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README.md

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----
-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]]
-```