Update README.md

README.md

@@ -14,7 +14,7 @@ Note: dense and ColBERT embeddings are normalized like the default behavior in t
 
 Install the necessary modules:
 ```bash
-pip install pip install huggingface-hub onnxruntime transformers
+pip install huggingface-hub onnxruntime transformers
 ```
 
 You can then use the model to compute embeddings, as follows:
@@ -24,8 +24,6 @@ from huggingface_hub import hf_hub_download
 import onnxruntime as ort
 from transformers import AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("ddmitov/bge_m3_dense_colbert_onnx")
-
 hf_hub_download(
     repo_id="ddmitov/bge_m3_dense_colbert_onnx",
     filename="model.onnx",
@@ -40,6 +38,8 @@ hf_hub_download(
     repo_type="model"
 )
 
+tokenizer = AutoTokenizer.from_pretrained("ddmitov/bge_m3_dense_colbert_onnx")
+
 ort_session = ort.InferenceSession("/tmp/model.onnx")
 
 inputs = tokenizer(
@@ -48,7 +48,9 @@ inputs = tokenizer(
   return_tensors="np"
 )
 
-inputs_onnx = {key: ort.OrtValue.ortvalue_from_numpy(value) for key, value in inputs.items()}
+inputs_onnx = {
+  key: ort.OrtValue.ortvalue_from_numpy(value) for key, value in inputs.items()
+}
 
 outputs = ort_session.run(None, inputs_onnx)