Hf sources (#429)

* HF sources for all models

Author: I8dNLo

fastembed/text/onnx_embedding.py

@@ -16,6 +16,7 @@
         "license": "mit",
         "size_in_GB": 0.42,
         "sources": {
+            "hf": "Qdrant/fast-bge-base-en",
             "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz",
         },
         "model_file": "model_optimized.onnx",
@@ -50,6 +51,7 @@
         "license": "mit",
         "size_in_GB": 0.13,
         "sources": {
+            "hf": "Qdrant/bge-small-en",
             "url": "https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz",
         },
         "model_file": "model_optimized.onnx",
@@ -72,6 +74,7 @@
         "license": "mit",
         "size_in_GB": 0.09,
         "sources": {
+            "hf": "Qdrant/bge-small-zh-v1.5",
             "url": "https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz",
         },
         "model_file": "model_optimized.onnx",

fastembed/text/onnx_text_model.py

@@ -79,7 +79,6 @@ def onnx_embed(
             onnx_input["token_type_ids"] = np.array(
                 [np.zeros(len(e), dtype=np.int64) for e in input_ids], dtype=np.int64
             )
-
         onnx_input = self._preprocess_onnx_input(onnx_input, **kwargs)
 
         model_output = self.model.run(self.ONNX_OUTPUT_NAMES, onnx_input)