Create README.md

README.md

@@ -0,0 +1,47 @@
+---
+base_model:
+- Alibaba-NLP/gte-multilingual-base
+pipeline_tag: text-generation
+license: apache-2.0
+---
+This is the ONNX version of the [gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) model.
+
+This example is adapted from the original model repository for the ONNX version.
+```python
+# Requires transformers>=4.36.0
+import onnxruntime as ort
+import numpy as np
+from transformers import AutoTokenizer
+input_texts = [
+    "what is the capital of China?",
+    "how to implement quick sort in python?",
+    "北京",
+    "快排算法介绍"
+]
+# Load the tokenizer (using the original model for tokenizer)
+tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-multilingual-base')
+# Load the ONNX model
+session = ort.InferenceSession("model.onnx")
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='np')
+# Run inference
+outputs = session.run(None, {
+    "input_ids": batch_dict["input_ids"],
+    "attention_mask": batch_dict["attention_mask"]
+})
+# Get embeddings from the second output (last hidden states)
+# Extract the [CLS] token embedding (first token) for each sequence
+last_hidden_states = outputs[1]  # Shape: (batch_size, seq_len, hidden_size)
+dimension = 768  # The output dimension of the output embedding, should be in [128, 768]
+embeddings = last_hidden_states[:, 0, :dimension]  # Shape: (batch_size, dimension)
+# Debug: Check embeddings
+print(f"Embeddings shape: {embeddings.shape}")
+print(f"First few values of first embedding: {embeddings[0][:5]}")
+print(f"First few values of second embedding: {embeddings[1][:5]}")
+# Normalize embeddings
+embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)
+# Calculate similarity scores
+scores = (embeddings[:1] @ embeddings[1:].T) * 100
+print(scores.tolist())
+# [[0.3016996383666992, 0.7503870129585266, 0.3203084468841553]]
+```