocabulary. ~~Vocab~~ |
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| `model` | A model instance that predicts the tag probabilities. The output vectors should match the number of tags in size, and be normalized as probabilities (all scores between 0 and 1, with the rows summing to `1`). ~~Model[List[Doc], List[Floats2d]]~~ |
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| `name` | String name of the component instance. Used to add entries to the `losses` during training. ~~str~~ |
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| _keyword-only_ | |
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| `set_morphology` | Whether to set morphological features. ~~bool~~ |
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## Tagger.\_\_call\_\_ {#call tag="method"}
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