nit

Author: hh-space-invader

fastembed/text/pooled_embedding.py

@@ -19,26 +19,26 @@
         "model_file": "onnx/model.onnx",
     },
     {
-        "model": "nomic-ai/nomic-embed-text-v1",
+        "model": "nomic-ai/nomic-embed-text-v1.5-Q",
         "dim": 768,
         "description": "Text embeddings, Multimodal (text, image), English, 8192 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
         "license": "apache-2.0",
-        "size_in_GB": 0.52,
+        "size_in_GB": 0.13,
         "sources": {
-            "hf": "nomic-ai/nomic-embed-text-v1",
+            "hf": "nomic-ai/nomic-embed-text-v1.5",
         },
-        "model_file": "onnx/model.onnx",
+        "model_file": "onnx/model_quantized.onnx",
     },
     {
-        "model": "nomic-ai/nomic-embed-text-v1.5-Q",
+        "model": "nomic-ai/nomic-embed-text-v1",
         "dim": 768,
         "description": "Text embeddings, Multimodal (text, image), English, 8192 input tokens truncation, Prefixes for queries/documents: necessary, 2024 year.",
         "license": "apache-2.0",
-        "size_in_GB": 0.13,
+        "size_in_GB": 0.52,
         "sources": {
-            "hf": "nomic-ai/nomic-embed-text-v1.5",
+            "hf": "nomic-ai/nomic-embed-text-v1",
         },
-        "model_file": "onnx/model_quantized.onnx",
+        "model_file": "onnx/model.onnx",
     },
 ]
 

tests/test_text_onnx_embeddings.py

@@ -45,10 +45,10 @@
     "jinaai/jina-embeddings-v2-base-es": np.array([-0.0108, -0.0092, -0.0373, 0.0171, -0.0301]),
     "nomic-ai/nomic-embed-text-v1": np.array([0.3708, 0.2031, -0.3406, -0.2114, -0.3230]),
     "nomic-ai/nomic-embed-text-v1.5": np.array(
-        [0.0802303, 0.3700881, -4.3053818, 0.4431803, -0.271572]
+        [-0.15407836, -0.03053198, -3.9138033, 0.1910364, 0.13224715]
     ),
     "nomic-ai/nomic-embed-text-v1.5-Q": np.array(
-        [0.0035279, 0.0162738, -0.1893193, 0.0194878, -0.0119418]
+        [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]