avoid changing output dimentionality for a single input (#148)

fastembed/sparse/splade_pp.py

@@ -27,11 +27,10 @@ def _post_process_onnx_output(cls, output: Tuple[np.ndarray, np.ndarray]) -> Ite
 
         weighted_log = relu_log * np.expand_dims(attention_mask, axis=-1)
 
-        max_val = np.max(weighted_log, axis=1)
+        scores = np.max(weighted_log, axis=1)
 
         # Score matrix of shape (batch_size, vocab_size)
         # Most of the values are 0, only a few are non-zero
-        scores = np.squeeze(max_val)
         for row_scores in scores:
             indices = row_scores.nonzero()[0]
             scores = row_scores[indices]

tests/test_sparse_embeddings.py

@@ -40,3 +40,18 @@ def test_batch_embedding():
 
         for i, value in enumerate(result.values):
             assert pytest.approx(value, abs=0.001) == expected_result["values"][i]
+
+
+def test_single_embedding():
+    docs_to_embed = docs
+
+    for model_name, expected_result in CANONICAL_COLUMN_VALUES.items():
+        print("evaluating", model_name)
+        model = SparseTextEmbedding(model_name=model_name)
+        result = next(iter(model.embed(docs_to_embed, batch_size=6)))
+        print(result.indices)
+
+        assert result.indices.tolist() == expected_result["indices"]
+
+        for i, value in enumerate(result.values):
+            assert pytest.approx(value, abs=0.001) == expected_result["values"][i]