You can deploy this model and start earning money today!

nsense/
all-MiniLM-L6-v2-onnx

Name: all-MiniLM-L6-v2-onnx
Availability: InStock

sentence-transformers/all-MiniLM-L6-v2

quantized

mit

text-embeddings-inference

endpoints_compatible

Views

No views yet

all-MiniLM-L6-v2-onnx

This is the ONNX-ported version of the sentence-transformers/all-MiniLM-L6-v2 for generating text embeddings.

Model details

txt

Embedding dimension: 384
Max sequence length: 256

It has been converted with following labels:

python

input_names=["input_ids", "attention_mask"],
output_names=["token_embeddings", "sentence_embedding"],
dynamic_axes={
    "input_ids": {0: "batch_size", 1: "sequence_length"},
    "attention_mask": {0: "batch_size", 1: "sequence_length"},
    "token_embeddings": {0: "batch_size", 1: "sequence_length"},
    "sentence_embedding": {0: "batch_size"}

Usage:

cpp

#include <iostream>
#include <vector>
#include <onnxruntime_cxx_api.h>

int main() {
    Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "embedding");
    Ort::SessionOptions session_options;
    session_options.SetIntraOpNumThreads(1);

    // Load the ONNX model
    const char* model_path = "your_model.onnx";
    Ort::Session session(env, model_path, session_options);

    Ort::AllocatorWithDefaultOptions allocator;

    // Input and output names
    const char* input_names[] = {"input_ids", "attention_mask"};
    const char* output_names[] = {"token_embeddings", "sentence_embedding"};

    // Mock input data (e.g., batch_size=1, sequence_length=5)
    std::vector<int64_t> input_ids = {101, 2009, 2003, 1037, 2742}; // Example token IDs, Use proper tokenization library to convert string to input_ids (e.g. tokenizers-cpp)
    std::vector<int64_t> attention_mask = {1, 1, 1, 1, 1};

    std::vector<int64_t> input_shape = {1, 5}; // batch_size=1, seq_len=5

    // Create input tensors
    Ort::Value input_ids_tensor = Ort::Value::CreateTensor<int64_t>(
        allocator, input_ids.data(), input_ids.size(), input_shape.data(), input_shape.size());

    Ort::Value attention_mask_tensor = Ort::Value::CreateTensor<int64_t>(
        allocator, attention_mask.data(), attention_mask.size(), input_shape.data(), input_shape.size());

    std::vector<Ort::Value> input_tensors;
    input_tensors.push_back(std::move(input_ids_tensor));
    input_tensors.push_back(std::move(attention_mask_tensor));

    // Run inference
    auto output_tensors = session.Run(Ort::RunOptions{nullptr},
                                      input_names, input_tensors.data(), 2,
                                      output_names, 2);

    // Extract sentence embedding
    float* sentence_embedding = output_tensors[1].GetTensorMutableData<float>();
    size_t embedding_size = output_tensors[1].GetTensorTypeAndShapeInfo().GetElementCount();

    std::cout << "Sentence Embedding:\n";
    for (size_t i = 0; i < embedding_size; ++i) {
        std::cout << sentence_embedding[i] << " ";
    }
    std::cout << std::endl;

    return 0;
}

nsense/all-MiniLM-L6-v2-onnx

all-MiniLM-L6-v2-onnx

This is the ONNX-ported version of the sentence-transformers/all-MiniLM-L6-v2 for generating text embeddings.

Usage:

nsense/
all-MiniLM-L6-v2-onnx