fix: fix thenlper, update warnings (#486)

Author: joein

fastembed/text/onnx_embedding.py

@@ -93,18 +93,6 @@
         ),
         model_file="model_optimized.onnx",
     ),
-    DenseModelDescription(
-        model="thenlper/gte-large",
-        dim=1024,
-        description=(
-            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
-            "Prefixes for queries/documents: not necessary, 2023 year."
-        ),
-        license="mit",
-        size_in_GB=1.20,
-        sources=ModelSource(hf="qdrant/gte-large-onnx"),
-        model_file="model.onnx",
-    ),
     DenseModelDescription(
         model="mixedbread-ai/mxbai-embed-large-v1",
         dim=1024,
@@ -314,6 +302,7 @@ def _preprocess_onnx_input(
 
     def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[NumpyArray]:
         embeddings = output.model_output
+
         if embeddings.ndim == 3:  # (batch_size, seq_len, embedding_dim)
             processed_embeddings = embeddings[:, 0]
         elif embeddings.ndim == 2:  # (batch_size, embedding_dim)

fastembed/text/pooled_normalized_embedding.py

@@ -109,6 +109,18 @@
         sources=ModelSource(hf="thenlper/gte-base"),
         model_file="onnx/model.onnx",
     ),
+    DenseModelDescription(
+        model="thenlper/gte-large",
+        dim=1024,
+        description=(
+            "Text embeddings, Unimodal (text), English, 512 input tokens truncation, "
+            "Prefixes for queries/documents: not necessary, 2023 year."
+        ),
+        license="mit",
+        size_in_GB=1.20,
+        sources=ModelSource(hf="qdrant/gte-large-onnx"),
+        model_file="model.onnx",
+    ),
 ]
 
 

fastembed/text/text_embedding.py

@@ -90,29 +90,24 @@ def __init__(
         super().__init__(model_name, cache_dir, threads, **kwargs)
         if model_name == "nomic-ai/nomic-embed-text-v1.5-Q":
             warnings.warn(
-                "The model 'nomic-ai/nomic-embed-text-v1.5-Q' has been updated on HuggingFace. "
-                "Please review the latest documentation and release notes to ensure compatibility with your workflow. ",
-                UserWarning,
-                stacklevel=2,
-            )
-        if model_name == "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2":
-            warnings.warn(
-                "The model 'sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2' has been updated to "
-                "include a mean pooling layer. Please ensure your usage aligns with the new functionality. "
-                "Support for the previous version without mean pooling will be removed as of version 0.5.2.",
+                "The model 'nomic-ai/nomic-embed-text-v1.5-Q' has been updated on HuggingFace. Please review "
+                "the latest documentation on HF and release notes to ensure compatibility with your workflow. ",
                 UserWarning,
                 stacklevel=2,
             )
         if model_name in {
-            "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
+            "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
+            "thenlper/gte-large",
             "intfloat/multilingual-e5-large",
+            "sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
         }:
             warnings.warn(
-                f"{model_name} has been updated as of fastembed 0.5.2, outputs are now average pooled.",
+                f"The model {model_name} now uses mean pooling instead of CLS embedding. "
+                f"In order to preserve the previous behaviour, consider either pinning fastembed version to 0.5.1 or "
+                "using `add_custom_model` functionality.",
                 UserWarning,
                 stacklevel=2,
             )
-
         for EMBEDDING_MODEL_TYPE in self.EMBEDDINGS_REGISTRY:
             supported_models = EMBEDDING_MODEL_TYPE._list_supported_models()
             if any(model_name.lower() == model.model.lower() for model in supported_models):

tests/test_text_onnx_embeddings.py

@@ -52,7 +52,7 @@
         [0.0802303, 0.3700881, -4.3053818, 0.4431803, -0.271572]
     ),
     "thenlper/gte-large": np.array(
-        [-0.01920587, 0.00113156, -0.00708992, -0.00632304, -0.04025577]
+        [-0.00986551, -0.00018734, 0.00605892, -0.03289612, -0.0387564],
     ),
     "mixedbread-ai/mxbai-embed-large-v1": np.array(
         [0.02295546, 0.03196154, 0.016512, -0.04031524, -0.0219634]