class_docstr: Pipeline that learns span boundaries.
  
  DOCS: https://spacy.io/api/spanfinder
class_docstr_tok: ["Pipeline","that","learns","span","boundaries",".","DOCS",":","https",":","\/\/spacy.io\/api\/spanfinder"]
docstr: Initialize the span finder.
  model (thinc.api.Model): The Thinc Model powering the pipeline
      component.
  name (str): The component instance name, used to add entries to the
      losses during training.
  threshold (float): Minimum probability to consider a prediction
      positive.
  scorer (Optional[Callable]): The scoring method.
  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.
  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.
  
  DOCS: https://spacy.io/api/spanfinder#init
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code: def __init__(
      self,
      nlp: Language,
      model: Model[Iterable[Doc], Floats2d],
      name: str = "span_finder",
      *,
      spans_key: str = DEFAULT_SPANS_KEY,
      threshold: float = 0.5,
      max_length: Optional[int] = None,
      min_length: Optional[int] = None,
      scorer: Optional[Callable] = span_finder_score,
  ) -> None:
      """Initialize the span finder.
      model (thinc.api.Model): The Thinc Model powering the pipeline
          component.
      name (str): The component instance name, used to add entries to the
          losses during training.
      threshold (float): Minimum probability to consider a prediction
          positive.
      scorer (Optional[Callable]): The scoring method.
      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.
      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.
  
      DOCS: https://spacy.io/api/spanfinder#init
      """
      self.vocab = nlp.vocab
      if (max_length is not None and max_length < 1) or (
          min_length is not None and min_length < 1
      ):
          raise ValueError(
              Errors.E1053.format(
                  min_length=min_length, max_length=max_length
              )
          )
      self.model = model
      self.name = name
      self.scorer = scorer
      self.cfg: Dict[str, Any] = {
          "min_length": min_length,
          "max_length": max_length,
          "threshold": threshold,
          "spans_key": spans_key,
      }
  
imports: from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
  from thinc.api import Config, Model, Optimizer, set_dropout_rate
  from thinc.types import Floats2d
  from ..errors import Errors
  from ..language import Language
  from ..scorer import Scorer
  from ..tokens import Doc, Span
  from ..training import Example
  from ..util import registry
  from .spancat import DEFAULT_SPANS_KEY
  from .trainable_pipe import TrainablePipe
label_desc: Use image node_id 1 to create a new SpanFinder object from inherited base classes: TrainablePipe with example: obj = SpanFinder(nlp, model, name)