# MedEmbed-large-v0.1 ONNX Model

This repository contains an ONNX version of the MedEmbed-large-v0.1 model, which was originally a SentenceTransformer model.

## Model Description

The original MedEmbed-large-v0.1 model is a sentence embedding model specialized for medical text. This ONNX version maintains the same functionality but is optimized for deployment in production environments.

## ONNX Conversion

The model was converted to ONNX format using PyTorch's `torch.onnx.export` functionality with ONNX opset version 14.

## Model Inputs and Outputs

- **Inputs**:
  - `input_ids`: Tensor of shape `[batch_size, sequence_length]`
  - `attention_mask`: Tensor of shape `[batch_size, sequence_length]`

- **Output**:
  - `sentence_embedding`: Tensor of shape `[batch_size, embedding_dimension]`

## Usage with Hugging Face

```python
import onnxruntime as ort
from transformers import AutoTokenizer

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("YOUR_MODEL_PATH")

# Load ONNX model
onnx_path = "YOUR_MODEL_PATH/MedEmbed-large-v0.1.onnx"
session = ort.InferenceSession(onnx_path)

# Tokenize input text
text = "Your medical text here"
inputs = tokenizer(text, return_tensors="np", padding=True, truncation=True)

# Run inference with ONNX model
onnx_inputs = {
    "input_ids": inputs["input_ids"],
    "attention_mask": inputs["attention_mask"]
}
embeddings = session.run(None, onnx_inputs)[0]
```

## Usage with OpenSearch

This model can be used with OpenSearch's neural search capabilities. Please refer to OpenSearch documentation for details on how to load and use ONNX models for text embedding.