diff --git a/spacy/pipeline/spancat_exclusive.py b/spacy/pipeline/spancat_exclusive.py
new file mode 100644
index 000000000..b06b890f0
--- /dev/null
+++ b/spacy/pipeline/spancat_exclusive.py
@@ -0,0 +1,73 @@
+from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast
+from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
+from thinc.api import Optimizer, Softmax_v2
+from thinc.types import Ragged, Ints2d, Floats2d, Ints1d
+
+import numpy
+
+from ..compat import Protocol, runtime_checkable
+from ..scorer import Scorer
+from ..language import Language
+from .trainable_pipe import TrainablePipe
+from ..tokens import Doc, SpanGroup, Span
+from ..vocab import Vocab
+from ..training import Example, validate_examples
+from ..errors import Errors
+from ..util import registry
+
+
+@registry.layers("spacy.Softmax.v1")
+def build_linear_logistic(nO=None, nI=None) -> Model[Floats2d, Floats2d]:
+    """An output layer for multi-label classification. It uses a linear layer
+    followed by a logistic activation.
+    """
+    return Softmax_v2(nI=nI, nO=nO)
+
+
+spancat_exclusive_default_config = """
+[model]
+@architectures = "spacy.SpanCategorizerExclusive.v1"
+scorer = {"@layers": "spacy.Softmax.v1"}
+
+[model.reducer]
+@layers = spacy.mean_max_reducer.v1
+hidden_size = 128
+
+[model.tok2vec]
+@architectures = "spacy.Tok2Vec.v1"
+[model.tok2vec.embed]
+@architectures = "spacy.MultiHashEmbed.v1"
+width = 96
+rows = [5000, 2000, 1000, 1000]
+attrs = ["ORTH", "PREFIX", "SUFFIX", "SHAPE"]
+include_static_vectors = false
+
+[model.tok2vec.encode]
+@architectures = "spacy.MaxoutWindowEncoder.v1"
+width = ${model.tok2vec.embed.width}
+window_size = 1
+maxout_pieces = 3
+depth = 4
+"""
+
+DEFAULT_SPANCAT_MODEL = Config().from_str(spancat_exclusive_default_config)["model"]
+
+
+@runtime_checkable
+class Suggester(Protocol):
+    def __call__(self, docs: Iterable[Doc], *, ops: Optional[Ops] = None) -> Ragged:
+        ...
+
+
+@Language.factory(
+    "spancat_exclusive",
+    assigns=["doc.spans"],
+    default_config={
+        "spans_key": "sc",
+        "model": DEFAULT_SPANCAT_MODEL,
+        "suggester": {"@misc": "spacy.ngram_suggester.v1", "sizes": [1, 2, 3]},
+        "scorer": {"@scorers": "spacy.spancat_scorer.v1"},
+    },
+)
+def make_spancat():
+    pass