to_onnx.py

import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import onnxruntime as ort
import numpy as np

def convert_and_test_onnx(model_name, output_path="language_detection.onnx", test_text="This is a test sentence."):
    """
    Converts a Hugging Face model to ONNX, modifies the tokenizer, and tests the ONNX model.
    """
    try:
        # Load the tokenizer and model
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForSequenceClassification.from_pretrained(model_name)

        # Modify the tokenizer's normalizer
        if hasattr(tokenizer.backend_tokenizer.normalizer, "normalizations"):
            tokenizer.backend_tokenizer.normalizer.normalizations = []
        tokenizer.save_pretrained("./modified_tokenizer")

        # Export the model to ONNX
        dummy_input = tokenizer("This is a test sentence.", return_tensors="pt")

        torch.onnx.export(
            model,
            (dummy_input["input_ids"], dummy_input["attention_mask"]),
            output_path,
            input_names=["input_ids", "attention_mask"],
            output_names=["output"],
            dynamic_axes={
                "input_ids": {0: "batch", 1: "sequence"},
                "attention_mask": {0: "batch", 1: "sequence"},
                "output": {0: "batch"},
            },
            opset_version=14,
        )

        print(f"Model successfully converted and saved to {output_path}")

        # Test the ONNX model
        ort_session = ort.InferenceSession(output_path)
        tokenizer_test = AutoTokenizer.from_pretrained("./modified_tokenizer")

        # Explicitly set return_token_type_ids=False
        inputs = tokenizer_test(test_text, return_tensors="np", return_token_type_ids=False)

        ort_inputs = {k: v for k, v in inputs.items()}

        ort_outputs = ort_session.run(None, ort_inputs)
        logits = ort_outputs[0]
        predicted_class_id = np.argmax(logits, axis=-1)

        label_list = model.config.id2label
        predicted_label = label_list[predicted_class_id[0]]

        print(f"Test text: {test_text}")
        print(f"Predicted label: {predicted_label}")

    except Exception as e:
        print(f"Error during conversion or testing: {e}")

if __name__ == "__main__":
    model_name = "dewdev/language_detection"
    test_text = "मैंने राजा को हिंदी में एक पत्र लिखा।"
    convert_and_test_onnx(model_name, test_text=test_text)