diff --git a/spacy/tests/pipeline/test_spancat.py b/spacy/tests/pipeline/test_spancat.py
index 0364abf73..a0ef7e3f0 100644
--- a/spacy/tests/pipeline/test_spancat.py
+++ b/spacy/tests/pipeline/test_spancat.py
@@ -1,9 +1,11 @@
 import pytest
-from numpy.testing import assert_equal
+from numpy.testing import assert_equal, assert_array_equal
+from thinc.api import get_current_ops
 from spacy.language import Language
 from spacy.training import Example
 from spacy.util import fix_random_seed, registry
 
+OPS = get_current_ops()
 
 SPAN_KEY = "labeled_spans"
 
@@ -116,12 +118,12 @@ def test_ngram_suggester(en_tokenizer):
             for span in spans:
                 assert 0 <= span[0] < len(doc)
                 assert 0 < span[1] <= len(doc)
-                spans_set.add((span[0], span[1]))
+                spans_set.add((int(span[0]), int(span[1])))
             # spans are unique
             assert spans.shape[0] == len(spans_set)
             offset += ngrams.lengths[i]
         # the number of spans is correct
-        assert_equal(ngrams.lengths, [max(0, len(doc) - (size - 1)) for doc in docs])
+        assert_array_equal(OPS.to_numpy(ngrams.lengths), [max(0, len(doc) - (size - 1)) for doc in docs])
 
     # test 1-3-gram suggestions
     ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1, 2, 3])
@@ -129,9 +131,9 @@ def test_ngram_suggester(en_tokenizer):
         en_tokenizer(text) for text in ["a", "a b", "a b c", "a b c d", "a b c d e"]
     ]
     ngrams = ngram_suggester(docs)
-    assert_equal(ngrams.lengths, [1, 3, 6, 9, 12])
-    assert_equal(
-        ngrams.data,
+    assert_array_equal(OPS.to_numpy(ngrams.lengths), [1, 3, 6, 9, 12])
+    assert_array_equal(
+        OPS.to_numpy(ngrams.data),
         [
             # doc 0
             [0, 1],
@@ -176,13 +178,13 @@ def test_ngram_suggester(en_tokenizer):
     ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1])
     docs = [en_tokenizer(text) for text in ["", "a", ""]]
     ngrams = ngram_suggester(docs)
-    assert_equal(ngrams.lengths, [len(doc) for doc in docs])
+    assert_array_equal(OPS.to_numpy(ngrams.lengths), [len(doc) for doc in docs])
 
     # test all empty docs
     ngram_suggester = registry.misc.get("spacy.ngram_suggester.v1")(sizes=[1])
     docs = [en_tokenizer(text) for text in ["", "", ""]]
     ngrams = ngram_suggester(docs)
-    assert_equal(ngrams.lengths, [len(doc) for doc in docs])
+    assert_array_equal(OPS.to_numpy(ngrams.lengths), [len(doc) for doc in docs])
 
 
 def test_ngram_sizes(en_tokenizer):
@@ -195,12 +197,12 @@ def test_ngram_sizes(en_tokenizer):
     ]
     ngrams_1 = size_suggester(docs)
     ngrams_2 = range_suggester(docs)
-    assert_equal(ngrams_1.lengths, [1, 3, 6, 9, 12])
-    assert_equal(ngrams_1.lengths, ngrams_2.lengths)
-    assert_equal(ngrams_1.data, ngrams_2.data)
+    assert_array_equal(OPS.to_numpy(ngrams_1.lengths), [1, 3, 6, 9, 12])
+    assert_array_equal(OPS.to_numpy(ngrams_1.lengths), OPS.to_numpy(ngrams_2.lengths))
+    assert_array_equal(OPS.to_numpy(ngrams_1.data), OPS.to_numpy(ngrams_2.data))
 
     # one more variation
     suggester_factory = registry.misc.get("spacy.ngram_range_suggester.v1")
     range_suggester = suggester_factory(min_size=2, max_size=4)
     ngrams_3 = range_suggester(docs)
-    assert_equal(ngrams_3.lengths, [0, 1, 3, 6, 9])
+    assert_array_equal(OPS.to_numpy(ngrams_3.lengths), [0, 1, 3, 6, 9])