removing unused imports

spacy/ml/models/coref.py

@@ -6,15 +6,7 @@ from typing import List, Callable, Tuple, Any
 from ...tokens import Doc
 from ...util import registry
 
-from .coref_util import (
-    get_predicted_clusters,
-    get_candidate_mentions,
-    select_non_crossing_spans,
-    make_clean_doc,
-    create_gold_scores,
-    logsumexp,
-    topk,
-)
+from .coref_util import get_candidate_mentions, select_non_crossing_spans, topk
 
 
 @registry.architectures("spacy.Coref.v1")
@@ -172,8 +164,8 @@ def span_embeddings_forward(
 
     # first and last token embeds
     # XXX probably would be faster to get these at once
-    #starts = [tokvecs[ii] for ii in mentions[:, 0]]
-    #ends = [tokvecs[jj] for jj in mentions[:, 1]]
+    # starts = [tokvecs[ii] for ii in mentions[:, 0]]
+    # ends = [tokvecs[jj] for jj in mentions[:, 1]]
     starts, ends = zip(*[(tokvecs[ii], tokvecs[jj]) for ii, jj in mentions])
 
     starts = ops.asarray2f(starts)

spacy/ml/models/coref_util.py

@@ -44,6 +44,7 @@ def topk(xp, arr, k, axis=None):
 def logsumexp(xp, arr, axis=None):
     """Emulate torch.logsumexp by returning the log of summed exponentials
     along each row in the given dimension.
+    TODO: currently not used?
 
     Reduces a 2d array to 1d."""
     # from slide 5 here:
@@ -217,6 +218,7 @@ def get_clusters_from_doc(doc) -> List[List[Tuple[int, int]]]:
 def make_clean_doc(nlp, doc):
     """Return a doc with raw data but not span annotations."""
     # Surely there is a better way to do this?
+    # TODO: currently not used?
 
     sents = [tok.is_sent_start for tok in doc]
     words = [tok.text for tok in doc]

spacy/pipeline/coref.py

@@ -17,7 +17,6 @@ from ..ml.models.coref_util import (
     create_gold_scores,
     MentionClusters,
     get_clusters_from_doc,
-    logsumexp,
     get_predicted_clusters,
     DEFAULT_CLUSTER_PREFIX,
     doc2clusters,