diff --git a/docarray/document/mixins/featurehash.py b/docarray/document/mixins/featurehash.py
index 04f692e2504..56e474e9d96 100644
--- a/docarray/document/mixins/featurehash.py
+++ b/docarray/document/mixins/featurehash.py
@@ -67,19 +67,16 @@ def _any_hash(v):
     try:
         return int(v)  # parse int parameter
     except ValueError:
-        try:
-            return float(v)  # parse float parameter
-        except ValueError:
-            if not v:
-                # ignore it when the parameter is empty
+        if not v:
+            # ignore it when the parameter is empty
+            return 0
+        if isinstance(v, str):
+            v = v.strip()
+            if v.lower() in {'true', 'yes'}:  # parse boolean parameter
+                return 1
+            if v.lower() in {'false', 'no'}:
                 return 0
-            if isinstance(v, str):
-                v = v.strip()
-                if v.lower() in {'true', 'yes'}:  # parse boolean parameter
-                    return 1
-                if v.lower() in {'false', 'no'}:
-                    return 0
-            if isinstance(v, (tuple, dict, list)):
-                v = json.dumps(v, sort_keys=True)
+        if isinstance(v, (tuple, dict, list)):
+            v = json.dumps(v, sort_keys=True)
 
     return int(hashlib.md5(str(v).encode('utf-8')).hexdigest(), base=16)
diff --git a/tests/unit/document/test_feature_hashing.py b/tests/unit/document/test_feature_hashing.py
index edd81932709..8b3d9aa8c8f 100644
--- a/tests/unit/document/test_feature_hashing.py
+++ b/tests/unit/document/test_feature_hashing.py
@@ -8,10 +8,17 @@
 @pytest.mark.parametrize('sparse', [True, False])
 @pytest.mark.parametrize('metric', ['jaccard', 'cosine'])
 def test_feature_hashing(n_dim, sparse, metric):
-    da = DocumentArray.empty(3)
-    da.texts = ['hello world', 'world, bye', 'hello bye']
+    da = DocumentArray.empty(6)
+    da.texts = [
+        'hello world',
+        'world, bye',
+        'hello bye',
+        'infinity test',
+        'nan test',
+        '2.3 test',
+    ]
     da.apply(lambda d: d.embed_feature_hashing(n_dim=n_dim, sparse=sparse))
-    assert da.embeddings.shape == (3, n_dim)
+    assert da.embeddings.shape == (6, n_dim)
     da.embeddings = to_numpy_array(da.embeddings)
     da.match(da, metric=metric, use_scipy=True)
     result = da['@m', ('id', f'scores__{metric}__value')]