SpanFinder into spaCy from experimental

spacy/cli/templates/quickstart_training.jinja

@@ -3,7 +3,7 @@ the docs and the init config command. It encodes various best practices and
 can help generate the best possible configuration, given a user's requirements. #}
 {%- set use_transformer = hardware != "cpu" and transformer_data -%}
 {%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
-{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "spancat", "spancat_singlelabel", "trainable_lemmatizer"] -%}
+{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "span_finder", "spancat", "spancat_singlelabel", "trainable_lemmatizer"] -%}
 [paths]
 train = null
 dev = null
@@ -28,7 +28,7 @@ lang = "{{ lang }}"
 tok2vec/transformer. #}
 {%- set with_accuracy_or_transformer = (use_transformer or with_accuracy) -%}
 {%- set textcat_needs_features = has_textcat and with_accuracy_or_transformer -%}
-{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "spancat" in components or "spancat_singlelabel" in components or "trainable_lemmatizer" in components or "entity_linker" in components or textcat_needs_features) -%}
+{%- if ("tagger" in components or "morphologizer" in components or "parser" in components or "ner" in components or "span_finder" in components or "spancat" in components or "spancat_singlelabel" in components or "trainable_lemmatizer" in components or "entity_linker" in components or textcat_needs_features) -%}
 {%- set full_pipeline = ["transformer" if use_transformer else "tok2vec"] + components -%}
 {%- else -%}
 {%- set full_pipeline = components -%}
@@ -127,6 +127,30 @@ grad_factor = 1.0
 @layers = "reduce_mean.v1"
 {% endif -%}
 
+{% if "span_finder" in components -%}
+[components.span_finder]
+factory = "span_finder"
+max_length = null
+min_length = null
+scorer = {"@scorers":"spacy.span_finder_scorer.v1"}
+spans_key = "sc"
+threshold = 0.5
+
+[components.span_finder.model]
+@architectures = "spacy.SpanFinder.v1"
+
+[components.span_finder.model.scorer]
+@layers = "spacy.LinearLogistic.v1"
+nO = 2
+
+[components.span_finder.model.tok2vec]
+@architectures = "spacy-transformers.TransformerListener.v1"
+grad_factor = 1.0
+
+[components.span_finder.model.tok2vec.pooling]
+@layers = "reduce_mean.v1"
+{% endif -%}
+
 {% if "spancat" in components -%}
 [components.spancat]
 factory = "spancat"
@@ -392,6 +416,27 @@ nO = null
 width = ${components.tok2vec.model.encode.width}
 {% endif %}
 
+{% if "span_finder" in components %}
+[components.span_finder]
+factory = "span_finder"
+max_length = null
+min_length = null
+scorer = {"@scorers":"spacy.span_finder_scorer.v1"}
+spans_key = "sc"
+threshold = 0.5
+
+[components.span_finder.model]
+@architectures = "spacy.SpanFinder.v1"
+
+[components.span_finder.model.scorer]
+@layers = "spacy.LinearLogistic.v1"
+nO = 2
+
+[components.span_finder.model.tok2vec]
+@architectures = "spacy.Tok2VecListener.v1"
+width = ${components.tok2vec.model.encode.width}
+{% endif %}
+
 {% if "spancat" in components %}
 [components.spancat]
 factory = "spancat"

spacy/errors.py

@@ -973,6 +973,10 @@ class Errors(metaclass=ErrorsWithCodes):
     E1052 = ("Unable to copy spans: the character offsets for the span at "
              "index {i} in the span group do not align with the tokenization "
              "in the target doc.")
+    E1053 = ("Both 'min_length' and 'max_length' should be larger than 0, but found"
+             " 'min_length': {min_length}, 'max_length': {max_length}")
+    E1054 = ("The text, including whitespace, must match between reference and "
+             "predicted docs when training {component}.")
 
 
 # Deprecated model shortcuts, only used in errors and warnings

spacy/ml/models/__init__.py

@@ -1,6 +1,7 @@
 from .entity_linker import *  # noqa
 from .multi_task import *  # noqa
 from .parser import *  # noqa
+from .span_finder import *  # noqa
 from .spancat import *  # noqa
 from .tagger import *  # noqa
 from .textcat import *  # noqa

spacy/ml/models/span_finder.py

@@ -0,0 +1,42 @@
+from typing import Callable, List, Tuple
+
+from thinc.api import Model, chain, with_array
+from thinc.types import Floats1d, Floats2d
+
+from spacy.tokens import Doc
+
+from ...util import registry
+
+InT = List[Doc]
+OutT = Floats2d
+
+
+@registry.architectures("spacy.SpanFinder.v1")
+def build_finder_model(
+    tok2vec: Model[InT, List[Floats2d]], scorer: Model[OutT, OutT]
+) -> Model[InT, OutT]:
+
+    logistic_layer: Model[List[Floats2d], List[Floats2d]] = with_array(scorer)
+    model: Model[InT, OutT] = chain(tok2vec, logistic_layer, flattener())
+    model.set_ref("tok2vec", tok2vec)
+    model.set_ref("scorer", scorer)
+    model.set_ref("logistic_layer", logistic_layer)
+
+    return model
+
+
+def flattener() -> Model[List[Floats2d], Floats2d]:
+    """Flattens the input to a 1-dimensional list of scores"""
+
+    def forward(
+        model: Model[Floats1d, Floats1d], X: List[Floats2d], is_train: bool
+    ) -> Tuple[Floats2d, Callable[[Floats2d], List[Floats2d]]]:
+        lens = model.ops.asarray1i([len(doc) for doc in X])
+        Y = model.ops.flatten(X)
+
+        def backprop(dY: Floats2d) -> List[Floats2d]:
+            return model.ops.unflatten(dY, lens)
+
+        return Y, backprop
+
+    return Model("Flattener", forward=forward)

spacy/pipeline/__init__.py

@@ -2,21 +2,22 @@
 from .dep_parser import DependencyParser
 from .edit_tree_lemmatizer import EditTreeLemmatizer
 from .entity_linker import EntityLinker
-from .ner import EntityRecognizer
 from .entityruler import EntityRuler
+from .functions import merge_entities, merge_noun_chunks, merge_subtokens
 from .lemmatizer import Lemmatizer
 from .morphologizer import Morphologizer
+from .ner import EntityRecognizer
 from .pipe import Pipe
-from .trainable_pipe import TrainablePipe
-from .senter import SentenceRecognizer
 from .sentencizer import Sentencizer
+from .senter import SentenceRecognizer
+from .span_finder import SpanFinder
+from .span_ruler import SpanRuler
+from .spancat import SpanCategorizer
 from .tagger import Tagger
 from .textcat import TextCategorizer
-from .spancat import SpanCategorizer
-from .span_ruler import SpanRuler
 from .textcat_multilabel import MultiLabel_TextCategorizer
 from .tok2vec import Tok2Vec
-from .functions import merge_entities, merge_noun_chunks, merge_subtokens
+from .trainable_pipe import TrainablePipe
 
 __all__ = [
     "AttributeRuler",
@@ -32,6 +33,7 @@
     "Sentencizer",
     "SpanCategorizer",
     "SpanRuler",
+    "SpanFinder",
     "Tagger",
     "TextCategorizer",
     "Tok2Vec",