Welcome to the world of machine learning! In this guide, we will explore how to convert and use the AdapterHub RoBERTa model in ONNX format for effective query answering. With ONNX, you’re enabling robust interoperability across different systems and enhancing the execution of your models.

What is ONNX?

ONNX (Open Neural Network Exchange) is an open format designed for sharing machine learning models across various frameworks and platforms. Using ONNX allows for better performance optimization and easier model sharing between different programming environments.

Steps to Convert and Use the AdapterHub RoBERTa Model

1. Download the ONNX Model

Begin by downloading the ROberta model from AdapterHub. You can do this using the following Python code:

onnx_path = hf_hub_download(repo_id="UKP-SQuARE/roberta-base-pf-drop-onnx", filename="model.onnx")  # or model_quant.onnx for quantization

2. Set Up the ONNX Runtime

To run the model efficiently, set up the ONNX Runtime with the following lines:

onnx_model = InferenceSession(onnx_path, providers=[CPUExecutionProvider])

3. Prepare Your Inputs

You need to prepare your inputs before asking questions to the model. This involves tokenizing your question. You can use the code below:

tokenizer = AutoTokenizer.from_pretrained("UKP-SQuARE/roberta-base-pf-drop-onnx")

Next, feed your queries to the model in the following manner:

question = "What are advantages of ONNX?"
inputs = tokenizer(question, context, padding=True, truncation=True, return_tensors="np")
inputs_int64 = {key: np.array(inputs[key], dtype=np.int64) for key in inputs}

4. Get Outputs

Finally, run the model to get outputs:

outputs = onnx_model.run(input_feed=dict(inputs_int64), output_names=None)

Understanding the Workflow with an Analogy

Imagine building a bridge (the ONNX model) that connects two islands (different machine learning frameworks). First, you construct the foundation by downloading the necessary materials (the model). You then build the infrastructure (set up the ONNX runtime), ensuring people can cross comfortably (process the inputs). Finally, the bridge is operational (getting outputs), allowing seamless travel between the islands. This entire flow enhances the efficiency and reliability of communication between varying systems.

Troubleshooting Common Issues

• Model Not Found: Ensure that the repository ID and model file names are correctly referenced in your download command.
• Import Errors: Verify that the necessary libraries, such as `onnxruntime` and `transformers`, are installed in your environment.
• Tokenizing Issues: Ensure that your input text is properly formatted and encoded with the correct tokenizer.

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Further Learning and Resources

The training code for this adapter is available on GitHub. You can also explore various evaluation results in detail by checking the original paper.

Conclusion

By utilizing ONNX with AdapterHub’s RoBERTa model, you empower your project with enhanced portability and efficiency. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.