Remove component factory functions from component files

spacy/pipeline/attributeruler.py

@@ -22,10 +22,6 @@ TagMapType = Dict[str, Dict[Union[int, str], Union[int, str]]]
 MorphRulesType = Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]
 
 
-def make_attribute_ruler(
-    nlp: Language, name: str, validate: bool, scorer: Optional[Callable]
-):
-    return AttributeRuler(nlp.vocab, name, validate=validate, scorer=scorer)
 
 
 def attribute_ruler_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:

spacy/pipeline/dep_parser.pyx

@@ -39,145 +39,6 @@ subword_features = true
 DEFAULT_PARSER_MODEL = Config().from_str(default_model_config)["model"]
 
 
-def make_parser(
-    nlp: Language,
-    name: str,
-    model: Model,
-    moves: Optional[TransitionSystem],
-    update_with_oracle_cut_size: int,
-    learn_tokens: bool,
-    min_action_freq: int,
-    scorer: Optional[Callable],
-):
-    """Create a transition-based DependencyParser component. The dependency parser
-    jointly learns sentence segmentation and labelled dependency parsing, and can
-    optionally learn to merge tokens that had been over-segmented by the tokenizer.
-
-    The parser uses a variant of the non-monotonic arc-eager transition-system
-    described by Honnibal and Johnson (2014), with the addition of a "break"
-    transition to perform the sentence segmentation. Nivre's pseudo-projective
-    dependency transformation is used to allow the parser to predict
-    non-projective parses.
-
-    The parser is trained using an imitation learning objective. The parser follows
-    the actions predicted by the current weights, and at each state, determines
-    which actions are compatible with the optimal parse that could be reached
-    from the current state. The weights such that the scores assigned to the
-    set of optimal actions is increased, while scores assigned to other
-    actions are decreased. Note that more than one action may be optimal for
-    a given state.
-
-    model (Model): The model for the transition-based parser. The model needs
-        to have a specific substructure of named components --- see the
-        spacy.ml.tb_framework.TransitionModel for details.
-    moves (Optional[TransitionSystem]): This defines how the parse-state is created,
-        updated and evaluated. If 'moves' is None, a new instance is
-        created with `self.TransitionSystem()`. Defaults to `None`.
-    update_with_oracle_cut_size (int): During training, cut long sequences into
-        shorter segments by creating intermediate states based on the gold-standard
-        history. The model is not very sensitive to this parameter, so you usually
-        won't need to change it. 100 is a good default.
-    learn_tokens (bool): Whether to learn to merge subtokens that are split
-        relative to the gold standard. Experimental.
-    min_action_freq (int): The minimum frequency of labelled actions to retain.
-        Rarer labelled actions have their label backed-off to "dep". While this
-        primarily affects the label accuracy, it can also affect the attachment
-        structure, as the labels are used to represent the pseudo-projectivity
-        transformation.
-    scorer (Optional[Callable]): The scoring method.
-    """
-    return DependencyParser(
-        nlp.vocab,
-        model,
-        name,
-        moves=moves,
-        update_with_oracle_cut_size=update_with_oracle_cut_size,
-        multitasks=[],
-        learn_tokens=learn_tokens,
-        min_action_freq=min_action_freq,
-        beam_width=1,
-        beam_density=0.0,
-        beam_update_prob=0.0,
-        # At some point in the future we can try to implement support for
-        # partial annotations, perhaps only in the beam objective.
-        incorrect_spans_key=None,
-        scorer=scorer,
-    )
-
-
-def make_beam_parser(
-    nlp: Language,
-    name: str,
-    model: Model,
-    moves: Optional[TransitionSystem],
-    update_with_oracle_cut_size: int,
-    learn_tokens: bool,
-    min_action_freq: int,
-    beam_width: int,
-    beam_density: float,
-    beam_update_prob: float,
-    scorer: Optional[Callable],
-):
-    """Create a transition-based DependencyParser component that uses beam-search.
-    The dependency parser jointly learns sentence segmentation and labelled
-    dependency parsing, and can optionally learn to merge tokens that had been
-    over-segmented by the tokenizer.
-
-    The parser uses a variant of the non-monotonic arc-eager transition-system
-    described by Honnibal and Johnson (2014), with the addition of a "break"
-    transition to perform the sentence segmentation. Nivre's pseudo-projective
-    dependency transformation is used to allow the parser to predict
-    non-projective parses.
-
-    The parser is trained using a global objective. That is, it learns to assign
-    probabilities to whole parses.
-
-    model (Model): The model for the transition-based parser. The model needs
-        to have a specific substructure of named components --- see the
-        spacy.ml.tb_framework.TransitionModel for details.
-    moves (Optional[TransitionSystem]): This defines how the parse-state is created,
-        updated and evaluated. If 'moves' is None, a new instance is
-        created with `self.TransitionSystem()`. Defaults to `None`.
-    update_with_oracle_cut_size (int): During training, cut long sequences into
-        shorter segments by creating intermediate states based on the gold-standard
-        history. The model is not very sensitive to this parameter, so you usually
-        won't need to change it. 100 is a good default.
-    beam_width (int): The number of candidate analyses to maintain.
-    beam_density (float): The minimum ratio between the scores of the first and
-        last candidates in the beam. This allows the parser to avoid exploring
-        candidates that are too far behind. This is mostly intended to improve
-        efficiency, but it can also improve accuracy as deeper search is not
-        always better.
-    beam_update_prob (float): The chance of making a beam update, instead of a
-        greedy update. Greedy updates are an approximation for the beam updates,
-        and are faster to compute.
-    learn_tokens (bool): Whether to learn to merge subtokens that are split
-        relative to the gold standard. Experimental.
-    min_action_freq (int): The minimum frequency of labelled actions to retain.
-        Rarer labelled actions have their label backed-off to "dep". While this
-        primarily affects the label accuracy, it can also affect the attachment
-        structure, as the labels are used to represent the pseudo-projectivity
-        transformation.
-    """
-    return DependencyParser(
-        nlp.vocab,
-        model,
-        name,
-        moves=moves,
-        update_with_oracle_cut_size=update_with_oracle_cut_size,
-        beam_width=beam_width,
-        beam_density=beam_density,
-        beam_update_prob=beam_update_prob,
-        multitasks=[],
-        learn_tokens=learn_tokens,
-        min_action_freq=min_action_freq,
-        # At some point in the future we can try to implement support for
-        # partial annotations, perhaps only in the beam objective.
-        incorrect_spans_key=None,
-        scorer=scorer,
-    )
-
-
 def parser_score(examples, **kwargs):
     """Score a batch of examples.
 

spacy/pipeline/edit_tree_lemmatizer.py

@@ -39,27 +39,6 @@ subword_features = true
 DEFAULT_EDIT_TREE_LEMMATIZER_MODEL = Config().from_str(default_model_config)["model"]
 
 
-def make_edit_tree_lemmatizer(
-    nlp: Language,
-    name: str,
-    model: Model,
-    backoff: Optional[str],
-    min_tree_freq: int,
-    overwrite: bool,
-    top_k: int,
-    scorer: Optional[Callable],
-):
-    """Construct an EditTreeLemmatizer component."""
-    return EditTreeLemmatizer(
-        nlp.vocab,
-        model,
-        name,
-        backoff=backoff,
-        min_tree_freq=min_tree_freq,
-        overwrite=overwrite,
-        top_k=top_k,
-        scorer=scorer,
-    )
 
 
 class EditTreeLemmatizer(TrainablePipe):

spacy/pipeline/entity_linker.py

@@ -40,84 +40,6 @@ subword_features = true
 DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
 
 
-def make_entity_linker(
-    nlp: Language,
-    name: str,
-    model: Model,
-    *,
-    labels_discard: Iterable[str],
-    n_sents: int,
-    incl_prior: bool,
-    incl_context: bool,
-    entity_vector_length: int,
-    get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
-    get_candidates_batch: Callable[
-        [KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
-    ],
-    generate_empty_kb: Callable[[Vocab, int], KnowledgeBase],
-    overwrite: bool,
-    scorer: Optional[Callable],
-    use_gold_ents: bool,
-    candidates_batch_size: int,
-    threshold: Optional[float] = None,
-):
-    """Construct an EntityLinker component.
-
-    model (Model[List[Doc], Floats2d]): A model that learns document vector
-        representations. Given a batch of Doc objects, it should return a single
-        array, with one row per item in the batch.
-    labels_discard (Iterable[str]): NER labels that will automatically get a "NIL" prediction.
-    n_sents (int): The number of neighbouring sentences to take into account.
-    incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
-    incl_context (bool): Whether or not to include the local context in the model.
-    entity_vector_length (int): Size of encoding vectors in the KB.
-    get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
-        produces a list of candidates, given a certain knowledge base and a textual mention.
-    get_candidates_batch (
-        Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]]
-        ): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
-    generate_empty_kb (Callable[[Vocab, int], KnowledgeBase]): Callable returning empty KnowledgeBase.
-    scorer (Optional[Callable]): The scoring method.
-    use_gold_ents (bool): Whether to copy entities from gold docs during training or not. If false, another
-        component must provide entity annotations.
-    candidates_batch_size (int): Size of batches for entity candidate generation.
-    threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
-        prediction is discarded. If None, predictions are not filtered by any threshold.
-    """
-
-    if not model.attrs.get("include_span_maker", False):
-        # The only difference in arguments here is that use_gold_ents and threshold aren't available.
-        return EntityLinker_v1(
-            nlp.vocab,
-            model,
-            name,
-            labels_discard=labels_discard,
-            n_sents=n_sents,
-            incl_prior=incl_prior,
-            incl_context=incl_context,
-            entity_vector_length=entity_vector_length,
-            get_candidates=get_candidates,
-            overwrite=overwrite,
-            scorer=scorer,
-        )
-    return EntityLinker(
-        nlp.vocab,
-        model,
-        name,
-        labels_discard=labels_discard,
-        n_sents=n_sents,
-        incl_prior=incl_prior,
-        incl_context=incl_context,
-        entity_vector_length=entity_vector_length,
-        get_candidates=get_candidates,
-        get_candidates_batch=get_candidates_batch,
-        generate_empty_kb=generate_empty_kb,
-        overwrite=overwrite,
-        scorer=scorer,
-        use_gold_ents=use_gold_ents,
-        candidates_batch_size=candidates_batch_size,
-        threshold=threshold,
-    )
 
 
 def entity_linker_score(examples, **kwargs):

spacy/pipeline/entityruler.py

@@ -19,26 +19,6 @@ DEFAULT_ENT_ID_SEP = "||"
 PatternType = Dict[str, Union[str, List[Dict[str, Any]]]]
 
 
-def make_entity_ruler(
-    nlp: Language,
-    name: str,
-    phrase_matcher_attr: Optional[Union[int, str]],
-    matcher_fuzzy_compare: Callable,
-    validate: bool,
-    overwrite_ents: bool,
-    ent_id_sep: str,
-    scorer: Optional[Callable],
-):
-    return EntityRuler(
-        nlp,
-        name,
-        phrase_matcher_attr=phrase_matcher_attr,
-        matcher_fuzzy_compare=matcher_fuzzy_compare,
-        validate=validate,
-        overwrite_ents=overwrite_ents,
-        ent_id_sep=ent_id_sep,
-        scorer=scorer,
-    )
 
 
 def entity_ruler_score(examples, **kwargs):

spacy/pipeline/functions.py

@@ -73,10 +73,6 @@ def merge_subtokens(doc: Doc, label: str = "subtok") -> Doc:
     return doc
 
 
-def make_token_splitter(
-    nlp: Language, name: str, *, min_length: int = 0, split_length: int = 0
-):
-    return TokenSplitter(min_length=min_length, split_length=split_length)
 
 
 class TokenSplitter:
@@ -136,8 +132,6 @@ class TokenSplitter:
         util.from_disk(path, serializers, [])
 
 
-def make_doc_cleaner(nlp: Language, name: str, *, attrs: Dict[str, Any], silent: bool):
-    return DocCleaner(attrs, silent=silent)
 
 
 class DocCleaner:

spacy/pipeline/lemmatizer.py

@@ -16,17 +16,6 @@ from ..vocab import Vocab
 from .pipe import Pipe
 
 
-def make_lemmatizer(
-    nlp: Language,
-    model: Optional[Model],
-    name: str,
-    mode: str,
-    overwrite: bool,
-    scorer: Optional[Callable],
-):
-    return Lemmatizer(
-        nlp.vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
-    )
 
 
 def lemmatizer_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:

spacy/pipeline/morphologizer.pyx

@@ -47,18 +47,6 @@ maxout_pieces = 3
 DEFAULT_MORPH_MODEL = Config().from_str(default_model_config)["model"]
 
 
-def make_morphologizer(
-    nlp: Language,
-    model: Model,
-    name: str,
-    overwrite: bool,
-    extend: bool,
-    label_smoothing: float,
-    scorer: Optional[Callable],
-):
-    return Morphologizer(nlp.vocab, model, name, overwrite=overwrite, extend=extend, label_smoothing=label_smoothing, scorer=scorer)
-
-
 def morphologizer_score(examples, **kwargs):
     def morph_key_getter(token, attr):
         return getattr(token, attr).key

spacy/pipeline/multitask.pyx

@@ -30,10 +30,6 @@ subword_features = true
 DEFAULT_MT_MODEL = Config().from_str(default_model_config)["model"]
 
 
-def make_nn_labeller(nlp: Language, name: str, model: Model, labels: Optional[dict], target: str):
-    return MultitaskObjective(nlp.vocab, model, name)
-
-
 class MultitaskObjective(Tagger):
     """Experimental: Assist training of a parser or tagger, by training a
     side-objective.

spacy/pipeline/ner.pyx

@@ -36,121 +36,6 @@ subword_features = true
 DEFAULT_NER_MODEL = Config().from_str(default_model_config)["model"]
 
 
-def make_ner(
-    nlp: Language,
-    name: str,
-    model: Model,
-    moves: Optional[TransitionSystem],
-    update_with_oracle_cut_size: int,
-    incorrect_spans_key: Optional[str],
-    scorer: Optional[Callable],
-):
-    """Create a transition-based EntityRecognizer component. The entity recognizer
-    identifies non-overlapping labelled spans of tokens.
-
-    The transition-based algorithm used encodes certain assumptions that are
-    effective for "traditional" named entity recognition tasks, but may not be
-    a good fit for every span identification problem. Specifically, the loss
-    function optimizes for whole entity accuracy, so if your inter-annotator
-    agreement on boundary tokens is low, the component will likely perform poorly
-    on your problem. The transition-based algorithm also assumes that the most
-    decisive information about your entities will be close to their initial tokens.
-    If your entities are long and characterised by tokens in their middle, the
-    component will likely do poorly on your task.
-
-    model (Model): The model for the transition-based parser. The model needs
-        to have a specific substructure of named components --- see the
-        spacy.ml.tb_framework.TransitionModel for details.
-    moves (Optional[TransitionSystem]): This defines how the parse-state is created,
-        updated and evaluated. If 'moves' is None, a new instance is
-        created with `self.TransitionSystem()`. Defaults to `None`.
-    update_with_oracle_cut_size (int): During training, cut long sequences into
-        shorter segments by creating intermediate states based on the gold-standard
-        history. The model is not very sensitive to this parameter, so you usually
-        won't need to change it. 100 is a good default.
-    incorrect_spans_key (Optional[str]): Identifies spans that are known
-        to be incorrect entity annotations. The incorrect entity annotations
-        can be stored in the span group, under this key.
-    scorer (Optional[Callable]): The scoring method.
-    """
-    return EntityRecognizer(
-        nlp.vocab,
-        model,
-        name,
-        moves=moves,
-        update_with_oracle_cut_size=update_with_oracle_cut_size,
-        incorrect_spans_key=incorrect_spans_key,
-        multitasks=[],
-        beam_width=1,
-        beam_density=0.0,
-        beam_update_prob=0.0,
-        scorer=scorer,
-    )
-
-
-def make_beam_ner(
-    nlp: Language,
-    name: str,
-    model: Model,
-    moves: Optional[TransitionSystem],
-    update_with_oracle_cut_size: int,
-    beam_width: int,
-    beam_density: float,
-    beam_update_prob: float,
-    incorrect_spans_key: Optional[str],
-    scorer: Optional[Callable],
-):
-    """Create a transition-based EntityRecognizer component that uses beam-search.
-    The entity recognizer identifies non-overlapping labelled spans of tokens.
-
-    The transition-based algorithm used encodes certain assumptions that are
-    effective for "traditional" named entity recognition tasks, but may not be
-    a good fit for every span identification problem. Specifically, the loss
-    function optimizes for whole entity accuracy, so if your inter-annotator
-    agreement on boundary tokens is low, the component will likely perform poorly
-    on your problem. The transition-based algorithm also assumes that the most
-    decisive information about your entities will be close to their initial tokens.
-    If your entities are long and characterised by tokens in their middle, the
-    component will likely do poorly on your task.
-
-    model (Model): The model for the transition-based parser. The model needs
-        to have a specific substructure of named components --- see the
-        spacy.ml.tb_framework.TransitionModel for details.
-    moves (Optional[TransitionSystem]): This defines how the parse-state is created,
-        updated and evaluated. If 'moves' is None, a new instance is
-        created with `self.TransitionSystem()`. Defaults to `None`.
-    update_with_oracle_cut_size (int): During training, cut long sequences into
-        shorter segments by creating intermediate states based on the gold-standard
-        history. The model is not very sensitive to this parameter, so you usually
-        won't need to change it. 100 is a good default.
-    beam_width (int): The number of candidate analyses to maintain.
-    beam_density (float): The minimum ratio between the scores of the first and
-        last candidates in the beam. This allows the parser to avoid exploring
-        candidates that are too far behind. This is mostly intended to improve
-        efficiency, but it can also improve accuracy as deeper search is not
-        always better.
-    beam_update_prob (float): The chance of making a beam update, instead of a
-        greedy update. Greedy updates are an approximation for the beam updates,
-        and are faster to compute.
-    incorrect_spans_key (Optional[str]): Optional key into span groups of
-        entities known to be non-entities.
-    scorer (Optional[Callable]): The scoring method.
-    """
-    return EntityRecognizer(
-        nlp.vocab,
-        model,
-        name,
-        moves=moves,
-        update_with_oracle_cut_size=update_with_oracle_cut_size,
-        multitasks=[],
-        beam_width=beam_width,
-        beam_density=beam_density,
-        beam_update_prob=beam_update_prob,
-        incorrect_spans_key=incorrect_spans_key,
-        scorer=scorer,
-    )
-
-
 def ner_score(examples, **kwargs):
     return get_ner_prf(examples, **kwargs)
 

spacy/pipeline/senter.pyx

@@ -34,10 +34,6 @@ subword_features = true
 DEFAULT_SENTER_MODEL = Config().from_str(default_model_config)["model"]
 
 
-def make_senter(nlp: Language, name: str, model: Model, overwrite: bool, scorer: Optional[Callable]):
-    return SentenceRecognizer(nlp.vocab, model, name, overwrite=overwrite, scorer=scorer)
-
-
 def senter_score(examples, **kwargs):
     def has_sents(doc):
         return doc.has_annotation("SENT_START")

spacy/pipeline/span_finder.py

@@ -41,43 +41,6 @@ depth = 4
 DEFAULT_SPAN_FINDER_MODEL = Config().from_str(span_finder_default_config)["model"]
 
 
-def make_span_finder(
-    nlp: Language,
-    name: str,
-    model: Model[Iterable[Doc], Floats2d],
-    spans_key: str,
-    threshold: float,
-    max_length: Optional[int],
-    min_length: Optional[int],
-    scorer: Optional[Callable],
-) -> "SpanFinder":
-    """Create a SpanFinder component. The component predicts whether a token is
-    the start or the end of a potential span.
-
-    model (Model[List[Doc], Floats2d]): A model instance that
-        is given a list of documents and predicts a probability for each token.
-    spans_key (str): Key of the doc.spans dict to save the spans under. During
-        initialization and training, the component will look for spans on the
-        reference document under the same key.
-    threshold (float): Minimum probability to consider a prediction positive.
-    max_length (Optional[int]): Maximum length of the produced spans, defaults
-        to None meaning unlimited length.
-    min_length (Optional[int]): Minimum length of the produced spans, defaults
-        to None meaning shortest span length is 1.
-    scorer (Optional[Callable]): The scoring method. Defaults to
-        Scorer.score_spans for the Doc.spans[spans_key] with overlapping
-        spans allowed.
-    """
-    return SpanFinder(
-        nlp,
-        model=model,
-        threshold=threshold,
-        name=name,
-        scorer=scorer,
-        max_length=max_length,
-        min_length=min_length,
-        spans_key=spans_key,
-    )
 
 
 def make_span_finder_scorer():

spacy/pipeline/span_ruler.py

@@ -32,61 +32,8 @@ PatternType = Dict[str, Union[str, List[Dict[str, Any]]]]
 DEFAULT_SPANS_KEY = "ruler"
 
 
-def make_entity_ruler(
-    nlp: Language,
-    name: str,
-    phrase_matcher_attr: Optional[Union[int, str]],
-    matcher_fuzzy_compare: Callable,
-    validate: bool,
-    overwrite_ents: bool,
-    scorer: Optional[Callable],
-    ent_id_sep: str,
-):
-    if overwrite_ents:
-        ents_filter = prioritize_new_ents_filter
-    else:
-        ents_filter = prioritize_existing_ents_filter
-    return SpanRuler(
-        nlp,
-        name,
-        spans_key=None,
-        spans_filter=None,
-        annotate_ents=True,
-        ents_filter=ents_filter,
-        phrase_matcher_attr=phrase_matcher_attr,
-        matcher_fuzzy_compare=matcher_fuzzy_compare,
-        validate=validate,
-        overwrite=False,
-        scorer=scorer,
-    )
 
 
-def make_span_ruler(
-    nlp: Language,
-    name: str,
-    spans_key: Optional[str],
-    spans_filter: Optional[Callable[[Iterable[Span], Iterable[Span]], Iterable[Span]]],
-    annotate_ents: bool,
-    ents_filter: Callable[[Iterable[Span], Iterable[Span]], Iterable[Span]],
-    phrase_matcher_attr: Optional[Union[int, str]],
-    matcher_fuzzy_compare: Callable,
-    validate: bool,
-    overwrite: bool,
-    scorer: Optional[Callable],
-):
-    return SpanRuler(
-        nlp,
-        name,
-        spans_key=spans_key,
-        spans_filter=spans_filter,
-        annotate_ents=annotate_ents,
-        ents_filter=ents_filter,
-        phrase_matcher_attr=phrase_matcher_attr,
-        matcher_fuzzy_compare=matcher_fuzzy_compare,
-        validate=validate,
-        overwrite=overwrite,
-        scorer=scorer,
-    )
 
 
 def prioritize_new_ents_filter(

spacy/pipeline/spancat.py

@@ -157,108 +157,8 @@ def build_preset_spans_suggester(spans_key: str) -> Suggester:
     return partial(preset_spans_suggester, spans_key=spans_key)
 
 
-def make_spancat(
-    nlp: Language,
-    name: str,
-    suggester: Suggester,
-    model: Model[Tuple[List[Doc], Ragged], Floats2d],
-    spans_key: str,
-    scorer: Optional[Callable],
-    threshold: float,
-    max_positive: Optional[int],
-) -> "SpanCategorizer":
-    """Create a SpanCategorizer component and configure it for multi-label
-    classification to be able to assign multiple labels for each span.
-    The span categorizer consists of two
-    parts: a suggester function that proposes candidate spans, and a labeller
-    model that predicts one or more labels for each span.
-
-    name (str): The component instance name, used to add entries to the
-        losses during training.
-    suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans.
-        Spans are returned as a ragged array with two integer columns, for the
-        start and end positions.
-    model (Model[Tuple[List[Doc], Ragged], Floats2d]): A model instance that
-        is given a list of documents and (start, end) indices representing
-        candidate span offsets. The model predicts a probability for each category
-        for each span.
-    spans_key (str): Key of the doc.spans dict to save the spans under. During
-        initialization and training, the component will look for spans on the
-        reference document under the same key.
-    scorer (Optional[Callable]): The scoring method. Defaults to
-        Scorer.score_spans for the Doc.spans[spans_key] with overlapping
-        spans allowed.
-    threshold (float): Minimum probability to consider a prediction positive.
-        Spans with a positive prediction will be saved on the Doc. Defaults to
-        0.5.
-    max_positive (Optional[int]): Maximum number of labels to consider positive
-        per span. Defaults to None, indicating no limit.
-    """
-    return SpanCategorizer(
-        nlp.vocab,
-        model=model,
-        suggester=suggester,
-        name=name,
-        spans_key=spans_key,
-        negative_weight=None,
-        allow_overlap=True,
-        max_positive=max_positive,
-        threshold=threshold,
-        scorer=scorer,
-        add_negative_label=False,
-    )
 
 
-def make_spancat_singlelabel(
-    nlp: Language,
-    name: str,
-    suggester: Suggester,
-    model: Model[Tuple[List[Doc], Ragged], Floats2d],
-    spans_key: str,
-    negative_weight: float,
-    allow_overlap: bool,
-    scorer: Optional[Callable],
-) -> "SpanCategorizer":
-    """Create a SpanCategorizer component and configure it for multi-class
-    classification. With this configuration each span can get at most one
-    label. The span categorizer consists of two
-    parts: a suggester function that proposes candidate spans, and a labeller
-    model that predicts one or more labels for each span.
-
-    name (str): The component instance name, used to add entries to the
-        losses during training.
-    suggester (Callable[[Iterable[Doc], Optional[Ops]], Ragged]): A function that suggests spans.
-        Spans are returned as a ragged array with two integer columns, for the
-        start and end positions.
-    model (Model[Tuple[List[Doc], Ragged], Floats2d]): A model instance that
-        is given a list of documents and (start, end) indices representing
-        candidate span offsets. The model predicts a probability for each category
-        for each span.
-    spans_key (str): Key of the doc.spans dict to save the spans under. During
-        initialization and training, the component will look for spans on the
-        reference document under the same key.
-    scorer (Optional[Callable]): The scoring method. Defaults to
-        Scorer.score_spans for the Doc.spans[spans_key] with overlapping
-        spans allowed.
-    negative_weight (float): Multiplier for the loss terms.
-        Can be used to downweight the negative samples if there are too many.
-    allow_overlap (bool): If True the data is assumed to contain overlapping spans.
-        Otherwise it produces non-overlapping spans greedily prioritizing
-        higher assigned label scores.
-    """
-    return SpanCategorizer(
-        nlp.vocab,
-        model=model,
-        suggester=suggester,
-        name=name,
-        spans_key=spans_key,
-        negative_weight=negative_weight,
-        allow_overlap=allow_overlap,
-        max_positive=1,
-        add_negative_label=True,
-        threshold=None,
-        scorer=scorer,
-    )
 
 
 def spancat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:

spacy/pipeline/tagger.pyx

@@ -35,25 +35,6 @@ subword_features = true
 DEFAULT_TAGGER_MODEL = Config().from_str(default_model_config)["model"]
 
 
-def make_tagger(
-    nlp: Language,
-    name: str,
-    model: Model,
-    overwrite: bool,
-    scorer: Optional[Callable],
-    neg_prefix: str,
-    label_smoothing: float,
-):
-    """Construct a part-of-speech tagger component.
-
-    model (Model[List[Doc], List[Floats2d]]): 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).
-    """
-    return Tagger(nlp.vocab, model, name, overwrite=overwrite, scorer=scorer, neg_prefix=neg_prefix, label_smoothing=label_smoothing)
-
-
 def tagger_score(examples, **kwargs):
     return Scorer.score_token_attr(examples, "tag", **kwargs)
 

spacy/pipeline/textcat.py

@@ -74,23 +74,6 @@ subword_features = true
 """
 
 
-def make_textcat(
-    nlp: Language,
-    name: str,
-    model: Model[List[Doc], List[Floats2d]],
-    threshold: float,
-    scorer: Optional[Callable],
-) -> "TextCategorizer":
-    """Create a TextCategorizer component. The text categorizer predicts categories
-    over a whole document. It can learn one or more labels, and the labels are considered
-    to be mutually exclusive (i.e. one true label per doc).
-
-    model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
-        scores for each category.
-    threshold (float): Cutoff to consider a prediction "positive".
-    scorer (Optional[Callable]): The scoring method.
-    """
-    return TextCategorizer(nlp.vocab, model, name, threshold=threshold, scorer=scorer)
 
 
 def textcat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:

spacy/pipeline/textcat_multilabel.py

@@ -72,26 +72,6 @@ subword_features = true
 """
 
 
-def make_multilabel_textcat(
-    nlp: Language,
-    name: str,
-    model: Model[List[Doc], List[Floats2d]],
-    threshold: float,
-    scorer: Optional[Callable],
-) -> "MultiLabel_TextCategorizer":
-    """Create a MultiLabel_TextCategorizer component. The text categorizer predicts categories
-    over a whole document. It can learn one or more labels, and the labels are considered
-    to be non-mutually exclusive, which means that there can be zero or more labels
-    per doc).
-
-    model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
-        scores for each category.
-    threshold (float): Cutoff to consider a prediction "positive".
-    scorer (Optional[Callable]): The scoring method.
-    """
-    return MultiLabel_TextCategorizer(
-        nlp.vocab, model, name, threshold=threshold, scorer=scorer
-    )
 
 
 def textcat_multilabel_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:

spacy/pipeline/tok2vec.py

@@ -24,8 +24,6 @@ subword_features = true
 DEFAULT_TOK2VEC_MODEL = Config().from_str(default_model_config)["model"]
 
 
-def make_tok2vec(nlp: Language, name: str, model: Model) -> "Tok2Vec":
-    return Tok2Vec(nlp.vocab, model, name)
 
 
 class Tok2Vec(TrainablePipe):