Run black

spacy/ml/models/coref.py

@@ -24,7 +24,7 @@ def build_coref(
     # the token count.
     mention_limit_ratio: float = 0.4,
     max_span_width: int = 20,
-    antecedent_limit: int = 50
+    antecedent_limit: int = 50,
 ):
     dim = tok2vec.get_dim("nO") * 3
 
@@ -90,15 +90,14 @@ def build_width_scorer(max_span_width, hidden_size, feature_embed_size=20):
         >> Linear(nI=hidden_size, nO=1)
     )
     span_width_prior.initialize()
-    model = Model(
-            "WidthScorer",
-            forward=width_score_forward,
-            layers=[span_width_prior])
+    model = Model("WidthScorer", forward=width_score_forward, layers=[span_width_prior])
     model.set_ref("width_prior", span_width_prior)
     return model
 
 
-def width_score_forward(model, embeds: SpanEmbeddings, is_train) -> Tuple[Floats1d, Callable]:
+def width_score_forward(
+    model, embeds: SpanEmbeddings, is_train
+) -> Tuple[Floats1d, Callable]:
     # calculate widths, subtracting 1 so it's 0-index
     w_ffnn = model.get_ref("width_prior")
     idxs = embeds.indices
@@ -115,6 +114,7 @@ def width_score_forward(model, embeds: SpanEmbeddings, is_train) -> Tuple[Floats
 
     return wscores, width_score_backward
 
+
 # model converting a Doc/Mention to span embeddings
 # get_mentions: Callable[Doc, Pairs[int]]
 def build_span_embedder(
@@ -123,8 +123,9 @@ def build_span_embedder(
 ) -> Model[Tuple[List[Floats2d], List[Doc]], SpanEmbeddings]:
 
     with Model.define_operators({">>": chain, "|": concatenate}):
-        span_reduce = (extract_spans() >> 
-                (reduce_first() | reduce_last() | reduce_mean()))
+        span_reduce = extract_spans() >> (
+            reduce_first() | reduce_last() | reduce_mean()
+        )
     model = Model(
         "SpanEmbedding",
         forward=span_embeddings_forward,
@@ -290,6 +291,7 @@ def build_take_vecs() -> Model[SpanEmbeddings, Floats2d]:
 def take_vecs_forward(model, inputs: SpanEmbeddings, is_train) -> Floats2d:
     idxs = inputs.indices
     lens = inputs.vectors.lengths
+
     def backprop(dY: Floats2d) -> SpanEmbeddings:
         vecs = Ragged(dY, lens)
         return SpanEmbeddings(idxs, vecs)
@@ -350,10 +352,10 @@ def ant_scorer_forward(
         # This will take the log of 0, which causes a warning, but we're doing
         # it on purpose so we can just ignore the warning.
         with warnings.catch_warnings():
-            warnings.filterwarnings('ignore', category=RuntimeWarning)
+            warnings.filterwarnings("ignore", category=RuntimeWarning)
             mask = xp.log(
                 (xp.expand_dims(ant_range, 1) - xp.expand_dims(ant_range, 0)) >= 1
-            ).astype('f')
+            ).astype("f")
 
         scores = pw_prod + pw_sum + mask
 

spacy/pipeline/coref.py

@@ -79,7 +79,9 @@ def make_coref(
 ) -> "CoreferenceResolver":
     """Create a CoreferenceResolver component."""
 
-    return CoreferenceResolver(nlp.vocab, model, name, span_cluster_prefix=span_cluster_prefix)
+    return CoreferenceResolver(
+        nlp.vocab, model, name, span_cluster_prefix=span_cluster_prefix
+    )
 
 
 class CoreferenceResolver(TrainablePipe):
@@ -308,7 +310,7 @@ class CoreferenceResolver(TrainablePipe):
             top_gscores = ops.asarray2f(top_gscores)
 
             with warnings.catch_warnings():
-                warnings.filterwarnings('ignore', category=RuntimeWarning)
+                warnings.filterwarnings("ignore", category=RuntimeWarning)
                 log_marg = ops.softmax(cscores + ops.xp.log(top_gscores), axis=1)
             log_norm = ops.softmax(cscores, axis=1)
             grad = log_norm - log_marg