Create examples/app-console.py

examples/app-console.py

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+#app-console.py
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer, AutoModel
+from quantmodel import QuantizedSentenceEncoder
+
+
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+
+
+def encode_original(texts, model_name="deepvk/USER-BGE-M3", normalize=True, device="cpu"):
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModel.from_pretrained(model_name).to(device)
+
+    encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors="pt").to(device)
+    with torch.no_grad():
+        model_output = model(**encoded_input)
+
+    embeddings = mean_pooling(model_output, encoded_input["attention_mask"])
+
+    if normalize:
+        embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+
+    return embeddings.cpu().numpy()
+
+
+if __name__ == "__main__":
+    texts = ["Привет мир!", "Hello world!"]
+
+    orig_embeddings = encode_original(texts)
+    quant_model = QuantizedSentenceEncoder()
+    quant_embeddings = quant_model.encode(texts)
+
+    print("Original shape:", orig_embeddings.shape)
+    print("Quantized shape:", quant_embeddings.shape)
+
+    cos_sim = np.dot(orig_embeddings[0], quant_embeddings[0])
+    print(f"Cosine similarity between original & quantized: {cos_sim:.4f}")